{{Short description|Type of database that uses vectors to represent other data}} {{Machine learning}} A '''vector database''', '''vector store''' or '''vector search engine''' is a database that stores and retrieves embeddings of data in vector space.<ref name="auto1" /> Vector databases typically implement approximate nearest neighbor algorithms so users can search for records semantically similar to a given input, unlike traditional databases which primarily look up records by exact match.<ref>{{cite web |author1=Roie Schwaber-Cohen |title=What is a Vector Database & How Does it Work |url=https://www.pinecone.io/learn/vector-database/ |access-date=18 November 2023 |publisher=Pinecone}}</ref><ref name=":0">{{cite web |title=What is a vector database |url=https://www.elastic.co/what-is/vector-database |access-date=18 November 2023 |publisher=Elastic}}</ref> Use-cases for vector databases include similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation (RAG).<ref name="auto1" />
Vector embeddings are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. Each data item is represented by one vector in this space. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized.<ref name="auto1">{{Cite web |last= |date=2023-12-26 |title=Vector database |url=https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database |access-date=2024-01-11 |website=learn.microsoft.com |language=}}</ref>
These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings<ref>{{Cite web |author=Evan Chaki |date=2023-07-31 |title=What is a vector database? |url=https://learn.microsoft.com/en-us/semantic-kernel/memories/vector-db |accessdate= |publisher=Microsoft |quote=A vector database is a type of database that stores data as high-dimensional vectors, which are mathematical representations of features or attributes.}}</ref> or deep learning networks. The goal is that semantically similar data items receive feature vectors close to each other.
Vector retrieval can be combined with metadata filtering or lexical search to support filtered and hybrid retrieval workflows.<ref>{{Cite web |title=Hybrid search using vectors and full text in Azure AI Search |url=https://learn.microsoft.com/en-us/azure/search/hybrid-search-overview |website=Microsoft Learn |access-date=2026-05-05}}</ref><ref>{{Cite web |title=Add a filter to a vector query in Azure AI Search |url=https://learn.microsoft.com/en-us/azure/search/vector-search-filters |website=Microsoft Learn |access-date=2026-05-05}}</ref>
== Techniques == Common techniques for similarity search on high-dimensional vectors include: * Hierarchical Navigable Small World (HNSW) graphs * Locality-sensitive hashing (LSH) and sketching * Product quantization (PQ) * Inverted files
These techniques may also be combined in vector search systems.<ref>{{Cite arXiv |last1=Pan |first1=James Jie |last2=Wang |first2=Jianguo |last3=Li |first3=Guoliang |title=Survey of Vector Database Management Systems |date=2023-10-21 |eprint=2310.14021 |class=cs.DB}}</ref>
In recent benchmarks, HNSW-based implementations have been among the best performers.<ref>{{Citation |last1=Aumüller |first1=Martin |title=ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms |date=2017 |work=Similarity Search and Applications |volume=10609 |pages=34–49 |editor-last=Beecks |editor-first=Christian |url=http://link.springer.com/10.1007/978-3-319-68474-1_3 |access-date=2024-03-19 |place=Cham |publisher=Springer International Publishing |doi=10.1007/978-3-319-68474-1_3 |isbn=978-3-319-68473-4 |last2=Bernhardsson |first2=Erik |last3=Faithfull |first3=Alexander |editor2-last=Borutta |editor2-first=Felix |editor3-last=Kröger |editor3-first=Peer |editor4-last=Seidl |editor4-first=Thomas|arxiv=1807.05614 }}</ref><ref>{{Cite book |last1=Aumüller |first1=Martin |last2=Bernhardsson |first2=Erik |last3=Faithfull |first3=Alexander |chapter=ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms |series=Lecture Notes in Computer Science |date=2017 |volume=10609 |editor-last=Beecks |editor-first=Christian |editor2-last=Borutta |editor2-first=Felix |editor3-last=Kröger |editor3-first=Peer |editor4-last=Seidl |editor4-first=Thomas |title=Similarity Search and Applications |chapter-url=https://link.springer.com/chapter/10.1007/978-3-319-68474-1_3 |language=en |location=Cham |publisher=Springer International Publishing |pages=34–49 |doi=10.1007/978-3-319-68474-1_3 |isbn=978-3-319-68474-1|arxiv=1807.05614 }}</ref> Conferences such as the International Conference on Similarity Search and Applications (SISAP)<ref>{{Cite web |title=Task description and call for participation SISAP 2025 Indexing Challenge |url=https://sisap-challenges.github.io/2025/index.html |access-date=2026-01-01 |website=sisap-challenges.github.io}}</ref> and the Conference on Neural Information Processing Systems (NeurIPS)<ref>{{Cite web |title=NeurIPS Competition Practical Vector Search (Big ANN) Challenge 2023 |url=https://neurips.cc/virtual/2023/competition/66587 |access-date=2026-01-01 |website=neurips.cc}}</ref> have hosted competitions on vector search in large databases.
== Applications == Vector databases are used in a wide range of machine learning applications including similarity search, semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation.<ref name="auto1" />
=== Retrieval-augmented generation === {{Main|Retrieval-augmented generation}} An especially common use-case for vector databases is in retrieval-augmented generation (RAG), a method to improve domain-specific responses of large language models. The retrieval component of a RAG can be any search system, but is most often implemented as a vector database. Text documents describing the domain of interest are collected, and for each document or document section, a feature vector (known as an "embedding") is computed, typically using a deep learning network, and stored in a vector database along with a link to the document. Given a user prompt, the feature vector of the prompt is computed, and the database is queried to retrieve the most relevant documents. These are then automatically added into the context window of the large language model, and the large language model proceeds to create a response to the prompt given this context.<ref>{{cite journal |last1=Lewis |first1=Patrick |last2=Perez |first2=Ethan |last3=Piktus |first3=Aleksandra |last4=Petroni |first4=Fabio |last5=Karpukhin |first5=Vladimir |last6=Goyal |first6=Naman |last7=Küttler |first7=Heinrich |title=Retrieval-augmented generation for knowledge-intensive NLP tasks |journal=Advances in Neural Information Processing Systems 33 |year=2020 |pages=9459–9474|arxiv=2005.11401 }}</ref>
== Implementations == {{Dynamic list|multiple=no}}
{| class="wikitable sortable " |+ |- ! Name !! License |- |Aerospike<ref>{{Cite web |date=2024-05-07 |title=Aerospike Recognized by Independent Research Firm Among Notable Vendors in Vector Databases Report |url=https://www.morningstar.com/news/globe-newswire/9111790/aerospike-recognized-by-independent-research-firm-among-notable-vendors-in-vector-databases-report |access-date=2024-08-01 |website=Morningstar |language=en-US}}</ref><ref>{{Cite web |date=2024-04-04 |title=Aerospike raises $109M for its real-time database platform to capitalize on the AI boom |url=https://techcrunch.com/2024/04/04/aerospike-raises-100m-for-its-real-time-database-platform-to-capitalize-on-the-ai-boom/ |access-date=2024-08-01 |website=TechCrunch |language=en-US}}</ref> |Proprietary |- |AllegroGraph<ref>{{Cite web |date=2023-12-29 |title=AllegroGraph 8.0 Incorporates Neuro-Symbolic AI, a Pathway to AGI |url=https://thenewstack.io/allegrograph-8-0-incorporates-neuro-symbolic-ai-a-pathway-to-agi/ |access-date=2024-06-06 |website=TheNewStack |language=en-US}}</ref><ref>{{Cite web |date=2024-01-18 |title=Franz Inc. Introduces AllegroGraph Cloud: A Managed Service for Neuro-Symbolic AI Knowledge Graphs |url=https://www.datanami.com/this-just-in/franz-inc-introduces-allegrograph-cloud-a-managed-service-for-neuro-symbolic-ai-knowledge-graphs/ |access-date=2024-06-06 |website=Datanami |language=en-US}}</ref> |Proprietary (Managed Service) |- | AlloyDB AI<ref>{{Cite web |last=Wiggers |first=Kyle |date=2023-08-29 |title=Google's AlloyDB AI transforms databases to power generative AI apps |url=https://techcrunch.com/2023/08/29/googles-alloydb-ai-transforms-databases-to-power-generative-ai-apps/ |access-date=2026-01-01 |website=TechCrunch |language=en-US}}</ref>|| Proprietary (Managed Service) |- |Apache Cassandra<ref>{{Cite web |date=2023-09-22 |title=5 Hard Problems in Vector Search, and How Cassandra Solves Them |url=https://thenewstack.io/5-hard-problems-in-vector-search-and-how-cassandra-solves-them/ |access-date=2023-09-22 |website=TheNewStack |language=en-US}}</ref><ref>{{Cite web |title=Vector Search quickstart |url=https://cassandra.apache.org/doc/latest/cassandra/vector-search/overview.html |access-date=2023-11-21 }}</ref> |Apache License 2.0 |- |Azure Cosmos DB<ref>{{Cite web |date=26 December 2023 |title=Vector database |url=https://learn.microsoft.com/azure/cosmos-db/vector-database |access-date=2024-01-10 |website=learn.microsoft.com}}</ref> |Proprietary (Managed Service) |- |Chroma<ref>{{Cite web |last=Palazzolo |first=Stephanie |title=Vector database Chroma scored $18 million in seed funding at a $75 million valuation. Here's why its technology is key to helping generative AI startups. |url=https://www.businessinsider.com/vector-database-startup-chroma-raises-seed-funding-generative-artificial-intelligence-2023-4 |access-date=2023-11-16 |website=Business Insider |language=en-US}}</ref><ref>{{Cite web |last=MSV |first=Janakiram |date=2023-07-28 |title=Exploring Chroma: The Open Source Vector Database for LLMs |url=https://thenewstack.io/exploring-chroma-the-open-source-vector-database-for-llms/ |access-date=2023-11-16 |website=The New Stack |language=en-US}}</ref> |Apache License 2.0<ref>{{cite web |title=chroma/LICENSE at main · chroma-core/chroma |url=https://github.com/chroma-core/chroma/blob/main/LICENSE |website=GitHub |language=en}}</ref> |- |ClickHouse<ref>{{Cite news |date=2023-10-26 |title=Can you use ClickHouse for vector search? {{!}} ClickHouse Docs |url=https://clickhouse.com/docs/knowledgebase/vector-search |archive-url=https://web.archive.org/web/20250622222726/https://clickhouse.com/docs/knowledgebase/vector-search |archive-date=2025-06-22 |access-date=2025-07-02 |language=en |url-status=live }}</ref> |Apache License 2.0 |- |Couchbase<ref>{{Cite web |date=2023-08-30 |title=Couchbase aims to boost developer database productivity with Capella IQ AI tool |url=https://venturebeat.com/ai/couchbase-aims-to-boost-developer-database-productivity-with-capella-iq-ai-tool/#h-next-on-the-roadmap-for-couchbase-is-vector-support |website=VentureBeat |language=en-US}}</ref><ref>{{Cite web |date=2023-12-06 |title=Investor Presentation Third Quarter Fiscal 2024 |url=https://investors.couchbase.com/static-files/551e5b96-5307-4119-b225-19cfd8540242 |website=Couchbase Investor Relations |language=en-US}}</ref> |BSL 1.1<ref>{{Cite web |last=Anderson |first=Scott |date=2021-03-26 |title=Couchbase Adopts BSL License |url=https://www.couchbase.com/blog/couchbase-adopts-bsl-license/ |access-date=2024-02-14 |website=The Couchbase Blog |language=en-US}}</ref> |- |CrateDB<ref>{{Cite web |date=16 November 2023 |title=Open Source Vector Database |url=https://cratedb.com/blog/open-source-vector-database |access-date=2024-11-06 |website=CrateDB Blog}}</ref> |Apache License 2.0 |- |DataStax<ref>{{cite news |last1=Sean Michael Kerner |date=18 July 2023 |title=DataStax brings vector database search to multicloud with Astra DB |url=https://venturebeat.com/data-infrastructure/datastax-brings-vector-database-search-to-multicloud-with-astra-db/ |agency=Venture Beat}}</ref> |Proprietary (Managed Service) |- |Elasticsearch<ref>{{cite news |last1=Kerner |first1=Sean |date=23 May 2023 |title=Elasticsearch Relevance Engine brings new vectors to generative AI |url=https://venturebeat.com/ai/elasticsearch-relevance-engine-brings-new-vectors-to-generative-ai/ |access-date=18 November 2023 |work=VentureBeat}}</ref> |Server Side Public License, Elastic License<ref>{{cite web |title=elasticsearch/LICENSE.txt at main · elastic/elasticsearch |url=https://github.com/elastic/elasticsearch/blob/main/LICENSE.txt |website=GitHub |language=en}}</ref> |- |JaguarDB<ref>{{Cite web |title=JaguarDB Homepage |url=http://jaguardb.com/ |access-date=2025-04-12 |website=JaguarDB |language=en-US}}</ref><ref>{{cite web |date=2023-07-03 |title=Vector DBMS |url=https://db-engines.com/de/ranking/vector+dbms |access-date=2025-04-12 |website=db-engines.com |language=en-US }}{{Dead link|date=November 2025 |bot=InternetArchiveBot }}</ref> |Proprietary |- |LanceDB<ref>{{Cite web |date=2024-12-17 |title=LanceDB Homepage |url=https://lancedb.com/ |access-date=2024-12-17 |website=LanceDB |language=en-US}}</ref><ref>{{Cite web |date=2025-09-22 |title=A scalable, elastic database and search solution for 1B+ vectors built on LanceDB and Amazon S3 {{!}} AWS Architecture Blog |url=https://aws.amazon.com/blogs/architecture/a-scalable-elastic-database-and-search-solution-for-1b-vectors-built-on-lancedb-and-amazon-s3/ |access-date=2026-01-01 |website=aws.amazon.com}}</ref> |Apache License 2.0<ref>{{Cite web |title=lancedb/LICENSE at main · lancedb/lancedb |url=https://github.com/lancedb/lancedb?tab=Apache-2.0-1-ov-file |access-date=2024-12-17 |website=GitHub |language=en}}</ref> |- |LlamaIndex<ref>{{Cite web |last=Wiggers |first=Kyle |date=2023-06-06 |title=LlamaIndex adds private data to large language models |url=https://techcrunch.com/2023/06/06/llamaindex-adds-private-data-to-large-language-models/ |access-date=2023-10-29 |website=TechCrunch |language=en-US}}</ref> |MIT License<ref>{{Cite web |title=llama_index/LICENSE at main · run-llama/llama_index |url=https://github.com/run-llama/llama_index/blob/main/LICENSE |access-date=2023-10-29 |website=GitHub |language=en}}</ref> |- |MariaDB<ref>{{Cite web |title=MariaDB Vector |url=https://mariadb.org/projects/mariadb-vector/ |access-date=2024-07-30 |website=MariaDB.org |language=en-US}}</ref><ref>{{Cite web |title=Vector search in old and modern databases |url=https://manticoresearch.com/blog/vector-search-in-databases/ |access-date=2024-07-30 |website=manticoresearch.com |language=en-us}}</ref> |GPL v2<ref>{{Cite web |title=Licensing FAQ |url=https://mariadb.com/kb/en/licensing-faq/ |access-date=2024-07-30 |website=MariaDB KnowledgeBase}}</ref> |- |Marqo<ref>{{Cite web |last=Sawers |first=Paul |date=2023-08-16 |title=Meet Marqo, an open source vector search engine for AI applications |url=https://techcrunch.com/2023/08/16/meet-marqo-an-open-source-vector-search-engine-for-ai-applications/ |access-date=2024-08-20 |website=TechCrunch |language=en-US}}</ref> |Apache License 2.0<ref>{{Citation |title=marqo-ai/marqo |date=2024-08-20 |url=https://github.com/marqo-ai/marqo?tab=Apache-2.0-1-ov-file#readme |access-date=2024-08-20 |publisher=Marqo}}</ref> |- |Meilisearch<ref>{{Cite web |date=2024-10-08 |title=Meilisearch Homepage |url=https://meilisearch.com/ |access-date=2023-10-29 |website=Meilisearch |language=en-US}}</ref><ref>{{Cite web |title=Compare Algolia vs ElasticSearch vs Meilisearch vs Typesense |url=https://typesense.org/typesense-vs-algolia-vs-elasticsearch-vs-meilisearch/ |access-date=2026-01-01 |website=typesense.org |language=en}}</ref> |MIT License<ref>{{Cite web |title=meilisearch/LICENSE at main · meilisearch/meilisearch |url=https://github.com/meilisearch/meilisearch/blob/main/LICENSE |access-date=2024-10-08 |website=GitHub |language=en}}</ref> |- |Milvus<ref>{{Cite web |last=Liao |first=Ingrid Lunden and Rita |date=2022-08-24 |title=Zilliz raises $60M, relocates to SF |url=https://techcrunch.com/2022/08/24/zilliz-the-startup-behind-the-milvus-open-source-vector-database-for-ai-applications-raises-60m-and-relocates-to-sf/ |access-date=2023-10-29 |website=TechCrunch |language=en-US}}</ref> |Apache License 2.0<ref>{{cite web|url=https://github.com/milvus-io/milvus/blob/master/LICENSE|website=GitHub|title=Milvus license}}</ref> |- |MongoDB Atlas<ref>{{Cite web |date=2023-06-22 |title=Introducing Atlas Vector Search: Build Intelligent Applications with Semantic Search and AI Over Any Type of Data |url=https://www.mongodb.com/blog/post/introducing-atlas-vector-search-build-intelligent-applications-semantic-search-ai|website=MongoDB|language=en-US}}</ref> |Server Side Public License (Managed service) |- |MyScaleDB<ref>{{Cite web |last=Jamil |first=Usama |date=2024-05-20 |title=Build an Advanced RAG Application Using MyScaleDB and LlamaIndex |url=https://thenewstack.io/build-an-advanced-rag-application-using-myscaledb-and-llamaindex/ |access-date=2026-01-01 |website=The New Stack |language=en-US}}</ref> |Apache License 2.0 |- |Neo4j<ref> {{Cite web |date=2023-08-22|url=https://itbrief.com.au/story/neo4j-enhances-its-graph-database-with-vector-search|website=itbrief|language=en-AU|title=Neo4j enhances its graph database with vector search}} </ref><ref> {{Cite web |url=https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes|website=neo4j|language=en-US|title=Vector search indexes }}</ref> |GPL v3 (Community Edition)<ref>{{Cite web|url=https://neo4j.com/licensing/|title=Neo4j Licensing}}</ref> |- |ObjectBox<ref>{{cite web|date=2024-07-03|title=Top Fifteen Vector Databases| url=https://db-engines.com/de/ranking/vektor+dbms | access-date=2024-07-03 |website=db-engines.com |language=en-US}}</ref> |Apache License 2.0<ref>{{cite web |url=https://github.com/objectbox/objectbox-java/blob/main/LICENSE.txt|website=github|title=ObjectBox Java license}}</ref> |- |OpenSearch<ref>{{cite web|date=2023-08-02|title=Using OpenSearch as a Vector Database| url=https://opensearch.org/platform/search/vector-database.html | access-date=2024-02-07 |website=OpenSearch.org |language=en-US}}</ref><ref>{{Citation |last1=Pan |first1=James Jie |title=Survey of Vector Database Management Systems |date=2023-10-21 |arxiv=2310.14021 |last2=Wang |first2=Jianguo |last3=Li |first3=Guoliang}}</ref> |Apache License 2.0<ref name="auto">{{cite web |url=https://github.com/opensearch-project/OpenSearch/blob/main/LICENSE.txt|website=github|title=OpenSearch license}}</ref> |- |Oracle Database<ref>{{Cite web |last1=Hook |first1=Doug |last2=Priyadarshi |first2=Ranjan |date=May 2, 2024 |title=Oracle Announces General Availability of AI Vector Search in Oracle Database 23ai |url=https://blogs.oracle.com/database/post/oracle-announces-general-availability-of-ai-vector-search-in-oracle-database-23ai |access-date=July 9, 2024 |website=oracle}}</ref> |Proprietary (Managed Service or License) |- |Pinecone<ref>{{Cite web |date=2023-07-14 |title=Pinecone leads 'explosion' in vector databases for generative AI |url=https://venturebeat.com/ai/pinecone-leads-explosion-in-vector-databases-for-generative-ai/ |access-date=2023-10-29 |website=VentureBeat |language=en-US}}</ref> |Proprietary (Managed Service) |- |Pixeltable (Incremental Embedding)<ref>{{Cite web |title=Automatic incremental embedding index |url=https://www.pixeltable.com/blog/pixeltable-incremental-embedding-indexes |access-date=2025-07-04 |website=Pixeltable |date=24 April 2025 |language=en-US}}</ref> |Apache License 2.0<ref name="auto"/> |- |Postgres with pgvector<ref>{{Cite web |title=pgvector |url=https://github.com/pgvector/pgvector |access-date=2023-11-27 |website=GitHub |language=en-US}}</ref> | PostgreSQL License<ref>{{Cite web |title=pgvector/License |url=https://github.com/pgvector/pgvector/blob/master/LICENSE |access-date=2023-11-27 |website=GitHub |language=en-US}}</ref> |- |Qdrant<ref>{{Cite web |last=Sawers |first=Paul |date=2023-04-19 |title=Qdrant, an open-source vector database startup, wants to help AI developers leverage unstructured data |url=https://techcrunch.com/2023/04/19/qdrant-an-open-source-vector-database-startup-wants-to-help-ai-developers-leverage-unstructured-data/ |access-date=2023-10-29 |website=TechCrunch |language=en-US}}</ref> |Apache License 2.0<ref>{{Cite web |title=qdrant/LICENSE at master · qdrant/qdrant |url=https://github.com/qdrant/qdrant/blob/master/LICENSE |access-date=2023-10-29 |website=GitHub |language=en}}</ref> |- |Redis Stack<ref>{{Cite web |title=Using Redis as a Vector Database with OpenAI {{!}} OpenAI Cookbook |url=https://cookbook.openai.com/examples/vector_databases/redis/getting-started-with-redis-and-openai |access-date=2024-02-10 |website=cookbook.openai.com |language=en}}</ref><ref>{{Cite web |title=Redis as a vector database quick start guide |url=https://redis.io/docs/get-started/vector-database/ |access-date=2024-01-31 |website=Redis |language=en |archive-date=2024-01-31 |archive-url=https://web.archive.org/web/20240131205622/https://redis.io/docs/get-started/vector-database/ |url-status=dead }}</ref> |[https://redis.io/docs/about/license/ Redis Source Available License] {{Webarchive|url=https://web.archive.org/web/20240131205042/https://redis.io/docs/about/license/ |date=2024-01-31 }}<ref>{{Cite web |title=Search and query |url=https://redis.io/docs/interact/search-and-query/ |access-date=2024-02-10 |website=Redis |language=en}}</ref> |- |ScyllaDB<ref>{{Cite web |date=2026-02-05 |title=Open source USearch library jumpstarts ScyllaDB vector search |url=https://thenewstack.io/open-source-usearch-library-jumpstarts-scylladb-vector-search/ |access-date=2026-02-23 |website=TheNewStack |language=en-US}}</ref><ref>{{Cite web |date=2026-01-20 |title=ScyllaDB adds vector search to managed database platform |url=https://www.techtarget.com/searchdatamanagement/news/366637573/ScyllaDB-adds-vector-search-to-managed-database-platform |access-date=2026-02-23 |website=TechTarget |language=en-US}}</ref> |Proprietary Source Available |-
|- |Snowflake<ref>{{Cite web |date=2024-05-17 |title=Vector data type and vector similarity functions — General Availability |url=https://docs.snowflake.com/en/release-notes/2024/other/2024-05-16-vector-data-type-ga |access-date=2024-05-17 |website=Snowflake |language=en-US}}</ref> |Proprietary (Managed Service) |- |SurrealDB<ref>{{Cite web |last=Wiggers |first=Kyle |date=2023-01-04 |title=SurrealDB raises $6M for its database-as-a-service offering |url=https://techcrunch.com/2023/01/04/surrealdb-raises-6m-startup-funding-database-as-a-service/ |access-date=2024-01-19 |website=TechCrunch |language=en-US}}</ref> |BSL 1.1<ref>{{Cite web |title=SurrealDB {{!}} License FAQs {{!}} The ultimate multi-model database |url=https://surrealdb.com/license |access-date=2024-02-14 |website=SurrealDB |language=en}}</ref> |- |TiDB<ref>{{cite web | title=TiDB Vector Search |url=https://docs.pingcap.com/tidb/stable/vector-search-overview/|website=docs.pingcap.com }}</ref> |Apache License 2.0<ref>{{citation | title=tidb/LICENSE at master · pingcap/tidb |url=https://github.com/pingcap/tidb/blob/master/LICENSE}}</ref> |- |Typesense<ref>{{Cite web |last=Martinez |first=Miguel |date=2024-06-20 |title=Typesense Homepage |url=https://typesense.org/ |access-date=2024-06-20 |website=Typesense |language=en-US}}</ref> |GPL v3 (Community Edition)<ref>{{cite web |url=https://github.com/typesense/typesense/blob/main/LICENSE.txt|title=Typesense licensing|website=GitHub }}</ref> |- |Vespa<ref>{{Cite web |title=Creating a Vespa Vector Database |url=https://www.capitalone.com/tech/ai/vector-database-intro/ |access-date=2026-01-01 |website=Capital One |language=en}}</ref> |Apache License 2.0<ref>{{cite web |url=https://github.com/vespa-engine/vespa/blob/master/LICENSE|title=vespa/LICENSE at master · vespa-engine/vespa|website=GitHub|language=en}}</ref> |- |Weaviate{{Citation needed|date=February 2026}} |BSD 3-Clause<ref>{{Cite web |title=weaviate/LICENSE at master · weaviate/weaviate |url=https://github.com/weaviate/weaviate/blob/master/LICENSE |access-date=2023-10-29 |website=GitHub |language=en}}</ref> |- |YDB<ref>{{Cite web |title=Langchain YDB |url=https://python.langchain.com/docs/integrations/vectorstores/ydb/ |access-date=2025-07-26 |website=Langchain |language=en-US}}</ref><ref>{{Cite web |title=YDB - Vector Search |url=https://ydb.tech/docs/en/concepts/vector_search |website=ydb.tech |access-date=2025-07-26 |language=en-US}}</ref> |Apache License 2.0<ref>{{Cite web |title=ydb/LICENSE at master · ydb-platform/ydb |url=https://github.com/ydb-platform/ydb/blob/main/LICENSE |access-date=2025-07-26 |website=GitHub |language=en}}</ref> |}
== See also == * {{annotated link|Curse of dimensionality}} * {{annotated link|Graph database}} * {{annotated link|Machine learning}} * {{annotated link|Nearest neighbor search}} * {{annotated link|Recommender system}}
== References == {{Reflist}} ==External links== * {{cite web |last=Sawers |first=Paul |title=Why vector databases are having a moment as the AI hype cycle peaks |website=TechCrunch |date=2024-04-20 |url=https://techcrunch.com/2024/04/20/why-vector-databases-are-having-a-moment-as-the-ai-hype-cycle-peaks/ |access-date=2024-04-23}}
Category:Vector databases Category:Machine learning