diff --git a/docs/reference/elasticsearch/index-settings/bbq.md b/docs/reference/elasticsearch/index-settings/bbq.md
index a53cd6e9c83c6..947b8d5916b1a 100644
--- a/docs/reference/elasticsearch/index-settings/bbq.md
+++ b/docs/reference/elasticsearch/index-settings/bbq.md
@@ -131,6 +131,111 @@ PUT bbq_flat-index
 }
 ```
 
+### `bbq_disk` [bbq-disk]
+
+```{applies_to}
+stack: ga 9.2
+```
+
+When you set a dense vector field’s `index_options` parameter to `type: bbq_disk`, {{es}} uses the DiskBBQ algorithm, a disk-based alternative to HNSW for [kNN search](https://www.elastic.co/docs//solutions/search/vector/knn) on compressed vectors. DiskBBQ stores the vector data on disk instead of in memory, lowering RAM requirements and reducing the overall cost of vector storage and search.
+
+DiskBBQ groups similar vectors into small clusters using [hierarchical K-means](https://www.elastic.co/search-labs/blog/k-means-for-vector-indices). When processing a query, it finds the centroids closest to the query vector and only compares the vectors within those clusters. This targeted approach reduces the number of in-memory operations, making it ideal for large-scale or memory-constrained environments.
+
+DiskBBQ typically performs well for recall levels up to around 95%. For use cases requiring exceptionally high recall (99% or higher), many vector clusters may need to be visited, which can negatively impact performance. In very high recall cases, [bbq_hnsw](#bbq-hnsw), or other HNSW-based formats deliver better performance depending on memory availability.
+
+The following example creates an index with a `dense_vector` field configured to use the `bbq_disk` algorithm.
+
+```console
+PUT bbq_disk-index
+{
+  "mappings": {
+    "properties": {
+      "my_vector": {
+        "type": "dense_vector",
+        "dims": 3,
+        "similarity": "l2_norm",
+        "index": true,
+        "index_options": {
+          "type": "bbq_disk"
+        }
+      }
+    }
+  }
+}
+```
+
+To change an existing index to use `bbq_disk`, update the field mapping:
+
+```console
+PUT bbq_disk-index/_mapping
+{
+  "properties": {
+    "my_vector": {
+      "type": "dense_vector",
+      "dims": 64,
+      "index": true,
+      "index_options": {
+        "type": "bbq_disk"
+      }
+    }
+  }
+}
+```
+
+To apply `bbq_disk` to all vectors at once, reindex them into a new index where the `index_options` parameter's `type` is set to `bbq_disk`:
+
+:::::{stepper}
+::::{step} Create a destination index
+```console
+PUT my-index-bbq
+{
+  "mappings": {
+    "properties": {
+      "my_vector": {
+        "type": "dense_vector",
+        "dims": 64,
+        "index": true,
+        "index_options": {
+          "type": "bbq_disk"
+        }
+      }
+    }
+  }
+}
+```
+::::
+
+::::{step} Reindex the data
+```console
+POST _reindex
+{
+  "source": { "index": "my-index" }, <1>
+  "dest":   { "index": "my-index-bbq" }
+}
+```
+1. The existing index to be reindexed into the newly created index with the `bbq_disk` algorithm.
+::::
+
+:::::
+
+You can set the `visit_percentage` parameter to define the fraction of vectors visited per shard during search.
+
+```console
+POST bbq_disk-index/_search
+{
+  "query": {
+    "knn": {
+      "field": "my_vector",
+      "query_vector": [0.0127, 0.1230, 0.3929],
+      "k": 10,
+      "visit_percentage": 10.0
+    }
+  }
+}
+```
+
+A lower `visit_percentage` can further reduce memory use and speed up queries, while a higher value can improve recall. Learn more about [top-level parameters for knn](/reference/query-languages/query-dsl/query-dsl-knn-query.md#knn-query-top-level-parameters) queries.
+
 ## Oversampling [bbq-oversampling]
 
 Oversampling is a technique used with BBQ searches to reduce the accuracy loss from compression. Compression lowers the memory footprint by over 95% and improves query latency, at the cost of decreased result accuracy. This decrease can be mitigated by oversampling during query time and reranking the top results using the full vector. 
@@ -158,5 +263,6 @@ You can change oversampling from the default 3× to another value. Refer to [Ove
 ## Learn more [bbq-learn-more]
 
 - [Better Binary Quantization (BBQ) in Lucene and {{es}}](https://www.elastic.co/search-labs/blog/better-binary-quantization-lucene-elasticsearch) - Learn how BBQ works, its benefits, and how it reduces memory usage while preserving search accuracy.
+- [Introducing a new vector storage format: DiskBBQ](https://www.elastic.co/search-labs/blog/diskbbq-elasticsearch-introduction) - Learn how DiskBBQ improves vector search in low-memory environments and compares to HNSW in speed and cost-effectiveness. 
 - [Dense vector field type](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/dense-vector) - Find code examples for using `bbq_hnsw` `index_type`.
 - [kNN search](https://www.elastic.co/docs/solutions/search/vector/knn) - Learn about the search algorithm that BBQ works with.
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