diff --git a/Cargo.lock b/Cargo.lock
index 922ec606d..bcca35173 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -387,14 +387,14 @@ checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711"
 [[package]]
 name = "arroy"
 version = "0.4.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2ece9e5347e7fdaaea3181dec7f916677ad5f3fcbac183648ce1924eb4aeef9a"
+source = "git+https://github.com/meilisearch/arroy/?rev=2386594dfb009ce08821a925ccc89fb8e30bf73d#2386594dfb009ce08821a925ccc89fb8e30bf73d"
 dependencies = [
  "bytemuck",
  "byteorder",
  "heed",
  "log",
  "memmap2",
+ "nohash",
  "ordered-float",
  "rand",
  "rayon",
@@ -3686,6 +3686,12 @@ version = "0.0.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6d02c0b00610773bb7fc61d85e13d86c7858cbdf00e1a120bfc41bc055dbaa0e"
 
+[[package]]
+name = "nohash"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a0f889fb66f7acdf83442c35775764b51fed3c606ab9cee51500dbde2cf528ca"
+
 [[package]]
 name = "nom"
 version = "7.1.3"
diff --git a/dump/src/reader/mod.rs b/dump/src/reader/mod.rs
index 3b96cbfb0..4f66ed8b3 100644
--- a/dump/src/reader/mod.rs
+++ b/dump/src/reader/mod.rs
@@ -255,6 +255,8 @@ pub(crate) mod test {
         }
         "###);
 
+        insta::assert_json_snapshot!(vector_index.settings().unwrap());
+
         {
             let documents: Result<Vec<_>> = vector_index.documents().unwrap().collect();
             let mut documents = documents.unwrap();
diff --git a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap
index 43bdb9726..77694a629 100644
--- a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap
+++ b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-5.snap
@@ -1,783 +1,56 @@
 ---
 source: dump/src/reader/mod.rs
-expression: document
+expression: vector_index.settings().unwrap()
 ---
 {
-  "id": "e3",
-  "desc": "overriden vector + map",
-  "_vectors": {
-    "default": [
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-      0.1
-    ],
-    "toto": [
-      0.1
-    ]
-  }
+  "displayedAttributes": [
+    "*"
+  ],
+  "searchableAttributes": [
+    "*"
+  ],
+  "filterableAttributes": [],
+  "sortableAttributes": [],
+  "rankingRules": [
+    "words",
+    "typo",
+    "proximity",
+    "attribute",
+    "sort",
+    "exactness"
+  ],
+  "stopWords": [],
+  "nonSeparatorTokens": [],
+  "separatorTokens": [],
+  "dictionary": [],
+  "synonyms": {},
+  "distinctAttribute": null,
+  "proximityPrecision": "byWord",
+  "typoTolerance": {
+    "enabled": true,
+    "minWordSizeForTypos": {
+      "oneTypo": 5,
+      "twoTypos": 9
+    },
+    "disableOnWords": [],
+    "disableOnAttributes": []
+  },
+  "faceting": {
+    "maxValuesPerFacet": 100,
+    "sortFacetValuesBy": {
+      "*": "alpha"
+    }
+  },
+  "pagination": {
+    "maxTotalHits": 1000
+  },
+  "embedders": {
+    "default": {
+      "source": "huggingFace",
+      "model": "BAAI/bge-base-en-v1.5",
+      "revision": "617ca489d9e86b49b8167676d8220688b99db36e",
+      "documentTemplate": "{% for field in fields %} {{ field.name }}: {{ field.value }}\n{% endfor %}"
+    }
+  },
+  "searchCutoffMs": null
 }
diff --git a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap
index a9c76227a..43bdb9726 100644
--- a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap
+++ b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-6.snap
@@ -3,784 +3,781 @@ source: dump/src/reader/mod.rs
 expression: document
 ---
 {
-  "id": "e2",
-  "desc": "natural vector + map",
+  "id": "e3",
+  "desc": "overriden vector + map",
   "_vectors": {
-    "toto": [],
-    "default": {
-      "embeddings": [
-        [
-          -0.05189208313822746,
-          -0.9273212552070618,
-          0.1443813145160675,
-          0.0932632014155388,
-          0.2665371894836426,
-          0.36266782879829407,
-          0.6402910947799683,
-          0.32014018297195435,
-          0.030915971845388412,
-          -0.9312191605567932,
-          -0.3718109726905823,
-          -0.2700554132461548,
-          -1.1014580726623535,
-          0.9154956936836244,
-          -0.3406888246536255,
-          1.0077725648880005,
-          0.6577560901641846,
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diff --git a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-7.snap b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-7.snap
index e5d28e450..a9c76227a 100644
--- a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-7.snap
+++ b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-7.snap
@@ -3,780 +3,781 @@ source: dump/src/reader/mod.rs
 expression: document
 ---
 {
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diff --git a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-8.snap b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-8.snap
index 4bd0e2c3e..e5d28e450 100644
--- a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-8.snap
+++ b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-8.snap
@@ -3,778 +3,783 @@ source: dump/src/reader/mod.rs
 expression: document
 ---
 {
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-  "desc": "overriden vector",
+  "id": "e1",
+  "desc": "natural vector",
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   }
 }
diff --git a/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-9.snap b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-9.snap
new file mode 100644
index 000000000..4bd0e2c3e
--- /dev/null
+++ b/dump/src/reader/snapshots/dump__reader__test__import_dump_v6_with_vectors-9.snap
@@ -0,0 +1,780 @@
+---
+source: dump/src/reader/mod.rs
+expression: document
+---
+{
+  "id": "e0",
+  "desc": "overriden vector",
+  "_vectors": {
+    "default": [
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+  }
+}
diff --git a/index-scheduler/Cargo.toml b/index-scheduler/Cargo.toml
index 6f099a025..432a86382 100644
--- a/index-scheduler/Cargo.toml
+++ b/index-scheduler/Cargo.toml
@@ -40,7 +40,7 @@ ureq = "2.10.0"
 uuid = { version = "1.10.0", features = ["serde", "v4"] }
 
 [dev-dependencies]
-arroy = "0.4.0"
+arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
 big_s = "1.0.2"
 crossbeam = "0.8.4"
 insta = { version = "1.39.0", features = ["json", "redactions"] }
diff --git a/index-scheduler/src/lib.rs b/index-scheduler/src/lib.rs
index 753e8c179..fe8244f9b 100644
--- a/index-scheduler/src/lib.rs
+++ b/index-scheduler/src/lib.rs
@@ -1477,7 +1477,7 @@ impl IndexScheduler {
             .map(
                 |IndexEmbeddingConfig {
                      name,
-                     config: milli::vector::EmbeddingConfig { embedder_options, prompt },
+                     config: milli::vector::EmbeddingConfig { embedder_options, prompt, quantized },
                      ..
                  }| {
                     let prompt =
@@ -1486,7 +1486,10 @@ impl IndexScheduler {
                     {
                         let embedders = self.embedders.read().unwrap();
                         if let Some(embedder) = embedders.get(&embedder_options) {
-                            return Ok((name, (embedder.clone(), prompt)));
+                            return Ok((
+                                name,
+                                (embedder.clone(), prompt, quantized.unwrap_or_default()),
+                            ));
                         }
                     }
 
@@ -1500,7 +1503,7 @@ impl IndexScheduler {
                         let mut embedders = self.embedders.write().unwrap();
                         embedders.insert(embedder_options, embedder.clone());
                     }
-                    Ok((name, (embedder, prompt)))
+                    Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
                 },
             )
             .collect();
@@ -5197,7 +5200,7 @@ mod tests {
             let simple_hf_name = name.clone();
 
             let configs = index_scheduler.embedders(configs).unwrap();
-            let (hf_embedder, _) = configs.get(&simple_hf_name).unwrap();
+            let (hf_embedder, _, _) = configs.get(&simple_hf_name).unwrap();
             let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap();
             let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo")).unwrap();
             let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo")).unwrap();
@@ -5519,6 +5522,7 @@ mod tests {
                             400,
                         ),
                     },
+                    quantized: None,
                 },
                 user_provided: RoaringBitmap<[1, 2]>,
             },
@@ -5531,28 +5535,8 @@ mod tests {
 
         // the document with the id 3 should keep its original embedding
         let docid = index.external_documents_ids.get(&rtxn, "3").unwrap().unwrap();
-        let mut embeddings = Vec::new();
-
-        'vectors: for i in 0..=u8::MAX {
-            let reader = arroy::Reader::open(&rtxn, i as u16, index.vector_arroy)
-                .map(Some)
-                .or_else(|e| match e {
-                    arroy::Error::MissingMetadata(_) => Ok(None),
-                    e => Err(e),
-                })
-                .transpose();
-
-            let Some(reader) = reader else {
-                break 'vectors;
-            };
-
-            let embedding = reader.unwrap().item_vector(&rtxn, docid).unwrap();
-            if let Some(embedding) = embedding {
-                embeddings.push(embedding)
-            } else {
-                break 'vectors;
-            }
-        }
+        let embeddings = index.embeddings(&rtxn, docid).unwrap();
+        let embeddings = &embeddings["my_doggo_embedder"];
 
         snapshot!(embeddings.len(), @"1");
         assert!(embeddings[0].iter().all(|i| *i == 3.0), "{:?}", embeddings[0]);
@@ -5737,6 +5721,7 @@ mod tests {
                             400,
                         ),
                     },
+                    quantized: None,
                 },
                 user_provided: RoaringBitmap<[0]>,
             },
@@ -5780,6 +5765,7 @@ mod tests {
                             400,
                         ),
                     },
+                    quantized: None,
                 },
                 user_provided: RoaringBitmap<[]>,
             },
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap
index 5eccdc57a..41cfcfdab 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap	
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap	
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 2 {uid: 2, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap
index e7c7382d5..e6d0d8232 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap	
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap	
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 2 {uid: 2, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap
index ac3b3f2d9..bd4cf0c09 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap	
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap	
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap
index e67ef0e51..746c7c870 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap	
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap	
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap
index 84d8486e1..15cfd732a 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued [0,]
diff --git a/index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap b/index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap
index 6ef17024d..9b5c6ce4c 100644
--- a/index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap
+++ b/index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, binary_quantized: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), document_template_max_bytes: NotSet, url: NotSet, request: NotSet, response: NotSet, headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued []
diff --git a/index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap b/index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap
index cf710b40f..37f0a062d 100644
--- a/index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap
+++ b/index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued [0,]
diff --git a/index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap b/index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap
index 9b5b465ab..3906fc6fc 100644
--- a/index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap
+++ b/index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap
@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), binary_quantized: NotSet, document_template: NotSet, document_template_max_bytes: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), headers: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, localized_attributes: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued []
diff --git a/meilisearch-types/src/error.rs b/meilisearch-types/src/error.rs
index 535bf2dd6..f755998a1 100644
--- a/meilisearch-types/src/error.rs
+++ b/meilisearch-types/src/error.rs
@@ -395,7 +395,10 @@ impl ErrorCode for milli::Error {
                     | UserError::InvalidSettingsDimensions { .. }
                     | UserError::InvalidUrl { .. }
                     | UserError::InvalidSettingsDocumentTemplateMaxBytes { .. }
-                    | UserError::InvalidPrompt(_) => Code::InvalidSettingsEmbedders,
+                    | UserError::InvalidPrompt(_)
+                    | UserError::InvalidDisableBinaryQuantization { .. } => {
+                        Code::InvalidSettingsEmbedders
+                    }
                     UserError::TooManyEmbedders(_) => Code::InvalidSettingsEmbedders,
                     UserError::InvalidPromptForEmbeddings(..) => Code::InvalidSettingsEmbedders,
                     UserError::NoPrimaryKeyCandidateFound => Code::IndexPrimaryKeyNoCandidateFound,
diff --git a/meilisearch/src/routes/indexes/settings.rs b/meilisearch/src/routes/indexes/settings.rs
index ceea17668..aaf8673d0 100644
--- a/meilisearch/src/routes/indexes/settings.rs
+++ b/meilisearch/src/routes/indexes/settings.rs
@@ -643,12 +643,19 @@ fn embedder_analytics(
             .max()
     });
 
+    let binary_quantization_used = setting.as_ref().map(|map| {
+        map.values()
+            .filter_map(|config| config.clone().set())
+            .any(|config| config.binary_quantized.set().is_some())
+    });
+
     json!(
         {
             "total": setting.as_ref().map(|s| s.len()),
             "sources": sources,
             "document_template_used": document_template_used,
-            "document_template_max_bytes": document_template_max_bytes
+            "document_template_max_bytes": document_template_max_bytes,
+            "binary_quantization_used": binary_quantization_used,
         }
     )
 }
diff --git a/meilisearch/src/routes/indexes/similar.rs b/meilisearch/src/routes/indexes/similar.rs
index dd30c793e..f94a02987 100644
--- a/meilisearch/src/routes/indexes/similar.rs
+++ b/meilisearch/src/routes/indexes/similar.rs
@@ -102,7 +102,7 @@ async fn similar(
 
     let index = index_scheduler.index(&index_uid)?;
 
-    let (embedder_name, embedder) =
+    let (embedder_name, embedder, quantized) =
         SearchKind::embedder(&index_scheduler, &index, &query.embedder, None)?;
 
     tokio::task::spawn_blocking(move || {
@@ -111,6 +111,7 @@ async fn similar(
             query,
             embedder_name,
             embedder,
+            quantized,
             retrieve_vectors,
             index_scheduler.features(),
         )
diff --git a/meilisearch/src/search/mod.rs b/meilisearch/src/search/mod.rs
index 9abfec3e3..66b6e56de 100644
--- a/meilisearch/src/search/mod.rs
+++ b/meilisearch/src/search/mod.rs
@@ -274,8 +274,8 @@ pub struct HybridQuery {
 #[derive(Clone)]
 pub enum SearchKind {
     KeywordOnly,
-    SemanticOnly { embedder_name: String, embedder: Arc<Embedder> },
-    Hybrid { embedder_name: String, embedder: Arc<Embedder>, semantic_ratio: f32 },
+    SemanticOnly { embedder_name: String, embedder: Arc<Embedder>, quantized: bool },
+    Hybrid { embedder_name: String, embedder: Arc<Embedder>, quantized: bool, semantic_ratio: f32 },
 }
 
 impl SearchKind {
@@ -285,9 +285,9 @@ impl SearchKind {
         embedder_name: &str,
         vector_len: Option<usize>,
     ) -> Result<Self, ResponseError> {
-        let (embedder_name, embedder) =
+        let (embedder_name, embedder, quantized) =
             Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
-        Ok(Self::SemanticOnly { embedder_name, embedder })
+        Ok(Self::SemanticOnly { embedder_name, embedder, quantized })
     }
 
     pub(crate) fn hybrid(
@@ -297,9 +297,9 @@ impl SearchKind {
         semantic_ratio: f32,
         vector_len: Option<usize>,
     ) -> Result<Self, ResponseError> {
-        let (embedder_name, embedder) =
+        let (embedder_name, embedder, quantized) =
             Self::embedder(index_scheduler, index, embedder_name, vector_len)?;
-        Ok(Self::Hybrid { embedder_name, embedder, semantic_ratio })
+        Ok(Self::Hybrid { embedder_name, embedder, quantized, semantic_ratio })
     }
 
     pub(crate) fn embedder(
@@ -307,16 +307,14 @@ impl SearchKind {
         index: &Index,
         embedder_name: &str,
         vector_len: Option<usize>,
-    ) -> Result<(String, Arc<Embedder>), ResponseError> {
+    ) -> Result<(String, Arc<Embedder>, bool), ResponseError> {
         let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
         let embedders = index_scheduler.embedders(embedder_configs)?;
 
-        let embedder = embedders.get(embedder_name);
-
-        let embedder = embedder
+        let (embedder, _, quantized) = embedders
+            .get(embedder_name)
             .ok_or(milli::UserError::InvalidEmbedder(embedder_name.to_owned()))
-            .map_err(milli::Error::from)?
-            .0;
+            .map_err(milli::Error::from)?;
 
         if let Some(vector_len) = vector_len {
             if vector_len != embedder.dimensions() {
@@ -330,7 +328,7 @@ impl SearchKind {
             }
         }
 
-        Ok((embedder_name.to_owned(), embedder))
+        Ok((embedder_name.to_owned(), embedder, quantized))
     }
 }
 
@@ -791,7 +789,7 @@ fn prepare_search<'t>(
                 search.query(q);
             }
         }
-        SearchKind::SemanticOnly { embedder_name, embedder } => {
+        SearchKind::SemanticOnly { embedder_name, embedder, quantized } => {
             let vector = match query.vector.clone() {
                 Some(vector) => vector,
                 None => {
@@ -805,14 +803,19 @@ fn prepare_search<'t>(
                 }
             };
 
-            search.semantic(embedder_name.clone(), embedder.clone(), Some(vector));
+            search.semantic(embedder_name.clone(), embedder.clone(), *quantized, Some(vector));
         }
-        SearchKind::Hybrid { embedder_name, embedder, semantic_ratio: _ } => {
+        SearchKind::Hybrid { embedder_name, embedder, quantized, semantic_ratio: _ } => {
             if let Some(q) = &query.q {
                 search.query(q);
             }
             // will be embedded in hybrid search if necessary
-            search.semantic(embedder_name.clone(), embedder.clone(), query.vector.clone());
+            search.semantic(
+                embedder_name.clone(),
+                embedder.clone(),
+                *quantized,
+                query.vector.clone(),
+            );
         }
     }
 
@@ -1441,6 +1444,7 @@ pub fn perform_similar(
     query: SimilarQuery,
     embedder_name: String,
     embedder: Arc<Embedder>,
+    quantized: bool,
     retrieve_vectors: RetrieveVectors,
     features: RoFeatures,
 ) -> Result<SimilarResult, ResponseError> {
@@ -1469,8 +1473,16 @@ pub fn perform_similar(
         ));
     };
 
-    let mut similar =
-        milli::Similar::new(internal_id, offset, limit, index, &rtxn, embedder_name, embedder);
+    let mut similar = milli::Similar::new(
+        internal_id,
+        offset,
+        limit,
+        index,
+        &rtxn,
+        embedder_name,
+        embedder,
+        quantized,
+    );
 
     if let Some(ref filter) = query.filter {
         if let Some(facets) = parse_filter(filter, Code::InvalidSimilarFilter, features)? {
diff --git a/meilisearch/tests/vector/binary_quantized.rs b/meilisearch/tests/vector/binary_quantized.rs
new file mode 100644
index 000000000..d3fe3c824
--- /dev/null
+++ b/meilisearch/tests/vector/binary_quantized.rs
@@ -0,0 +1,380 @@
+use meili_snap::{json_string, snapshot};
+
+use crate::common::{GetAllDocumentsOptions, Server};
+use crate::json;
+use crate::vector::generate_default_user_provided_documents;
+
+#[actix_rt::test]
+async fn retrieve_binary_quantize_status_in_the_settings() {
+    let server = Server::new().await;
+    let index = server.index("doggo");
+    let (value, code) = server.set_features(json!({"vectorStore": true})).await;
+    snapshot!(code, @"200 OK");
+    snapshot!(value, @r###"
+    {
+      "vectorStore": true,
+      "metrics": false,
+      "logsRoute": false,
+      "editDocumentsByFunction": false,
+      "containsFilter": false
+    }
+    "###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let (settings, code) = index.settings().await;
+    snapshot!(code, @"200 OK");
+    snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3}"###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": false,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let (settings, code) = index.settings().await;
+    snapshot!(code, @"200 OK");
+    snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":false}"###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": true,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let (settings, code) = index.settings().await;
+    snapshot!(code, @"200 OK");
+    snapshot!(settings["embedders"]["manual"], @r###"{"source":"userProvided","dimensions":3,"binaryQuantized":true}"###);
+}
+
+#[actix_rt::test]
+async fn binary_quantize_before_sending_documents() {
+    let server = Server::new().await;
+    let index = server.index("doggo");
+    let (value, code) = server.set_features(json!({"vectorStore": true})).await;
+    snapshot!(code, @"200 OK");
+    snapshot!(value, @r###"
+    {
+      "vectorStore": true,
+      "metrics": false,
+      "logsRoute": false,
+      "editDocumentsByFunction": false,
+      "containsFilter": false
+    }
+    "###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": true,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let documents = json!([
+      {"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
+      {"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
+    ]);
+    let (value, code) = index.add_documents(documents, None).await;
+    snapshot!(code, @"202 Accepted");
+    index.wait_task(value.uid()).await.succeeded();
+
+    // Make sure the documents are binary quantized
+    let (documents, _code) = index
+        .get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
+        .await;
+    snapshot!(json_string!(documents), @r###"
+    {
+      "results": [
+        {
+          "id": 0,
+          "name": "kefir",
+          "_vectors": {
+            "manual": {
+              "embeddings": [
+                [
+                  -1.0,
+                  -1.0,
+                  1.0
+                ]
+              ],
+              "regenerate": false
+            }
+          }
+        },
+        {
+          "id": 1,
+          "name": "echo",
+          "_vectors": {
+            "manual": {
+              "embeddings": [
+                [
+                  1.0,
+                  1.0,
+                  -1.0
+                ]
+              ],
+              "regenerate": false
+            }
+          }
+        }
+      ],
+      "offset": 0,
+      "limit": 20,
+      "total": 2
+    }
+    "###);
+}
+
+#[actix_rt::test]
+async fn binary_quantize_after_sending_documents() {
+    let server = Server::new().await;
+    let index = server.index("doggo");
+    let (value, code) = server.set_features(json!({"vectorStore": true})).await;
+    snapshot!(code, @"200 OK");
+    snapshot!(value, @r###"
+    {
+      "vectorStore": true,
+      "metrics": false,
+      "logsRoute": false,
+      "editDocumentsByFunction": false,
+      "containsFilter": false
+    }
+    "###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let documents = json!([
+      {"id": 0, "name": "kefir", "_vectors": { "manual": [-1.2, -2.3, 3.2] }},
+      {"id": 1, "name": "echo", "_vectors": { "manual": [2.5, 1.5, -130] }},
+    ]);
+    let (value, code) = index.add_documents(documents, None).await;
+    snapshot!(code, @"202 Accepted");
+    index.wait_task(value.uid()).await.succeeded();
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": true,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    // Make sure the documents are binary quantized
+    let (documents, _code) = index
+        .get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
+        .await;
+    snapshot!(json_string!(documents), @r###"
+    {
+      "results": [
+        {
+          "id": 0,
+          "name": "kefir",
+          "_vectors": {
+            "manual": {
+              "embeddings": [
+                [
+                  -1.0,
+                  -1.0,
+                  1.0
+                ]
+              ],
+              "regenerate": false
+            }
+          }
+        },
+        {
+          "id": 1,
+          "name": "echo",
+          "_vectors": {
+            "manual": {
+              "embeddings": [
+                [
+                  1.0,
+                  1.0,
+                  -1.0
+                ]
+              ],
+              "regenerate": false
+            }
+          }
+        }
+      ],
+      "offset": 0,
+      "limit": 20,
+      "total": 2
+    }
+    "###);
+}
+
+#[actix_rt::test]
+async fn try_to_disable_binary_quantization() {
+    let server = Server::new().await;
+    let index = server.index("doggo");
+    let (value, code) = server.set_features(json!({"vectorStore": true})).await;
+    snapshot!(code, @"200 OK");
+    snapshot!(value, @r###"
+    {
+      "vectorStore": true,
+      "metrics": false,
+      "logsRoute": false,
+      "editDocumentsByFunction": false,
+      "containsFilter": false
+    }
+    "###);
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": true,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "source": "userProvided",
+                  "dimensions": 3,
+                  "binaryQuantized": false,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    let ret = server.wait_task(response.uid()).await;
+    snapshot!(ret, @r###"
+    {
+      "uid": "[uid]",
+      "indexUid": "doggo",
+      "status": "failed",
+      "type": "settingsUpdate",
+      "canceledBy": null,
+      "details": {
+        "embedders": {
+          "manual": {
+            "source": "userProvided",
+            "dimensions": 3,
+            "binaryQuantized": false
+          }
+        }
+      },
+      "error": {
+        "message": "`.embedders.manual.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors.",
+        "code": "invalid_settings_embedders",
+        "type": "invalid_request",
+        "link": "https://docs.meilisearch.com/errors#invalid_settings_embedders"
+      },
+      "duration": "[duration]",
+      "enqueuedAt": "[date]",
+      "startedAt": "[date]",
+      "finishedAt": "[date]"
+    }
+    "###);
+}
+
+#[actix_rt::test]
+async fn binary_quantize_clear_documents() {
+    let server = Server::new().await;
+    let index = generate_default_user_provided_documents(&server).await;
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "manual": {
+                  "binaryQuantized": true,
+              }
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    server.wait_task(response.uid()).await.succeeded();
+
+    let (value, _code) = index.clear_all_documents().await;
+    index.wait_task(value.uid()).await.succeeded();
+
+    // Make sure the documents DB has been cleared
+    let (documents, _code) = index
+        .get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
+        .await;
+    snapshot!(json_string!(documents), @r###"
+    {
+      "results": [],
+      "offset": 0,
+      "limit": 20,
+      "total": 0
+    }
+    "###);
+
+    // Make sure the arroy DB has been cleared
+    let (documents, _code) =
+        index.search_post(json!({ "hybrid": { "embedder": "manual" }, "vector": [1, 1, 1] })).await;
+    snapshot!(documents, @r###"
+    {
+      "hits": [],
+      "query": "",
+      "processingTimeMs": "[duration]",
+      "limit": 20,
+      "offset": 0,
+      "estimatedTotalHits": 0,
+      "semanticHitCount": 0
+    }
+    "###);
+}
diff --git a/meilisearch/tests/vector/mod.rs b/meilisearch/tests/vector/mod.rs
index 0e38c1366..47d0c1051 100644
--- a/meilisearch/tests/vector/mod.rs
+++ b/meilisearch/tests/vector/mod.rs
@@ -1,3 +1,4 @@
+mod binary_quantized;
 mod openai;
 mod rest;
 mod settings;
diff --git a/milli/Cargo.toml b/milli/Cargo.toml
index 8c77f338c..5fc2d65c8 100644
--- a/milli/Cargo.toml
+++ b/milli/Cargo.toml
@@ -80,7 +80,7 @@ hf-hub = { git = "https://github.com/dureuill/hf-hub.git", branch = "rust_tls",
 tiktoken-rs = "0.5.9"
 liquid = "0.26.6"
 rhai = { version = "1.19.0", features = ["serde", "no_module", "no_custom_syntax", "no_time", "sync"] }
-arroy = "0.4.0"
+arroy = { git = "https://github.com/meilisearch/arroy/", rev = "2386594dfb009ce08821a925ccc89fb8e30bf73d" }
 rand = "0.8.5"
 tracing = "0.1.40"
 ureq = { version = "2.10.0", features = ["json"] }
diff --git a/milli/src/error.rs b/milli/src/error.rs
index f0e92a9ab..400d3d3be 100644
--- a/milli/src/error.rs
+++ b/milli/src/error.rs
@@ -258,6 +258,10 @@ only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and undersco
     },
     #[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")]
     InvalidSettingsDimensions { embedder_name: String },
+    #[error(
+        "`.embedders.{embedder_name}.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors."
+    )]
+    InvalidDisableBinaryQuantization { embedder_name: String },
     #[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")]
     InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String },
     #[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")]
diff --git a/milli/src/index.rs b/milli/src/index.rs
index 512e911aa..c47896df7 100644
--- a/milli/src/index.rs
+++ b/milli/src/index.rs
@@ -21,7 +21,7 @@ use crate::heed_codec::{BEU16StrCodec, FstSetCodec, StrBEU16Codec, StrRefCodec};
 use crate::order_by_map::OrderByMap;
 use crate::proximity::ProximityPrecision;
 use crate::vector::parsed_vectors::RESERVED_VECTORS_FIELD_NAME;
-use crate::vector::{Embedding, EmbeddingConfig};
+use crate::vector::{ArroyWrapper, Embedding, EmbeddingConfig};
 use crate::{
     default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
     FacetDistribution, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldIdWordCountCodec,
@@ -162,7 +162,7 @@ pub struct Index {
     /// Maps an embedder name to its id in the arroy store.
     pub embedder_category_id: Database<Str, U8>,
     /// Vector store based on arroy™.
-    pub vector_arroy: arroy::Database<arroy::distances::Angular>,
+    pub vector_arroy: arroy::Database<Unspecified>,
 
     /// Maps the document id to the document as an obkv store.
     pub(crate) documents: Database<BEU32, ObkvCodec>,
@@ -1614,15 +1614,17 @@ impl Index {
         &'a self,
         rtxn: &'a RoTxn<'a>,
         embedder_id: u8,
-    ) -> impl Iterator<Item = Result<arroy::Reader<'a, arroy::distances::Angular>>> + 'a {
+        quantized: bool,
+    ) -> impl Iterator<Item = Result<ArroyWrapper>> + 'a {
         crate::vector::arroy_db_range_for_embedder(embedder_id).map_while(move |k| {
-            arroy::Reader::open(rtxn, k, self.vector_arroy)
-                .map(Some)
-                .or_else(|e| match e {
-                    arroy::Error::MissingMetadata(_) => Ok(None),
-                    e => Err(e.into()),
-                })
-                .transpose()
+            let reader = ArroyWrapper::new(self.vector_arroy, k, quantized);
+            // Here we don't care about the dimensions, but we want to know if we can read
+            // in the database or if its metadata are missing because there is no document with that many vectors.
+            match reader.dimensions(rtxn) {
+                Ok(_) => Some(Ok(reader)),
+                Err(arroy::Error::MissingMetadata(_)) => None,
+                Err(e) => Some(Err(e.into())),
+            }
         })
     }
 
@@ -1644,32 +1646,18 @@ impl Index {
         docid: DocumentId,
     ) -> Result<BTreeMap<String, Vec<Embedding>>> {
         let mut res = BTreeMap::new();
-        for row in self.embedder_category_id.iter(rtxn)? {
-            let (embedder_name, embedder_id) = row?;
-            let embedder_id = (embedder_id as u16) << 8;
-            let mut embeddings = Vec::new();
-            'vectors: for i in 0..=u8::MAX {
-                let reader = arroy::Reader::open(rtxn, embedder_id | (i as u16), self.vector_arroy)
-                    .map(Some)
-                    .or_else(|e| match e {
-                        arroy::Error::MissingMetadata(_) => Ok(None),
-                        e => Err(e),
-                    })
-                    .transpose();
-
-                let Some(reader) = reader else {
-                    break 'vectors;
-                };
-
-                let embedding = reader?.item_vector(rtxn, docid)?;
-                if let Some(embedding) = embedding {
-                    embeddings.push(embedding)
-                } else {
-                    break 'vectors;
-                }
-            }
-
-            res.insert(embedder_name.to_owned(), embeddings);
+        let embedding_configs = self.embedding_configs(rtxn)?;
+        for config in embedding_configs {
+            let embedder_id = self.embedder_category_id.get(rtxn, &config.name)?.unwrap();
+            let embeddings = self
+                .arroy_readers(rtxn, embedder_id, config.config.quantized())
+                .map_while(|reader| {
+                    reader
+                        .and_then(|r| r.item_vector(rtxn, docid).map_err(|e| e.into()))
+                        .transpose()
+                })
+                .collect::<Result<Vec<_>>>()?;
+            res.insert(config.name.to_owned(), embeddings);
         }
         Ok(res)
     }
diff --git a/milli/src/search/hybrid.rs b/milli/src/search/hybrid.rs
index e08111473..8b274804c 100644
--- a/milli/src/search/hybrid.rs
+++ b/milli/src/search/hybrid.rs
@@ -190,7 +190,7 @@ impl<'a> Search<'a> {
             return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
         };
         // no embedder, no semantic search
-        let Some(SemanticSearch { vector, embedder_name, embedder }) = semantic else {
+        let Some(SemanticSearch { vector, embedder_name, embedder, quantized }) = semantic else {
             return Ok(return_keyword_results(self.limit, self.offset, keyword_results));
         };
 
@@ -212,7 +212,7 @@ impl<'a> Search<'a> {
         };
 
         search.semantic =
-            Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder });
+            Some(SemanticSearch { vector: Some(vector_query), embedder_name, embedder, quantized });
 
         // TODO: would be better to have two distinct functions at this point
         let vector_results = search.execute()?;
diff --git a/milli/src/search/mod.rs b/milli/src/search/mod.rs
index 3057066d2..d5b05f515 100644
--- a/milli/src/search/mod.rs
+++ b/milli/src/search/mod.rs
@@ -32,6 +32,7 @@ pub struct SemanticSearch {
     vector: Option<Vec<f32>>,
     embedder_name: String,
     embedder: Arc<Embedder>,
+    quantized: bool,
 }
 
 pub struct Search<'a> {
@@ -89,9 +90,10 @@ impl<'a> Search<'a> {
         &mut self,
         embedder_name: String,
         embedder: Arc<Embedder>,
+        quantized: bool,
         vector: Option<Vec<f32>>,
     ) -> &mut Search<'a> {
-        self.semantic = Some(SemanticSearch { embedder_name, embedder, vector });
+        self.semantic = Some(SemanticSearch { embedder_name, embedder, quantized, vector });
         self
     }
 
@@ -206,7 +208,7 @@ impl<'a> Search<'a> {
             degraded,
             used_negative_operator,
         } = match self.semantic.as_ref() {
-            Some(SemanticSearch { vector: Some(vector), embedder_name, embedder }) => {
+            Some(SemanticSearch { vector: Some(vector), embedder_name, embedder, quantized }) => {
                 execute_vector_search(
                     &mut ctx,
                     vector,
@@ -219,6 +221,7 @@ impl<'a> Search<'a> {
                     self.limit,
                     embedder_name,
                     embedder,
+                    *quantized,
                     self.time_budget.clone(),
                     self.ranking_score_threshold,
                 )?
diff --git a/milli/src/search/new/mod.rs b/milli/src/search/new/mod.rs
index b30306a0b..f7c590360 100644
--- a/milli/src/search/new/mod.rs
+++ b/milli/src/search/new/mod.rs
@@ -312,6 +312,7 @@ fn get_ranking_rules_for_placeholder_search<'ctx>(
     Ok(ranking_rules)
 }
 
+#[allow(clippy::too_many_arguments)]
 fn get_ranking_rules_for_vector<'ctx>(
     ctx: &SearchContext<'ctx>,
     sort_criteria: &Option<Vec<AscDesc>>,
@@ -320,6 +321,7 @@ fn get_ranking_rules_for_vector<'ctx>(
     target: &[f32],
     embedder_name: &str,
     embedder: &Embedder,
+    quantized: bool,
 ) -> Result<Vec<BoxRankingRule<'ctx, PlaceholderQuery>>> {
     // query graph search
 
@@ -347,6 +349,7 @@ fn get_ranking_rules_for_vector<'ctx>(
                         limit_plus_offset,
                         embedder_name,
                         embedder,
+                        quantized,
                     )?;
                     ranking_rules.push(Box::new(vector_sort));
                     vector = true;
@@ -576,6 +579,7 @@ pub fn execute_vector_search(
     length: usize,
     embedder_name: &str,
     embedder: &Embedder,
+    quantized: bool,
     time_budget: TimeBudget,
     ranking_score_threshold: Option<f64>,
 ) -> Result<PartialSearchResult> {
@@ -591,6 +595,7 @@ pub fn execute_vector_search(
         vector,
         embedder_name,
         embedder,
+        quantized,
     )?;
 
     let mut placeholder_search_logger = logger::DefaultSearchLogger;
diff --git a/milli/src/search/new/vector_sort.rs b/milli/src/search/new/vector_sort.rs
index e56f3cbbe..de1dacbe7 100644
--- a/milli/src/search/new/vector_sort.rs
+++ b/milli/src/search/new/vector_sort.rs
@@ -16,6 +16,7 @@ pub struct VectorSort<Q: RankingRuleQueryTrait> {
     limit: usize,
     distribution_shift: Option<DistributionShift>,
     embedder_index: u8,
+    quantized: bool,
 }
 
 impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
@@ -26,6 +27,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
         limit: usize,
         embedder_name: &str,
         embedder: &Embedder,
+        quantized: bool,
     ) -> Result<Self> {
         let embedder_index = ctx
             .index
@@ -41,6 +43,7 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
             limit,
             distribution_shift: embedder.distribution(),
             embedder_index,
+            quantized,
         })
     }
 
@@ -49,16 +52,12 @@ impl<Q: RankingRuleQueryTrait> VectorSort<Q> {
         ctx: &mut SearchContext<'_>,
         vector_candidates: &RoaringBitmap,
     ) -> Result<()> {
-        let readers: std::result::Result<Vec<_>, _> =
-            ctx.index.arroy_readers(ctx.txn, self.embedder_index).collect();
-        let readers = readers?;
-
         let target = &self.target;
         let mut results = Vec::new();
 
-        for reader in readers.iter() {
+        for reader in ctx.index.arroy_readers(ctx.txn, self.embedder_index, self.quantized) {
             let nns_by_vector =
-                reader.nns_by_vector(ctx.txn, target, self.limit, None, Some(vector_candidates))?;
+                reader?.nns_by_vector(ctx.txn, target, self.limit, Some(vector_candidates))?;
             results.extend(nns_by_vector.into_iter());
         }
         results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
diff --git a/milli/src/search/similar.rs b/milli/src/search/similar.rs
index bf5cc323f..0cb8d723d 100644
--- a/milli/src/search/similar.rs
+++ b/milli/src/search/similar.rs
@@ -18,9 +18,11 @@ pub struct Similar<'a> {
     embedder_name: String,
     embedder: Arc<Embedder>,
     ranking_score_threshold: Option<f64>,
+    quantized: bool,
 }
 
 impl<'a> Similar<'a> {
+    #[allow(clippy::too_many_arguments)]
     pub fn new(
         id: DocumentId,
         offset: usize,
@@ -29,6 +31,7 @@ impl<'a> Similar<'a> {
         rtxn: &'a heed::RoTxn<'a>,
         embedder_name: String,
         embedder: Arc<Embedder>,
+        quantized: bool,
     ) -> Self {
         Self {
             id,
@@ -40,6 +43,7 @@ impl<'a> Similar<'a> {
             embedder_name,
             embedder,
             ranking_score_threshold: None,
+            quantized,
         }
     }
 
@@ -67,19 +71,13 @@ impl<'a> Similar<'a> {
                 .get(self.rtxn, &self.embedder_name)?
                 .ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
 
-        let readers: std::result::Result<Vec<_>, _> =
-            self.index.arroy_readers(self.rtxn, embedder_index).collect();
-
-        let readers = readers?;
-
         let mut results = Vec::new();
 
-        for reader in readers.iter() {
-            let nns_by_item = reader.nns_by_item(
+        for reader in self.index.arroy_readers(self.rtxn, embedder_index, self.quantized) {
+            let nns_by_item = reader?.nns_by_item(
                 self.rtxn,
                 self.id,
                 self.limit + self.offset + 1,
-                None,
                 Some(&universe),
             )?;
             if let Some(mut nns_by_item) = nns_by_item {
diff --git a/milli/src/update/index_documents/extract/extract_vector_points.rs b/milli/src/update/index_documents/extract/extract_vector_points.rs
index e9b83b92c..38a4ebe8a 100644
--- a/milli/src/update/index_documents/extract/extract_vector_points.rs
+++ b/milli/src/update/index_documents/extract/extract_vector_points.rs
@@ -20,7 +20,7 @@ use crate::update::del_add::{DelAdd, KvReaderDelAdd, KvWriterDelAdd};
 use crate::update::settings::InnerIndexSettingsDiff;
 use crate::vector::error::{EmbedErrorKind, PossibleEmbeddingMistakes, UnusedVectorsDistribution};
 use crate::vector::parsed_vectors::{ParsedVectorsDiff, VectorState, RESERVED_VECTORS_FIELD_NAME};
-use crate::vector::settings::{EmbedderAction, ReindexAction};
+use crate::vector::settings::ReindexAction;
 use crate::vector::{Embedder, Embeddings};
 use crate::{try_split_array_at, DocumentId, FieldId, Result, ThreadPoolNoAbort};
 
@@ -208,65 +208,65 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
 
     if reindex_vectors {
         for (name, action) in settings_diff.embedding_config_updates.iter() {
-            match action {
-                EmbedderAction::WriteBackToDocuments(_) => continue, // already deleted
-                EmbedderAction::Reindex(action) => {
-                    let Some((embedder_name, (embedder, prompt))) = configs.remove_entry(name)
-                    else {
-                        tracing::error!(embedder = name, "Requested embedder config not found");
-                        continue;
-                    };
+            if let Some(action) = action.reindex() {
+                let Some((embedder_name, (embedder, prompt, _quantized))) =
+                    configs.remove_entry(name)
+                else {
+                    tracing::error!(embedder = name, "Requested embedder config not found");
+                    continue;
+                };
 
-                    // (docid, _index) -> KvWriterDelAdd -> Vector
-                    let manual_vectors_writer = create_writer(
-                        indexer.chunk_compression_type,
-                        indexer.chunk_compression_level,
-                        tempfile::tempfile()?,
-                    );
+                // (docid, _index) -> KvWriterDelAdd -> Vector
+                let manual_vectors_writer = create_writer(
+                    indexer.chunk_compression_type,
+                    indexer.chunk_compression_level,
+                    tempfile::tempfile()?,
+                );
 
-                    // (docid) -> (prompt)
-                    let prompts_writer = create_writer(
-                        indexer.chunk_compression_type,
-                        indexer.chunk_compression_level,
-                        tempfile::tempfile()?,
-                    );
+                // (docid) -> (prompt)
+                let prompts_writer = create_writer(
+                    indexer.chunk_compression_type,
+                    indexer.chunk_compression_level,
+                    tempfile::tempfile()?,
+                );
 
-                    // (docid) -> ()
-                    let remove_vectors_writer = create_writer(
-                        indexer.chunk_compression_type,
-                        indexer.chunk_compression_level,
-                        tempfile::tempfile()?,
-                    );
+                // (docid) -> ()
+                let remove_vectors_writer = create_writer(
+                    indexer.chunk_compression_type,
+                    indexer.chunk_compression_level,
+                    tempfile::tempfile()?,
+                );
 
-                    let action = match action {
-                        ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
-                        ReindexAction::RegeneratePrompts => {
-                            let Some((_, old_prompt)) = old_configs.get(name) else {
-                                tracing::error!(embedder = name, "Old embedder config not found");
-                                continue;
-                            };
+                let action = match action {
+                    ReindexAction::FullReindex => ExtractionAction::SettingsFullReindex,
+                    ReindexAction::RegeneratePrompts => {
+                        let Some((_, old_prompt, _quantized)) = old_configs.get(name) else {
+                            tracing::error!(embedder = name, "Old embedder config not found");
+                            continue;
+                        };
 
-                            ExtractionAction::SettingsRegeneratePrompts { old_prompt }
-                        }
-                    };
+                        ExtractionAction::SettingsRegeneratePrompts { old_prompt }
+                    }
+                };
 
-                    extractors.push(EmbedderVectorExtractor {
-                        embedder_name,
-                        embedder,
-                        prompt,
-                        prompts_writer,
-                        remove_vectors_writer,
-                        manual_vectors_writer,
-                        add_to_user_provided: RoaringBitmap::new(),
-                        action,
-                    });
-                }
+                extractors.push(EmbedderVectorExtractor {
+                    embedder_name,
+                    embedder,
+                    prompt,
+                    prompts_writer,
+                    remove_vectors_writer,
+                    manual_vectors_writer,
+                    add_to_user_provided: RoaringBitmap::new(),
+                    action,
+                });
+            } else {
+                continue;
             }
         }
     } else {
         // document operation
 
-        for (embedder_name, (embedder, prompt)) in configs.into_iter() {
+        for (embedder_name, (embedder, prompt, _quantized)) in configs.into_iter() {
             // (docid, _index) -> KvWriterDelAdd -> Vector
             let manual_vectors_writer = create_writer(
                 indexer.chunk_compression_type,
diff --git a/milli/src/update/index_documents/mod.rs b/milli/src/update/index_documents/mod.rs
index 6d659a7a2..326dd842d 100644
--- a/milli/src/update/index_documents/mod.rs
+++ b/milli/src/update/index_documents/mod.rs
@@ -43,7 +43,7 @@ use crate::update::index_documents::parallel::ImmutableObkvs;
 use crate::update::{
     IndexerConfig, UpdateIndexingStep, WordPrefixDocids, WordPrefixIntegerDocids, WordsPrefixesFst,
 };
-use crate::vector::EmbeddingConfigs;
+use crate::vector::{ArroyWrapper, EmbeddingConfigs};
 use crate::{CboRoaringBitmapCodec, Index, Object, Result};
 
 static MERGED_DATABASE_COUNT: usize = 7;
@@ -679,6 +679,24 @@ where
         let number_of_documents = self.index.number_of_documents(self.wtxn)?;
         let mut rng = rand::rngs::StdRng::seed_from_u64(42);
 
+        // If an embedder wasn't used in the typedchunk but must be binary quantized
+        // we should insert it in `dimension`
+        for (name, action) in settings_diff.embedding_config_updates.iter() {
+            if action.is_being_quantized && !dimension.contains_key(name.as_str()) {
+                let index = self.index.embedder_category_id.get(self.wtxn, name)?.ok_or(
+                    InternalError::DatabaseMissingEntry {
+                        db_name: "embedder_category_id",
+                        key: None,
+                    },
+                )?;
+                let first_id = crate::vector::arroy_db_range_for_embedder(index).next().unwrap();
+                let reader =
+                    ArroyWrapper::new(self.index.vector_arroy, first_id, action.was_quantized);
+                let dim = reader.dimensions(self.wtxn)?;
+                dimension.insert(name.to_string(), dim);
+            }
+        }
+
         for (embedder_name, dimension) in dimension {
             let wtxn = &mut *self.wtxn;
             let vector_arroy = self.index.vector_arroy;
@@ -686,13 +704,23 @@ where
             let embedder_index = self.index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
                 InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
             )?;
+            let embedder_config = settings_diff.embedding_config_updates.get(&embedder_name);
+            let was_quantized = settings_diff
+                .old
+                .embedding_configs
+                .get(&embedder_name)
+                .map_or(false, |conf| conf.2);
+            let is_quantizing = embedder_config.map_or(false, |action| action.is_being_quantized);
 
             pool.install(|| {
                 for k in crate::vector::arroy_db_range_for_embedder(embedder_index) {
-                    let writer = arroy::Writer::new(vector_arroy, k, dimension);
-                    if writer.need_build(wtxn)? {
-                        writer.build(wtxn, &mut rng, None)?;
-                    } else if writer.is_empty(wtxn)? {
+                    let mut writer = ArroyWrapper::new(vector_arroy, k, was_quantized);
+                    if is_quantizing {
+                        writer.quantize(wtxn, k, dimension)?;
+                    }
+                    if writer.need_build(wtxn, dimension)? {
+                        writer.build(wtxn, &mut rng, dimension)?;
+                    } else if writer.is_empty(wtxn, dimension)? {
                         break;
                     }
                 }
@@ -2746,6 +2774,7 @@ mod tests {
                         response: Setting::NotSet,
                         distribution: Setting::NotSet,
                         headers: Setting::NotSet,
+                        binary_quantized: Setting::NotSet,
                     }),
                 );
                 settings.set_embedder_settings(embedders);
@@ -2774,7 +2803,7 @@ mod tests {
             std::sync::Arc::new(crate::vector::Embedder::new(embedder.embedder_options).unwrap());
         let res = index
             .search(&rtxn)
-            .semantic(embedder_name, embedder, Some([0.0, 1.0, 2.0].to_vec()))
+            .semantic(embedder_name, embedder, false, Some([0.0, 1.0, 2.0].to_vec()))
             .execute()
             .unwrap();
         assert_eq!(res.documents_ids.len(), 3);
diff --git a/milli/src/update/index_documents/transform.rs b/milli/src/update/index_documents/transform.rs
index 73fa3ca7b..bb2cfe56c 100644
--- a/milli/src/update/index_documents/transform.rs
+++ b/milli/src/update/index_documents/transform.rs
@@ -28,7 +28,8 @@ use crate::update::index_documents::GrenadParameters;
 use crate::update::settings::{InnerIndexSettings, InnerIndexSettingsDiff};
 use crate::update::{AvailableDocumentsIds, UpdateIndexingStep};
 use crate::vector::parsed_vectors::{ExplicitVectors, VectorOrArrayOfVectors};
-use crate::vector::settings::{EmbedderAction, WriteBackToDocuments};
+use crate::vector::settings::WriteBackToDocuments;
+use crate::vector::ArroyWrapper;
 use crate::{
     is_faceted_by, FieldDistribution, FieldId, FieldIdMapMissingEntry, FieldsIdsMap, Index, Result,
 };
@@ -989,19 +990,17 @@ impl<'a, 'i> Transform<'a, 'i> {
             None
         };
 
-        let readers: Result<
-            BTreeMap<&str, (Vec<arroy::Reader<'_, arroy::distances::Angular>>, &RoaringBitmap)>,
-        > = settings_diff
+        let readers: Result<BTreeMap<&str, (Vec<ArroyWrapper>, &RoaringBitmap)>> = settings_diff
             .embedding_config_updates
             .iter()
             .filter_map(|(name, action)| {
-                if let EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
-                    embedder_id,
-                    user_provided,
-                }) = action
+                if let Some(WriteBackToDocuments { embedder_id, user_provided }) =
+                    action.write_back()
                 {
-                    let readers: Result<Vec<_>> =
-                        self.index.arroy_readers(wtxn, *embedder_id).collect();
+                    let readers: Result<Vec<_>> = self
+                        .index
+                        .arroy_readers(wtxn, *embedder_id, action.was_quantized)
+                        .collect();
                     match readers {
                         Ok(readers) => Some(Ok((name.as_str(), (readers, user_provided)))),
                         Err(error) => Some(Err(error)),
@@ -1104,23 +1103,14 @@ impl<'a, 'i> Transform<'a, 'i> {
             }
         }
 
-        let mut writers = Vec::new();
-
         // delete all vectors from the embedders that need removal
         for (_, (readers, _)) in readers {
             for reader in readers {
-                let dimensions = reader.dimensions();
-                let arroy_index = reader.index();
-                drop(reader);
-                let writer = arroy::Writer::new(self.index.vector_arroy, arroy_index, dimensions);
-                writers.push(writer);
+                let dimensions = reader.dimensions(wtxn)?;
+                reader.clear(wtxn, dimensions)?;
             }
         }
 
-        for writer in writers {
-            writer.clear(wtxn)?;
-        }
-
         let grenad_params = GrenadParameters {
             chunk_compression_type: self.indexer_settings.chunk_compression_type,
             chunk_compression_level: self.indexer_settings.chunk_compression_level,
diff --git a/milli/src/update/index_documents/typed_chunk.rs b/milli/src/update/index_documents/typed_chunk.rs
index 9de95778b..97a4bf712 100644
--- a/milli/src/update/index_documents/typed_chunk.rs
+++ b/milli/src/update/index_documents/typed_chunk.rs
@@ -27,6 +27,7 @@ use crate::update::index_documents::helpers::{
     as_cloneable_grenad, keep_latest_obkv, try_split_array_at,
 };
 use crate::update::settings::InnerIndexSettingsDiff;
+use crate::vector::ArroyWrapper;
 use crate::{
     lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, FieldId, GeoPoint, Index, InternalError,
     Result, SerializationError, U8StrStrCodec,
@@ -666,9 +667,14 @@ pub(crate) fn write_typed_chunk_into_index(
             let embedder_index = index.embedder_category_id.get(wtxn, &embedder_name)?.ok_or(
                 InternalError::DatabaseMissingEntry { db_name: "embedder_category_id", key: None },
             )?;
+            let binary_quantized = settings_diff
+                .old
+                .embedding_configs
+                .get(&embedder_name)
+                .map_or(false, |conf| conf.2);
             // FIXME: allow customizing distance
             let writers: Vec<_> = crate::vector::arroy_db_range_for_embedder(embedder_index)
-                .map(|k| arroy::Writer::new(index.vector_arroy, k, expected_dimension))
+                .map(|k| ArroyWrapper::new(index.vector_arroy, k, binary_quantized))
                 .collect();
 
             // remove vectors for docids we want them removed
@@ -679,7 +685,7 @@ pub(crate) fn write_typed_chunk_into_index(
 
                 for writer in &writers {
                     // Uses invariant: vectors are packed in the first writers.
-                    if !writer.del_item(wtxn, docid)? {
+                    if !writer.del_item(wtxn, expected_dimension, docid)? {
                         break;
                     }
                 }
@@ -711,7 +717,7 @@ pub(crate) fn write_typed_chunk_into_index(
                     )));
                 }
                 for (embedding, writer) in embeddings.iter().zip(&writers) {
-                    writer.add_item(wtxn, docid, embedding)?;
+                    writer.add_item(wtxn, expected_dimension, docid, embedding)?;
                 }
             }
 
@@ -734,7 +740,7 @@ pub(crate) fn write_typed_chunk_into_index(
                             break;
                         };
                         if candidate == vector {
-                            writer.del_item(wtxn, docid)?;
+                            writer.del_item(wtxn, expected_dimension, docid)?;
                             deleted_index = Some(index);
                         }
                     }
@@ -751,8 +757,13 @@ pub(crate) fn write_typed_chunk_into_index(
                         if let Some((last_index, vector)) = last_index_with_a_vector {
                             // unwrap: computed the index from the list of writers
                             let writer = writers.get(last_index).unwrap();
-                            writer.del_item(wtxn, docid)?;
-                            writers.get(deleted_index).unwrap().add_item(wtxn, docid, &vector)?;
+                            writer.del_item(wtxn, expected_dimension, docid)?;
+                            writers.get(deleted_index).unwrap().add_item(
+                                wtxn,
+                                expected_dimension,
+                                docid,
+                                &vector,
+                            )?;
                         }
                     }
                 }
@@ -762,8 +773,8 @@ pub(crate) fn write_typed_chunk_into_index(
 
                     // overflow was detected during vector extraction.
                     for writer in &writers {
-                        if !writer.contains_item(wtxn, docid)? {
-                            writer.add_item(wtxn, docid, &vector)?;
+                        if !writer.contains_item(wtxn, expected_dimension, docid)? {
+                            writer.add_item(wtxn, expected_dimension, docid, &vector)?;
                             break;
                         }
                     }
diff --git a/milli/src/update/settings.rs b/milli/src/update/settings.rs
index 8702e7ea6..6e2b53d58 100644
--- a/milli/src/update/settings.rs
+++ b/milli/src/update/settings.rs
@@ -954,7 +954,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
                 let old_configs = self.index.embedding_configs(self.wtxn)?;
                 let remove_all: Result<BTreeMap<String, EmbedderAction>> = old_configs
                     .into_iter()
-                    .map(|IndexEmbeddingConfig { name, config: _, user_provided }| -> Result<_> {
+                    .map(|IndexEmbeddingConfig { name, config, user_provided }| -> Result<_> {
                         let embedder_id =
                             self.index.embedder_category_id.get(self.wtxn, &name)?.ok_or(
                                 crate::InternalError::DatabaseMissingEntry {
@@ -964,10 +964,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
                             )?;
                         Ok((
                             name,
-                            EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
-                                embedder_id,
-                                user_provided,
-                            }),
+                            EmbedderAction::with_write_back(
+                                WriteBackToDocuments { embedder_id, user_provided },
+                                config.quantized(),
+                            ),
                         ))
                     })
                     .collect();
@@ -1004,7 +1004,8 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
             match joined {
                 // updated config
                 EitherOrBoth::Both((name, (old, user_provided)), (_, new)) => {
-                    let settings_diff = SettingsDiff::from_settings(old, new);
+                    let was_quantized = old.binary_quantized.set().unwrap_or_default();
+                    let settings_diff = SettingsDiff::from_settings(&name, old, new)?;
                     match settings_diff {
                         SettingsDiff::Remove => {
                             tracing::debug!(
@@ -1023,25 +1024,29 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
                             self.index.embedder_category_id.delete(self.wtxn, &name)?;
                             embedder_actions.insert(
                                 name,
-                                EmbedderAction::WriteBackToDocuments(WriteBackToDocuments {
-                                    embedder_id,
-                                    user_provided,
-                                }),
+                                EmbedderAction::with_write_back(
+                                    WriteBackToDocuments { embedder_id, user_provided },
+                                    was_quantized,
+                                ),
                             );
                         }
-                        SettingsDiff::Reindex { action, updated_settings } => {
+                        SettingsDiff::Reindex { action, updated_settings, quantize } => {
                             tracing::debug!(
                                 embedder = name,
                                 user_provided = user_provided.len(),
                                 ?action,
                                 "reindex embedder"
                             );
-                            embedder_actions.insert(name.clone(), EmbedderAction::Reindex(action));
+                            embedder_actions.insert(
+                                name.clone(),
+                                EmbedderAction::with_reindex(action, was_quantized)
+                                    .with_is_being_quantized(quantize),
+                            );
                             let new =
                                 validate_embedding_settings(Setting::Set(updated_settings), &name)?;
                             updated_configs.insert(name, (new, user_provided));
                         }
-                        SettingsDiff::UpdateWithoutReindex { updated_settings } => {
+                        SettingsDiff::UpdateWithoutReindex { updated_settings, quantize } => {
                             tracing::debug!(
                                 embedder = name,
                                 user_provided = user_provided.len(),
@@ -1049,6 +1054,12 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
                             );
                             let new =
                                 validate_embedding_settings(Setting::Set(updated_settings), &name)?;
+                            if quantize {
+                                embedder_actions.insert(
+                                    name.clone(),
+                                    EmbedderAction::default().with_is_being_quantized(true),
+                                );
+                            }
                             updated_configs.insert(name, (new, user_provided));
                         }
                     }
@@ -1067,8 +1078,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
                         &mut setting,
                     );
                     let setting = validate_embedding_settings(setting, &name)?;
-                    embedder_actions
-                        .insert(name.clone(), EmbedderAction::Reindex(ReindexAction::FullReindex));
+                    embedder_actions.insert(
+                        name.clone(),
+                        EmbedderAction::with_reindex(ReindexAction::FullReindex, false),
+                    );
                     updated_configs.insert(name, (setting, RoaringBitmap::new()));
                 }
             }
@@ -1082,19 +1095,14 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
         let mut find_free_index =
             move || free_indices.find(|(_, free)| **free).map(|(index, _)| index as u8);
         for (name, action) in embedder_actions.iter() {
-            match action {
-                EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => {
-                    /* cannot be a new embedder, so has to have an id already */
-                }
-                EmbedderAction::Reindex(ReindexAction::FullReindex) => {
-                    if self.index.embedder_category_id.get(self.wtxn, name)?.is_none() {
-                        let id = find_free_index()
-                            .ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
-                        tracing::debug!(embedder = name, id, "assigning free id to new embedder");
-                        self.index.embedder_category_id.put(self.wtxn, name, &id)?;
-                    }
-                }
-                EmbedderAction::WriteBackToDocuments(_) => { /* already removed */ }
+            // ignore actions that are not possible for a new embedder
+            if matches!(action.reindex(), Some(ReindexAction::FullReindex))
+                && self.index.embedder_category_id.get(self.wtxn, name)?.is_none()
+            {
+                let id =
+                    find_free_index().ok_or(UserError::TooManyEmbedders(updated_configs.len()))?;
+                tracing::debug!(embedder = name, id, "assigning free id to new embedder");
+                self.index.embedder_category_id.put(self.wtxn, name, &id)?;
             }
         }
         let updated_configs: Vec<IndexEmbeddingConfig> = updated_configs
@@ -1277,7 +1285,11 @@ impl InnerIndexSettingsDiff {
 
         // if the user-defined searchables changed, then we need to reindex prompts.
         if cache_user_defined_searchables {
-            for (embedder_name, (config, _)) in new_settings.embedding_configs.inner_as_ref() {
+            for (embedder_name, (config, _, _quantized)) in
+                new_settings.embedding_configs.inner_as_ref()
+            {
+                let was_quantized =
+                    old_settings.embedding_configs.get(embedder_name).map_or(false, |conf| conf.2);
                 // skip embedders that don't use document templates
                 if !config.uses_document_template() {
                     continue;
@@ -1287,16 +1299,19 @@ impl InnerIndexSettingsDiff {
                 // this always makes the code clearer by explicitly handling the cases
                 match embedding_config_updates.entry(embedder_name.clone()) {
                     std::collections::btree_map::Entry::Vacant(entry) => {
-                        entry.insert(EmbedderAction::Reindex(ReindexAction::RegeneratePrompts));
+                        entry.insert(EmbedderAction::with_reindex(
+                            ReindexAction::RegeneratePrompts,
+                            was_quantized,
+                        ));
+                    }
+                    std::collections::btree_map::Entry::Occupied(entry) => {
+                        let EmbedderAction {
+                            was_quantized: _,
+                            is_being_quantized: _,
+                            write_back: _, // We are deleting this embedder, so no point in regeneration
+                            reindex: _,    // We are already fully reindexing
+                        } = entry.get();
                     }
-                    std::collections::btree_map::Entry::Occupied(entry) => match entry.get() {
-                        EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */
-                        }
-                        EmbedderAction::Reindex(ReindexAction::FullReindex) => { /* we are already fully reindexing */
-                        }
-                        EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { /* we are already regenerating prompts */
-                        }
-                    },
                 };
             }
         }
@@ -1546,7 +1561,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
         .map(
             |IndexEmbeddingConfig {
                  name,
-                 config: EmbeddingConfig { embedder_options, prompt },
+                 config: EmbeddingConfig { embedder_options, prompt, quantized },
                  ..
              }| {
                 let prompt = Arc::new(prompt.try_into().map_err(crate::Error::from)?);
@@ -1556,7 +1571,7 @@ fn embedders(embedding_configs: Vec<IndexEmbeddingConfig>) -> Result<EmbeddingCo
                         .map_err(crate::vector::Error::from)
                         .map_err(crate::Error::from)?,
                 );
-                Ok((name, (embedder, prompt)))
+                Ok((name, (embedder, prompt, quantized.unwrap_or_default())))
             },
         )
         .collect();
@@ -1581,6 +1596,7 @@ fn validate_prompt(
             response,
             distribution,
             headers,
+            binary_quantized: binary_quantize,
         }) => {
             let max_bytes = match document_template_max_bytes.set() {
                 Some(max_bytes) => NonZeroUsize::new(max_bytes).ok_or_else(|| {
@@ -1613,6 +1629,7 @@ fn validate_prompt(
                 response,
                 distribution,
                 headers,
+                binary_quantized: binary_quantize,
             }))
         }
         new => Ok(new),
@@ -1638,6 +1655,7 @@ pub fn validate_embedding_settings(
         response,
         distribution,
         headers,
+        binary_quantized: binary_quantize,
     } = settings;
 
     if let Some(0) = dimensions.set() {
@@ -1678,6 +1696,7 @@ pub fn validate_embedding_settings(
             response,
             distribution,
             headers,
+            binary_quantized: binary_quantize,
         }));
     };
     match inferred_source {
@@ -1779,6 +1798,7 @@ pub fn validate_embedding_settings(
         response,
         distribution,
         headers,
+        binary_quantized: binary_quantize,
     }))
 }
 
diff --git a/milli/src/vector/mod.rs b/milli/src/vector/mod.rs
index 23417ced2..d52e68bbe 100644
--- a/milli/src/vector/mod.rs
+++ b/milli/src/vector/mod.rs
@@ -1,8 +1,12 @@
 use std::collections::HashMap;
 use std::sync::Arc;
 
+use arroy::distances::{Angular, BinaryQuantizedAngular};
+use arroy::ItemId;
 use deserr::{DeserializeError, Deserr};
+use heed::{RoTxn, RwTxn, Unspecified};
 use ordered_float::OrderedFloat;
+use roaring::RoaringBitmap;
 use serde::{Deserialize, Serialize};
 
 use self::error::{EmbedError, NewEmbedderError};
@@ -26,6 +30,171 @@ pub type Embedding = Vec<f32>;
 
 pub const REQUEST_PARALLELISM: usize = 40;
 
+pub struct ArroyWrapper {
+    quantized: bool,
+    index: u16,
+    database: arroy::Database<Unspecified>,
+}
+
+impl ArroyWrapper {
+    pub fn new(database: arroy::Database<Unspecified>, index: u16, quantized: bool) -> Self {
+        Self { database, index, quantized }
+    }
+
+    pub fn index(&self) -> u16 {
+        self.index
+    }
+
+    pub fn dimensions(&self, rtxn: &RoTxn) -> Result<usize, arroy::Error> {
+        if self.quantized {
+            Ok(arroy::Reader::open(rtxn, self.index, self.quantized_db())?.dimensions())
+        } else {
+            Ok(arroy::Reader::open(rtxn, self.index, self.angular_db())?.dimensions())
+        }
+    }
+
+    pub fn quantize(
+        &mut self,
+        wtxn: &mut RwTxn,
+        index: u16,
+        dimension: usize,
+    ) -> Result<(), arroy::Error> {
+        if !self.quantized {
+            let writer = arroy::Writer::new(self.angular_db(), index, dimension);
+            writer.prepare_changing_distance::<BinaryQuantizedAngular>(wtxn)?;
+            self.quantized = true;
+        }
+        Ok(())
+    }
+
+    pub fn need_build(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).need_build(rtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).need_build(rtxn)
+        }
+    }
+
+    pub fn build<R: rand::Rng + rand::SeedableRng>(
+        &self,
+        wtxn: &mut RwTxn,
+        rng: &mut R,
+        dimension: usize,
+    ) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).build(wtxn, rng, None)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).build(wtxn, rng, None)
+        }
+    }
+
+    pub fn add_item(
+        &self,
+        wtxn: &mut RwTxn,
+        dimension: usize,
+        item_id: arroy::ItemId,
+        vector: &[f32],
+    ) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension)
+                .add_item(wtxn, item_id, vector)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension)
+                .add_item(wtxn, item_id, vector)
+        }
+    }
+
+    pub fn del_item(
+        &self,
+        wtxn: &mut RwTxn,
+        dimension: usize,
+        item_id: arroy::ItemId,
+    ) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).del_item(wtxn, item_id)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).del_item(wtxn, item_id)
+        }
+    }
+
+    pub fn clear(&self, wtxn: &mut RwTxn, dimension: usize) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).clear(wtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).clear(wtxn)
+        }
+    }
+
+    pub fn is_empty(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).is_empty(rtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).is_empty(rtxn)
+        }
+    }
+
+    pub fn contains_item(
+        &self,
+        rtxn: &RoTxn,
+        dimension: usize,
+        item: arroy::ItemId,
+    ) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).contains_item(rtxn, item)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).contains_item(rtxn, item)
+        }
+    }
+
+    pub fn nns_by_item(
+        &self,
+        rtxn: &RoTxn,
+        item: ItemId,
+        limit: usize,
+        filter: Option<&RoaringBitmap>,
+    ) -> Result<Option<Vec<(ItemId, f32)>>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(rtxn, self.index, self.quantized_db())?
+                .nns_by_item(rtxn, item, limit, None, None, filter)
+        } else {
+            arroy::Reader::open(rtxn, self.index, self.angular_db())?
+                .nns_by_item(rtxn, item, limit, None, None, filter)
+        }
+    }
+
+    pub fn nns_by_vector(
+        &self,
+        txn: &RoTxn,
+        item: &[f32],
+        limit: usize,
+        filter: Option<&RoaringBitmap>,
+    ) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(txn, self.index, self.quantized_db())?
+                .nns_by_vector(txn, item, limit, None, None, filter)
+        } else {
+            arroy::Reader::open(txn, self.index, self.angular_db())?
+                .nns_by_vector(txn, item, limit, None, None, filter)
+        }
+    }
+
+    pub fn item_vector(&self, rtxn: &RoTxn, docid: u32) -> Result<Option<Vec<f32>>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(rtxn, self.index, self.quantized_db())?.item_vector(rtxn, docid)
+        } else {
+            arroy::Reader::open(rtxn, self.index, self.angular_db())?.item_vector(rtxn, docid)
+        }
+    }
+
+    fn angular_db(&self) -> arroy::Database<Angular> {
+        self.database.remap_data_type()
+    }
+
+    fn quantized_db(&self) -> arroy::Database<BinaryQuantizedAngular> {
+        self.database.remap_data_type()
+    }
+}
+
 /// One or multiple embeddings stored consecutively in a flat vector.
 pub struct Embeddings<F> {
     data: Vec<F>,
@@ -124,39 +293,48 @@ pub struct EmbeddingConfig {
     pub embedder_options: EmbedderOptions,
     /// Document template
     pub prompt: PromptData,
+    /// If this embedder is binary quantized
+    pub quantized: Option<bool>,
     // TODO: add metrics and anything needed
 }
 
+impl EmbeddingConfig {
+    pub fn quantized(&self) -> bool {
+        self.quantized.unwrap_or_default()
+    }
+}
+
 /// Map of embedder configurations.
 ///
 /// Each configuration is mapped to a name.
 #[derive(Clone, Default)]
-pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>)>);
+pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>);
 
 impl EmbeddingConfigs {
     /// Create the map from its internal component.s
-    pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>)>) -> Self {
+    pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>) -> Self {
         Self(data)
     }
 
     /// Get an embedder configuration and template from its name.
-    pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>)> {
+    pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>, bool)> {
         self.0.get(name).cloned()
     }
 
-    pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
+    pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
         &self.0
     }
 
-    pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>)> {
+    pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
         self.0
     }
 }
 
 impl IntoIterator for EmbeddingConfigs {
-    type Item = (String, (Arc<Embedder>, Arc<Prompt>));
+    type Item = (String, (Arc<Embedder>, Arc<Prompt>, bool));
 
-    type IntoIter = std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>)>;
+    type IntoIter =
+        std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>, bool)>;
 
     fn into_iter(self) -> Self::IntoIter {
         self.0.into_iter()
diff --git a/milli/src/vector/settings.rs b/milli/src/vector/settings.rs
index b7ae90d89..3bb7f09e6 100644
--- a/milli/src/vector/settings.rs
+++ b/milli/src/vector/settings.rs
@@ -32,6 +32,9 @@ pub struct EmbeddingSettings {
     pub dimensions: Setting<usize>,
     #[serde(default, skip_serializing_if = "Setting::is_not_set")]
     #[deserr(default)]
+    pub binary_quantized: Setting<bool>,
+    #[serde(default, skip_serializing_if = "Setting::is_not_set")]
+    #[deserr(default)]
     pub document_template: Setting<String>,
     #[serde(default, skip_serializing_if = "Setting::is_not_set")]
     #[deserr(default)]
@@ -85,23 +88,63 @@ pub enum ReindexAction {
 
 pub enum SettingsDiff {
     Remove,
-    Reindex { action: ReindexAction, updated_settings: EmbeddingSettings },
-    UpdateWithoutReindex { updated_settings: EmbeddingSettings },
+    Reindex { action: ReindexAction, updated_settings: EmbeddingSettings, quantize: bool },
+    UpdateWithoutReindex { updated_settings: EmbeddingSettings, quantize: bool },
 }
 
-pub enum EmbedderAction {
-    WriteBackToDocuments(WriteBackToDocuments),
-    Reindex(ReindexAction),
+#[derive(Default, Debug)]
+pub struct EmbedderAction {
+    pub was_quantized: bool,
+    pub is_being_quantized: bool,
+    pub write_back: Option<WriteBackToDocuments>,
+    pub reindex: Option<ReindexAction>,
 }
 
+impl EmbedderAction {
+    pub fn is_being_quantized(&self) -> bool {
+        self.is_being_quantized
+    }
+
+    pub fn write_back(&self) -> Option<&WriteBackToDocuments> {
+        self.write_back.as_ref()
+    }
+
+    pub fn reindex(&self) -> Option<&ReindexAction> {
+        self.reindex.as_ref()
+    }
+
+    pub fn with_is_being_quantized(mut self, quantize: bool) -> Self {
+        self.is_being_quantized = quantize;
+        self
+    }
+
+    pub fn with_write_back(write_back: WriteBackToDocuments, was_quantized: bool) -> Self {
+        Self {
+            was_quantized,
+            is_being_quantized: false,
+            write_back: Some(write_back),
+            reindex: None,
+        }
+    }
+
+    pub fn with_reindex(reindex: ReindexAction, was_quantized: bool) -> Self {
+        Self { was_quantized, is_being_quantized: false, write_back: None, reindex: Some(reindex) }
+    }
+}
+
+#[derive(Debug)]
 pub struct WriteBackToDocuments {
     pub embedder_id: u8,
     pub user_provided: RoaringBitmap,
 }
 
 impl SettingsDiff {
-    pub fn from_settings(old: EmbeddingSettings, new: Setting<EmbeddingSettings>) -> Self {
-        match new {
+    pub fn from_settings(
+        embedder_name: &str,
+        old: EmbeddingSettings,
+        new: Setting<EmbeddingSettings>,
+    ) -> Result<Self, UserError> {
+        let ret = match new {
             Setting::Set(new) => {
                 let EmbeddingSettings {
                     mut source,
@@ -116,6 +159,7 @@ impl SettingsDiff {
                     mut distribution,
                     mut headers,
                     mut document_template_max_bytes,
+                    binary_quantized: mut binary_quantize,
                 } = old;
 
                 let EmbeddingSettings {
@@ -131,8 +175,17 @@ impl SettingsDiff {
                     distribution: new_distribution,
                     headers: new_headers,
                     document_template_max_bytes: new_document_template_max_bytes,
+                    binary_quantized: new_binary_quantize,
                 } = new;
 
+                if matches!(binary_quantize, Setting::Set(true))
+                    && matches!(new_binary_quantize, Setting::Set(false))
+                {
+                    return Err(UserError::InvalidDisableBinaryQuantization {
+                        embedder_name: embedder_name.to_string(),
+                    });
+                }
+
                 let mut reindex_action = None;
 
                 // **Warning**: do not use short-circuiting || here, we want all these operations applied
@@ -172,6 +225,7 @@ impl SettingsDiff {
                         _ => {}
                     }
                 }
+                let binary_quantize_changed = binary_quantize.apply(new_binary_quantize);
                 if url.apply(new_url) {
                     match source {
                         // do not regenerate on an url change in OpenAI
@@ -231,16 +285,27 @@ impl SettingsDiff {
                     distribution,
                     headers,
                     document_template_max_bytes,
+                    binary_quantized: binary_quantize,
                 };
 
                 match reindex_action {
-                    Some(action) => Self::Reindex { action, updated_settings },
-                    None => Self::UpdateWithoutReindex { updated_settings },
+                    Some(action) => Self::Reindex {
+                        action,
+                        updated_settings,
+                        quantize: binary_quantize_changed,
+                    },
+                    None => Self::UpdateWithoutReindex {
+                        updated_settings,
+                        quantize: binary_quantize_changed,
+                    },
                 }
             }
             Setting::Reset => Self::Remove,
-            Setting::NotSet => Self::UpdateWithoutReindex { updated_settings: old },
-        }
+            Setting::NotSet => {
+                Self::UpdateWithoutReindex { updated_settings: old, quantize: false }
+            }
+        };
+        Ok(ret)
     }
 }
 
@@ -486,7 +551,7 @@ impl std::fmt::Display for EmbedderSource {
 
 impl From<EmbeddingConfig> for EmbeddingSettings {
     fn from(value: EmbeddingConfig) -> Self {
-        let EmbeddingConfig { embedder_options, prompt } = value;
+        let EmbeddingConfig { embedder_options, prompt, quantized } = value;
         let document_template_max_bytes =
             Setting::Set(prompt.max_bytes.unwrap_or(default_max_bytes()).get());
         match embedder_options {
@@ -507,6 +572,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 response: Setting::NotSet,
                 headers: Setting::NotSet,
                 distribution: Setting::some_or_not_set(distribution),
+                binary_quantized: Setting::some_or_not_set(quantized),
             },
             super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions {
                 url,
@@ -527,6 +593,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 response: Setting::NotSet,
                 headers: Setting::NotSet,
                 distribution: Setting::some_or_not_set(distribution),
+                binary_quantized: Setting::some_or_not_set(quantized),
             },
             super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions {
                 embedding_model,
@@ -547,6 +614,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 response: Setting::NotSet,
                 headers: Setting::NotSet,
                 distribution: Setting::some_or_not_set(distribution),
+                binary_quantized: Setting::some_or_not_set(quantized),
             },
             super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions {
                 dimensions,
@@ -564,6 +632,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 response: Setting::NotSet,
                 headers: Setting::NotSet,
                 distribution: Setting::some_or_not_set(distribution),
+                binary_quantized: Setting::some_or_not_set(quantized),
             },
             super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
                 api_key,
@@ -586,6 +655,7 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 response: Setting::Set(response),
                 distribution: Setting::some_or_not_set(distribution),
                 headers: Setting::Set(headers),
+                binary_quantized: Setting::some_or_not_set(quantized),
             },
         }
     }
@@ -607,8 +677,11 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
             response,
             distribution,
             headers,
+            binary_quantized,
         } = value;
 
+        this.quantized = binary_quantized.set();
+
         if let Some(source) = source.set() {
             match source {
                 EmbedderSource::OpenAi => {