Vector Databases (September 2026)

1

SingleStore

Top pick

singlestore.com

Best for Engineering teams building real-time AI applications·Free trial

SingleStore is a distributed SQL database that supports vector storage and similarity search alongside relational and full-text data. It allows developers to combine vector search with SQL queries, joins, and transactional workloads in a single system, avoiding the need for a separate vector database. SingleStore is used for building AI applications such as retrieval-augmented generation, recommendation systems, and semantic search where vector operations need to coexist with structured business data. It suits engineering teams building real-time, data-intensive applications that require both analytical and AI-driven query capabilities.

• Vector similarity search
• Combined SQL and vector queries
• Real-time data ingestion

2

LanceDB

Free plan

lancedb.com

Best for Developers building AI and RAG applications

LanceDB is an open-source vector database built for AI applications that require search over embeddings alongside structured metadata. It is based on the Lance columnar data format, designed for fast random access and efficient storage of vectors, images, text, and other multimodal data. LanceDB supports hybrid search combining vector similarity with filtering, and can run embedded within an application or as a managed service. It suits engineering teams building retrieval-augmented generation systems, recommendation engines, or multimodal search features who want a lightweight, developer-friendly database option.

• Vector similarity search
• Hybrid search with filtering
• Multimodal data storage

3

Marqo

marqo.ai

Best for Engineering teams building semantic or multimodal search

Marqo is an open-source vector search engine that combines embedding generation, storage, and retrieval in a single system. It supports text and image inputs, enabling multimodal semantic search without requiring separate embedding pipelines. Marqo can be self-hosted or used via a managed cloud service, and integrates with applications through a REST API. It is suited for engineering teams building search, recommendation, or retrieval-augmented generation features who want an end-to-end vector search solution rather than assembling embedding and indexing components separately.

• Built-in embedding generation
• Text and image search
• REST API integration

4

Vespa

Free plan

vespa.ai

Best for Engineering teams building large-scale search and recommendation syste

Vespa is an open-source platform for building applications that combine vector search, text search, and structured data queries with real-time inference. It supports large-scale similarity search alongside traditional filtering and ranking, allowing hybrid retrieval strategies within a single system. Vespa handles indexing, serving, and machine-learned model execution at query time, making it suited to search and recommendation workloads that require low-latency responses over large datasets. It is typically used by engineering teams building custom search, recommendation, or personalization systems that need scalability and flexible ranking logic.

• Hybrid vector and text search
• Real-time inference at query time
• Structured data filtering and ranking

5

Qdrant

Free plan

qdrant.tech

Best for Developers building semantic search or RAG applications

Qdrant is an open-source vector database and similarity search engine used to store, index, and query high-dimensional embeddings. It supports filtering alongside vector search, payload storage, and various distance metrics, and can be self-hosted or used via a managed cloud service. Client libraries are available for multiple programming languages. Qdrant is suited for developers and engineering teams building applications such as semantic search, recommendation systems, and retrieval-augmented generation, ranging from small projects to larger production deployments requiring scalable vector indexing.

• Vector similarity search
• Payload filtering
• Self-hosted or managed cloud

6

Typesense

Free plan

typesense.org

Best for Developers building hybrid keyword and vector search

Typesense is an open-source search engine that supports vector search alongside traditional keyword and filtering capabilities. It combines full-text, faceted, and approximate nearest-neighbor vector search in a single API, allowing hybrid search use cases such as semantic search combined with typo tolerance and filtering. Typesense can be self-hosted or run via a managed cloud offering. It suits engineering teams building search or recommendation features into applications who want an alternative to Elasticsearch with simpler setup and built-in vector capabilities.

• Hybrid keyword and vector search
• Typo tolerance and faceting
• Self-hosted or managed cloud

7

Chroma

Free plan

trychroma.com

Best for Developers building LLM and RAG applications

Chroma is an open-source embedding database used to store, query, and retrieve vector embeddings for AI applications. It supports similarity search, metadata filtering, and integrates with common embedding models and frameworks used in retrieval-augmented generation pipelines. Chroma can run locally for development or be deployed for production workloads, offering client libraries for Python and JavaScript. It is suited to developers and teams building LLM-powered applications, chatbots, or semantic search features who need a lightweight, embeddable vector store without managing complex infrastructure.

• Embedding storage and retrieval
• Similarity search
• Metadata filtering