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Choose an Azure service for vector search

Vector search is a way to find information stored in a database in the shape of vectors. Vectors are groups of numbers that represent features or characteristics of media, such as text or images. They capture semantic relationships within the information, which enables similarity search beyond exact keyword matching.

Azure provides multiple ways to store and search vectorized data. This article helps you choose the right Azure vector search service for your applications.

This article compares the following services based on their vector search capabilities:

To compare the system requirements for each service, see Choose a candidate service and Capability matrix.

Choose a candidate service

This section helps you select the best service or services for your needs. To narrow the choices, start by considering the system requirements.

Key requirements

Flowchart that helps you choose the right Azure vector search service.

To decide whether to use a traditional database solution or AI Search, consider your requirements and whether you can perform live or real-time vector searches on your data. If you frequently change values in vectorized fields, and if those changes need to be searchable in real time or near real time, a traditional relational or NoSQL database is the best fit for your scenario. Similarly, your existing database might be the best way to meet your performance target. However, if your workload doesn't require real-time or near-real-time vector searchability, and you can manage an index of vectors, you can use AI Search.

If you choose a traditional database solution, choose a database service based on your team's skill set and your existing databases. If you already use a database service, such as Azure Cosmos DB for NoSQL, that service might be the easiest solution for your scenario.

  • Azure Cosmos DB for NoSQL is a good fit if you want to keep operational data and vector search in the same system and if you need full-text scoring, hybrid search, or built-in reranking.

  • AI Search might be a good choice if your workload requires first-class hybrid search and semantic ranking.

  • Azure Database for PostgreSQL supports horizontal scaling by using elastic clusters, a managed offering of the open-source Citus extension that supports horizontal sharding. This capability distributes vector data across multiple nodes, which can be useful for large vector datasets.

  • Consider Azure Managed Redis when you need ultra-low-latency, in-memory vector search or when Redis is already deployed for caching or session management.

Each database service has unique capabilities and limitations for vector search. Check that your database type has the required functionality.

New services and extra database instances can increase cost and complexity. To reduce overhead, you can continue to use your existing design. Vector search in your current databases might be more cost effective than a dedicated vector search service. However, some advanced search features aren't available by default in traditional databases. For example, if you need reranking or hybrid search, you can implement these capabilities by using code, such as Transact-SQL (T-SQL).

Capability matrix

The tables in this section summarize Azure vector search service capabilities. Compare the available services with your requirements. Some services are a better fit for specific scenarios, so consider the trade-offs shown in each table.

If you're working in Microsoft Fabric, you can use Real-Time Intelligence for vector similarity search (VSS) by using an eventhouse as a vector database. For more information, see the Fabric documentation.

Basic features

Native support for vector data types, approximate nearest neighbor (ANN) vector indexes, vector dimension limits, multiple vector fields, and multiple vector indexes varies across services. Your workload might require one or more of these features.

The following table shows the vector capabilities of each Azure service.

Capability Azure Cosmos DB for NoSQL Azure DocumentDB Azure Database for PostgreSQL Azure Managed Redis AI Search SQL Database
Built-in vector search Yes Yes1 Yes2 Yes3 Yes4 Yes
Vector data type Yes Yes Yes Yes Yes Yes5
Dimension limits6 5057 or 4,096 16,0008, 4,000, or 2,000, depending on the configuration 16,0009 or 2,000 32,768 4,09610 1,998 11
Multiple vector fields Yes No Yes Yes Yes Yes
Multiple vector indexes Yes No Yes Yes Yes Yes
  1. Azure DocumentDB supports vector search on embeddings.
  2. pgvector, an extension of PostgreSQL, supports vector search. The pg_diskann extension offers DiskANN-based vector indexing for efficient ANN search at scale.
  3. The RediSearch module in Azure Managed Redis provides vector search.
  4. AI Search supports vectors.
  5. SQL Database supports a vector data type.
  6. OpenAI embedding models include 1,536 dimensions for text-embedding-ada-002 and for text-embedding-3-small, and 3,072 dimensions for text-embedding-3-large. Azure Vision multimodal embedding models have 1,024 dimensions for both image and text.
  7. Vectors indexed by using the flat index type can have up to 505 dimensions. Vectors indexed by using the quantizedFlat or DiskANN index type can have up to 4,096 dimensions.
  8. You can index vectors up to 16,000 dimensions by using DiskANN and product quantization. Hierarchical navigable small world (HNSW) or IVFFlat with half-precision supports vector indexes of up to 4,000 dimensions. Without compression, the default maximum vector dimension for indexing is 2,000. For more information, see the vector dimensions for Azure DocumentDB.
  9. Vectors can have up to 16,000 dimensions. However, indexing by using IVFFlat and HNSW algorithms supports vectors with up to 2,000 dimensions.
  10. AI Search supports Matryoshka Representation Learning-based dimension truncation. Text-embedding-3 models can reduce vector dimensions. For example, you can use 256 or 512 dimensions.
  11. SQL Database supports a native vector data type with up to 1,998 dimensions.

Search methods

Workloads often need to combine vector search with full-text search or hybrid search. Hybrid search is a full-text search or semantic search combined with a vector search. When combined, hybrid search and reranking achieve high accuracy for workloads. You can manually implement hybrid search and reranking by using your own code, or you can consider how your vector store supports this workload requirement.

Search method Azure Cosmos DB for NoSQL Azure DocumentDB Azure Database for PostgreSQL Azure Managed Redis AI Search SQL Database
Full-text search Yes12 Yes2 Yes3 Yes4 Yes Yes5
Hybrid search Yes6 Yes7 Yes8 Yes9 Yes10 Yes11
Built-in reranking Yes No No No Yes1 No
  1. Semantic ranking reranks results of full-text and vector searches.
  2. Azure DocumentDB supports search and query by using text indexes.
  3. PostgreSQL supports full-text search.
  4. Azure Managed Redis supports full-text search by using the RediSearch module, including text tokenization, stemming, and ranking.
  5. SQL Server supports full-text search.
  6. Azure Cosmos DB for NoSQL supports hybrid search.
  7. Azure DocumentDB natively supports hybrid search that combines full-text and vector search with reciprocal rank fusion.
  8. Hybrid search isn't built in, but sample code is available.
  9. Azure Managed Redis supports hybrid search by using VSS combined with attribute filtering on text, numeric, tag, and geo fields.
  10. Hybrid search, which combines full-text search, vector search, and semantic ranking, is a feature in AI Search.
  11. An example of hybrid search for SQL Database and SQL Server is available.
  12. Azure Cosmos DB for NoSQL supports full-text search and full-text scoring.

Vector data indexing algorithms

Vector data indexing is the ability to efficiently store and retrieve vectors. Indexing influences the speed and accuracy of similarity searches and nearest neighbor queries on data sources.

Indexes typically use either an exhaustive k-nearest neighbor (Ek-NN) algorithm or an ANN algorithm. Ek-NN performs an exhaustive search on all data points and returns the accurate k nearest neighbors. When searching a small amount of data, Ek-NN works in milliseconds. For larger datasets, you might experience latency.

DiskANN, HNSW, and IVFFlat are ANN algorithm indexes. Selecting the appropriate indexing strategy requires careful consideration of various factors such as the nature of the dataset, the specific requirements of the queries, and the available resources. DiskANN can adapt to change in the dataset and save computational resources. HNSW excels in systems that require fast query responses and can adapt to changes in the dataset. IVFFlat is effective in environments where hardware resources are limited or query volumes aren't high.

The following table shows the available vector data indexing types.

Indexing approach Azure Cosmos DB for NoSQL Azure DocumentDB Azure Database for PostgreSQL Azure Managed Redis AI Search SQL Database
DiskANN Yes Yes1 Yes2 No No Yes3
Ek-NN Yes Yes Yes Yes4 Yes Yes
HNSW No Yes1 Yes Yes5 Yes No
IVFFlat No Yes Yes No No No
Other Flat, quantizedFlat6 Vector field limitation,7
vector index limitation8		 - - Scalar quantization, binary quantization9 -
  1. For more information, see Integrated vector store in Azure DocumentDB.
  2. For more information, see DiskANN for Azure Database for PostgreSQL.
  3. Native DiskANN vector indexing is in preview. For more information, see Vector search and vector indexes in the SQL Database Engine.
  4. Azure Managed Redis supports Ek-NN search by using the FLAT index type for brute-force search.
  5. Azure Managed Redis supports HNSW for ANN search. For more information, see VSS.
  6. For more information, see Vector indexing policies.
  7. Indexing applies to only one vector per path.
  8. You can create only one index per vector path.
  9. AI Search supports scalar and binary quantization to reduce vector size in a search index.

Similarity and distance calculation capabilities

Vector search supports cosine similarity, dot product, and Euclidean distance calculation methods. Use these methods to calculate the similarity or distance between two vectors.

Preliminary data analysis uses metrics and Euclidean distances so that you can extract a variety of data structure insights. Text classification generally performs better under Euclidean distances. Retrieval of the most similar texts to a given text typically functions better with cosine similarity.

Azure OpenAI embeddings rely on cosine similarity to compute similarity between documents and a query.

Built-in vector comparison calculation Azure Cosmos DB for NoSQL Azure DocumentDB Azure Database for PostgreSQL Azure Managed Redis AI Search SQL Database
Cosine similarity Yes1 Yes Yes Yes2 Yes Yes3
Euclidean distance Yes1 Yes Yes Yes2 Yes Yes3
Dot product Yes1 Yes Yes Yes2 Yes Yes3
  1. For more information, see the vector distance calculation for Azure Cosmos DB for NoSQL.
  2. Azure Managed Redis supports cosine similarity, Euclidean distance, and inner product distance metrics. For more information, see VSS.
  3. For more information, see the distance calculation examples for SQL Database and SQL Server.

Integration with Azure OpenAI and other components

You can link vector search to other Microsoft components. For example, Azure OpenAI helps you create vectors for your data and input queries for VSS.

Capability Azure Cosmos DB for NoSQL Azure DocumentDB Azure Database for PostgreSQL Azure Managed Redis AI Search SQL Database
Foundry IQ integration No No No No Yes No
Foundry Agent Service integration Yes1 No Yes2 Yes2 Yes3 Yes2
Integrated Azure OpenAI embedding generation No No Yes4 No Yes5 Yes6
Semantic Kernel integration Yes7 Yes8 Yes9 Yes10 Yes11 Yes12
  1. Foundry Agent Service integration is provided through agent state storage.
  2. Foundry Agent Service integration is provided through data, vector search, or tool access.
  3. Foundry Agent Service integration is provided through knowledge retrieval and vector search.
  4. The Azure AI extension is available.
  5. AI Search includes a skill that vectorizes text chunks.
  6. You can create a stored procedure for your embedding model deployment.
  7. This service is supported as a memory connector and a vector database connector. For more information, see the C# documentation and the Python documentation.
  8. This service is supported as a vector database connector. For more information, see the C# documentation and the Python documentation.
  9. This service is supported as a memory connector and a vector database connector. For more information, see the C# documentation.
  10. This service is supported as a vector database connector. For more information, see Using the Redis connector.
  11. This service is supported as a memory connector and a vector database connector. For more information, see the C# documentation and the Python documentation.
  12. This service is supported as a memory connector.

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