Open-Source Vector Databases

Qdrant is a self-hosted vector database for developers building semantic search, retrieval-augmented generation (RAG), recommendations and AI agent memory. It stores embeddings alongside JSON metadata, so applications can find similar content while restricting results by attributes such as location, text or numeric ranges. The Rust engine is open source under Apache 2.0 and runs locally in Docker or on your own servers.

chromem-go is a vector database that runs inside your Go application, so developers can add semantic search or retrieval augmented generation (RAG) without maintaining a separate database server. It stores text alongside embeddings and retrieves related documents for use in LLM answers. Its focus is ordinary application workloads rather than collections containing millions of documents.

Marqo Open Source is a self-hosted search engine for developers building semantic document search, image search, or retrieval for AI applications. It handles embedding generation alongside storage and retrieval, so applications can submit documents without maintaining a separate embedding service. The open-source project is deprecated and no longer receives updates.

Infinity is a self-hosted database for developers building search and retrieval-augmented generation (RAG) into LLM applications. It combines embedding search with full-text search and structured filters, so an application can retrieve relevant records through both meaning and exact terms.

SurrealDB is a self-hosted database for developers building AI agents, knowledge graphs and applications that need several kinds of data together. It stores documents, relationships, vectors and time-series data in one engine, so an application's records and its AI retrieval layer can share the same database.

Chroma DB is an open-source search database for developers building AI apps and agents that need to retrieve information from their own data. You can run it locally or host it on your own infrastructure under the Apache 2.0 license. Chroma Cloud is a separate hosted service for managed, serverless search.

Typesense combines typo-tolerant site search with vector and semantic search in a self-hosted engine. It's for developers building searchable apps, product catalogs or AI search over their own data. The C++ engine uses an in-memory architecture for low-latency results as users type.

LanceDB is an open source vector database for developers building AI retrieval applications and teams working with training datasets. Its embedded library runs locally or in your own cloud under the Apache 2.0 license. Cloud and Enterprise offerings provide managed infrastructure for production workloads.

pgvectorscale adds an index for large embedding datasets to PostgreSQL databases that use pgvector. It's for application developers and database administrators who want to keep AI similarity search in their existing database, with more control over search speed and storage use.

txtai is a Python framework for developers building search applications, chat with their data, and AI agents on their own hardware or servers. Its embeddings database combines sparse and dense vector search with graphs and relational data, so the same system can find related content and supply context to language models. It's open source under Apache 2.0.

OpenSearch combines document and enterprise search with vector retrieval for AI applications. It's for developers building search into their products and teams analyzing application logs, infrastructure performance, or security events. The suite uses the Apache 2.0 license throughout, including its data ingestion and dashboard components.

Meilisearch combines keyword search and AI retrieval in a search engine you can host on your own server. It's for developers building search into websites, applications, product catalogs, or internal data tools. Meilisearch Cloud provides a separate, fully managed hosted service.

sqlite-vec adds vector storage and similarity search to SQLite, so developers can keep embeddings alongside application data in a local database. It's for applications that need to find related items by vector distance without running a separate vector database server. The extension is small, written in C and has no dependencies.

Weaviate is a self-hosted vector database for developers building search applications, RAG systems, recommendation engines, and chatbots. It stores data objects alongside their vector embeddings, so applications can search by meaning and filter results using structured data. You can run the database locally with Docker, deploy it on Kubernetes, or use the hosted Weaviate Cloud service.

Milvus is an open-source vector database for developers building RAG applications, image search and recommendation systems. It stores embeddings alongside metadata so applications can retrieve related text, images or multimodal data. You can run it on your own hardware, from a laptop prototype to a distributed production cluster.

ParadeDB adds full-text, vector and hybrid search to the Postgres database that holds your application data. It's for developers who need search and analytics without maintaining a separate Elasticsearch cluster or synchronizing a second copy of their data. It runs as the pg_search extension in self-managed Postgres, including local Docker deployments.

pgvector adds vector storage and similarity search to Postgres, so developers can keep embeddings alongside application records in a self-hosted database. It suits applications that need to find similar items while retaining SQL queries, joins and transactional guarantees. It runs on Linux, macOS and Windows, with Docker also supported.

Vespa is a self-hosted AI search platform for developers building search, RAG, and recommendation systems over large, changing datasets. It combines retrieval with machine-learned ranking, so an application can find candidate results and evaluate their relevance in the same platform. The code is open source under Apache 2.0. You can run it on your own servers or use the managed Vespa Cloud service, where applications run in the cloud.