Top Vector Databases (September 2026)

1

Rockset

Top pick

rockset.com

Best for Engineering teams building real-time search apps·Free trial

Rockset is a real-time analytics database that supports vector search alongside SQL-based queries on structured, semi-structured, and time-series data. It combines vector similarity search with metadata filtering and aggregations, allowing developers to build search and recommendation features without maintaining a separate vector store. Data can be ingested continuously from sources like Kafka, DynamoDB, and MongoDB, with indexing designed to keep results current. It suits engineering teams building real-time applications, such as personalization or fraud detection, that need to combine vector search with traditional analytical queries in one system.

• Vector similarity search
• Real-time data ingestion
• SQL-based querying

2

Typesense

typesense.org

Best for Developers building search or RAG applications·Free trial

Typesense is an open-source search engine that supports keyword, vector, and hybrid search for building fast, typo-tolerant search experiences. It offers built-in vector search for semantic and AI-powered applications, alongside traditional filtering, faceting, and geo search. Typesense can be self-hosted or used via a managed cloud offering, and provides client libraries for multiple programming languages. It suits development teams building site search, product discovery, or retrieval-augmented generation features who want an open-source alternative to proprietary search and vector database platforms, with straightforward setup and configuration.

• Hybrid vector and keyword search
• Typo-tolerant full-text search
• Self-hosted or managed cloud

3

Chroma

Free plan

trychroma.com

Best for Developers building LLM and RAG applications

Chroma is an open-source embedding database designed for building applications with large language models. It stores vector embeddings alongside metadata and documents, supporting semantic search, filtering, and retrieval-augmented generation workflows. Chroma can run locally, embedded in an application, or as a standalone server, and integrates with common LLM and embedding frameworks. It is aimed at developers who need a lightweight, developer-friendly vector store for prototyping or production AI applications without managing complex infrastructure.

• Embedding storage & retrieval
• Semantic search & filtering
• Local or server deployment

4

Elasticsearch

Free plan

elastic.co

Best for Teams extending existing search infrastructure

Elasticsearch is a distributed search and analytics engine developed by Elastic that has added vector search capabilities alongside its traditional full-text search functionality. It supports dense vector fields and approximate nearest neighbor search, enabling hybrid queries that combine keyword and semantic search. Built on the broader Elastic Stack, it integrates with tools for ingestion, visualization, and monitoring. It suits engineering teams already using Elasticsearch for search or logging who want to extend existing infrastructure with embedding-based retrieval rather than adopting a separate dedicated vector database.

• Hybrid keyword and vector search
• Approximate nearest neighbor search
• Elastic Stack integration

5

LanceDB

Free plan

lancedb.com

Best for AI developers building retrieval and search apps

LanceDB is an open-source vector database built on the Lance columnar storage format, designed for storing, indexing, and querying embeddings alongside metadata and multimodal data such as images, text, and audio. It can run embedded within an application or deployed as a serverless service, supporting retrieval-augmented generation, semantic search, and recommendation workloads. LanceDB integrates with common machine learning and data tooling, offering SQL-like filtering combined with vector similarity search. It suits developers and data teams building AI applications who need a lightweight, scalable option without managing separate infrastructure for structured and unstructured data.

• Vector similarity search
• Multimodal data storage
• Embedded or serverless deployment

6

OpenSearch

Free plan

opensearch.org

Best for Engineering teams needing open-source hybrid search

OpenSearch is an open-source search and analytics suite derived from Elasticsearch, offering full-text search, log analytics, and vector search capabilities through its k-NN plugin. It supports approximate nearest neighbor search for embeddings alongside traditional keyword and structured queries, enabling hybrid search use cases. OpenSearch can be self-hosted or run as a managed service through Amazon OpenSearch Service. It suits engineering teams building search, observability, or AI-powered retrieval applications who want an open-source foundation with community-driven development rather than a proprietary vendor lock-in.

• Approximate k-NN vector search
• Full-text and structured search
• Self-hosted or managed deployment

7

Zilliz Cloud

zilliz.com

Best for Teams building AI apps needing managed vector search·Free trial

Zilliz Cloud is a fully managed vector database service built on the open-source Milvus project. It enables storage, indexing, and similarity search of high-dimensional embeddings for use cases such as semantic search, recommendation systems, and retrieval-augmented generation. The platform handles infrastructure provisioning, scaling, and maintenance, offering APIs and SDKs for integration with machine learning and AI application pipelines. It supports hybrid search combining vector and metadata filtering. Zilliz Cloud suits engineering and data science teams building AI-driven applications who prefer a managed alternative to self-hosting Milvus.

• Milvus-based managed service
• Hybrid vector and metadata search
• Scalable infrastructure management

8

Vespa

Free plan

vespa.ai

Best for Engineering teams building large-scale search or recommendation system

Vespa is an open-source platform for building applications that combine vector search, lexical search, and structured data filtering with real-time machine learning inference. It supports approximate nearest neighbor search alongside traditional ranking, enabling hybrid retrieval at scale. Originally developed at Yahoo, it is suited for engineering teams building search, recommendation, or personalization systems that require low-latency serving over large datasets, and offers both self-hosted deployment and a managed cloud service.

• Hybrid vector and lexical search
• Real-time ML model inference
• Scalable distributed serving

9

SingleStore

singlestore.com

Best for Engineering teams building AI apps needing SQL + vectors·Free trial

SingleStore is a distributed SQL database that supports vector storage and similarity search alongside relational, JSON, and full-text data. It allows teams to combine vector search with structured queries in a single system, which can be useful for building AI applications that require real-time analytics alongside semantic search. SingleStore is designed to handle high-throughput ingestion and querying at scale. It suits engineering teams building AI-powered applications who prefer to consolidate operational and vector data rather than maintaining a separate dedicated vector database.

• Vector similarity search
• Distributed SQL engine
• Real-time data ingestion