I am looking for a vector database that is open source and does not require a cloud subscription.

Several robust, fully open-source vector databases and libraries run entirely on your local machine, on-premise, or via Docker with zero cloud subscription or vendor lock-in required.

Here are the best options broken down by how you plan to use them:

• ChromaDB — An AI-native ChromaDB GitHub Repository that can run entirely in-process (embedded directly inside your Python or JavaScript application) or spun up as a local lightweight server. It requires zero infrastructure or Docker setup for local usage and automatically persists data to a local directory.
• FAISS (Facebook AI Similarity Search) — A high-performance library developed by Meta rather than a standalone database server. It is ideal if you want to perform lightning-fast similarity search and clustering entirely in memory or persisted to a local disk file with absolute control and zero external dependencies.
• pgvector (PostgreSQL Extension) — Turns a standard, self-hosted PostgreSQL instance into a powerful vector database. If you already use Postgres for relational data, adding the pgvector GitHub Repository lets you store and query high-dimensional vectors right alongside your regular tables using standard SQL.
• Qdrant — Written in Rust, Qdrant is an open-source vector search engine that provides a comfortable local deployment via Docker. It features robust filtering capabilities and a generous open-source license without forcing you onto their managed cloud.
• Milvus Lite / Self-Hosted Milvus — Milvus is a heavy-duty distributed vector database, but it offers a local Milvus Lite python package that runs in-memory/on-disk for development, as well as full docker-compose deployment files to run the entire scalable stack on your own hardware.

If you tell me:

• The programming language or framework you're using (Python, Node.js, Go, etc.)
• The scale of your data (thousands of vectors vs. millions)
• Whether you want it embedded in your app or running as a local Docker container

I can help you pick the ideal match and write a quick setup snippet.

Yes. If your requirements are open source + self-hosted + no cloud subscription, you have several good choices.

My top picks

• Qdrant — My first recommendation. It is open source, can run entirely on your own machine/server, and supports dense, sparse, and multivector search, metadata filtering, and hybrid search. You can run it with Docker and persist the database to a local directory.
• Weaviate — A more feature-rich AI database. It supports semantic and hybrid search, RAG-oriented workflows, structured filtering, and can be deployed locally with Docker or Kubernetes without using Weaviate Cloud.

Quick comparison

What I'd choose

If you're building something like a local RAG application, document search system, or AI assistant, I'd choose Qdrant. It's relatively lightweight, has a straightforward API, and you aren't locked into a hosted service. Qdrant's local setup is essentially:

docker run -p 6333:6333 -p 6334:6334 \
  -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
  qdrant/qdrant

That gives you a vector database running on your own machine, with data persisted locally.

If you tell me what you're building (e.g. RAG, semantic search, image search, millions of documents, Python app, etc.) and roughly how many vectors you expect, I can narrow this down to the best 1–2 options.

Here are top web results for exploring this topic:

Instaclustr·https://www.instaclustr.com Top 10 open source vector databases - NetApp Instaclustr Faiss is an open source library developed by Meta for similarity search and clustering of dense vectors. It is written primarily in C++ with Python and NumPy wrappers, and includes algorithms for exac Redis·https://redis.io Comparing the best open source vector databases (2026) - Redis Comparing the best open source vector databases. July 01, 2026 9 minute read. James Tessier. Summarize with AI. Open source vector databases come in two flavors: specialized tools that handle vectors Reddit·https://www.reddit.com how can I build in vector search solution without a cloud database?You can use Redis Open Source 8. It was released yesterday. It can be run on-premise. It is persistent and it is one of the fastest and most scalable vector databases out there today. It also supports Medium·https://medium.com A Broke Bch's Guide to Tech Start-up: Choosing Vector Database If you have worked with graph databases or knowledge graphs before you have either used or heard of Neo4j. Neo4j added support for vector fields as a first class citizen to its nodes and vector search ZenML·https://www.zenml.io We Tried and Tested 10 Best Vector Databases for RAG Pipelines Qdrant is an open-source vector search engine written in Rust. It's purpose-built for RAG applications where speed and memory safety are paramount. Features. Filter vectors using JSON payload fields s Qdrant·https://qdrant.tech Qdrant - Vector Search Engine Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API. Facebook·https://www.facebook.com What are cheap or open source cloud -based vector databases?Im using free one, faiss. So far so good. Have tried multiple|qdrant, but i still stick to pinecone. AIMultiple·https://aimultiple.com**Vector Database Benchmark: 7 Open-Source Engines for RAG The write throughput here is the achieved rate against a 150/s target under concurrent reads, so the fast engines are capped near 150 rather than showing their peak. Read this as single-row, high-freq Atlan·https://atlan.com Top Vector Databases for Enterprise AI: 2026 Comparison - Atlan Serverless tier eliminates index sizing guesswork; scales to billions of vectors automatically; Sub-100ms latency at scale is well-documented in independent benchmarks; Enterprise security: SOC 2 Type zilliz.com·https://zilliz.com/blog/top-5-open-source-vector-search-engines Top 5 Open Source Vector Databases in 2025 - Zilliz blog Vector search, also known as vector similarity search, has quickly evolved from an experimental technology to a must-have component in many AI applications. As developers and technical leaders, we're

If you want an open-source vector database that you can run yourself without paying for a cloud subscription, you have several good options. The best choice depends mostly on your scale and whether you already use another database.

Good self-hosted open-source choices

My practical recommendations

• Small app, local AI assistant, document search: Start with Qdrant. It is a dedicated vector database but still simple enough to run on a single machine.
• You already have PostgreSQL: Use pgvector. It avoids introducing another database and is often enough for millions of embeddings.
• Enterprise-scale / hundreds of millions to billions of vectors: Consider Milvus. It is powerful but usually requires more infrastructure.
• Need keyword + semantic search together: Consider Weaviate.

Easiest way to start

For a self-hosted setup, a common first choice is:

• Docker container running Qdrant
• Local embedding model (for example, from Hugging Face)
• Your application talks to Qdrant over HTTP/gRPC
• Store documents and metadata alongside embeddings

This gives you a completely local stack with no vendor subscription.

If you tell me your expected size (number of documents/vectors), language/framework (Python, JavaScript, etc.), and whether this is for RAG, search, or recommendations, I can narrow it down.

If you want a powerful, open-source vector database that runs entirely on your local machine or self-hosted infrastructure without forcing a cloud subscription, you have some fantastic options.

Here are the top open-source, self-hosted vector databases available:

• Chroma : An AI-native open-source embedding database designed specifically to get up and running quickly with Python or JavaScript. It runs locally in-memory or persists to your local disk, making it ideal for local prototyping and smaller-to-medium RAG (Retrieval-Augmented Generation) applications.
• Milvus : A highly scalable, distributed open-source vector database built for massive enterprise workloads. While it scales out to the cloud, it can be deployed entirely locally or on-premise using Docker Compose or Kubernetes with no required cloud lock-in.
• Qdrant : Written in Rust, Qdrant is exceptionally fast and resource-efficient. It offers a robust open-source engine with an easy-to-use Python and REST/gRPC API. It runs locally via Docker and scales easily on your own hardware.
• LanceDB : A serverless, open-source vector database built on top of the columnar data format Lance. It doesn't require a separate server process or a cloud subscription—it embeds directly into your application process and stores data locally on your filesystem or object storage.
• FAISS (Facebook AI Similarity Search) : Strictly a library rather than a full database system, FAISS is the gold standard for dense vector similarity search in memory. It doesn't handle metadata filtering or CRUD operations out-of-the-box like a database, but it is entirely local, open-source, and lightning-fast.

To help narrow down the best choice, tell me:

• What programming language or stack are you using (Python, Node.js, Go, etc.)?
• Will this run purely in-memory/locally on a single machine, or do you need to scale across multiple servers?

Yes. There are several good open-source, self-hosted vector databases that don't require a cloud subscription.

My top choices

1. Qdrant — probably the best standalone choice

Qdrant is open source and can be run entirely on your own hardware. It supports dense, sparse, and multivector search, metadata filtering, hybrid search, REST, and gRPC.

It's particularly attractive if you want something that is actually a vector database, rather than adding vector capabilities to an existing database.

You can run it locally with Docker, for example:

docker run -p 6333:6333 qdrant/qdrant

Qdrant also has a self-hosted web UI, which is useful for inspecting collections and data.

2. pgvector — excellent if you already use PostgreSQL

pgvector on GitHub turns PostgreSQL into a vector database through an extension. It supports exact search, HNSW and IVFFlat approximate search, cosine/L2/inner-product distances, sparse vectors, and more.

This is often my recommendation for an application where you already have PostgreSQL:

Postgres + pgvector → relational data + metadata + embeddings + vector search in one system.

It can be installed directly or run via Docker, with no hosted service required.

Which would I choose?

• Building a RAG/AI application from scratch: Qdrant
• Already using PostgreSQL: pgvector
• Millions/billions of vectors and specialized vector infrastructure: Milvus
• Experimenting locally or building a small prototype: Chroma

One important distinction: "open source" and "no cloud subscription" aren't quite the same thing. You can self-host Qdrant and pgvector without paying for a cloud service; the costs are simply the hardware/VM/storage you choose to run them on.

If you tell me roughly how many documents/vectors you expect (e.g. 100K, 10M, 1B), what language you're using (Python/Node/etc.), and whether you already have PostgreSQL, I can narrow this down to the best choice for your setup.

• Chroma : An open-source embedding database designed specifically for AI applications. It runs easily in-memory or locally via Python/JavaScript without forcing a cloud tier, though a hosted version exists if you ever want it.
• FAISS (Facebook AI Similarity Search) : A library developed by Meta for efficient similarity search and clustering of dense vectors. It is entirely open-source, runs locally, and handles massive datasets in memory (though it doesn't have a built-in traditional database wrapper/persistence layer out of the box).
• LanceDB : An open-source, serverless vector database built on top of the Lance columnar data format. It runs locally in your process or data files without managing a separate server or cloud subscription.
• Milvus : A highly scalable open-source vector database built for embedding storage and similarity search. It can be run fully locally via Docker for development and production without a cloud dependency.
• Qdrant : A vector similarity search engine and vector database with an Apache 2.0 open-source core. It offers a convenient local mode where it runs as a self-hosted Docker container or embedded library without requiring any cloud connection.
• Weaviate : An open-source vector database that can be self-hosted locally via Docker. While they offer a managed cloud service, the core open-source engine is fully functional on your own hardware or local machine.

If you can tell me a bit more about your project:

• What programming language or framework are you using?
• What is the estimated scale of your vector data (number of vectors and dimensions)?
• Do you need persistent disk storage or is in-memory/embedded sufficient?

I can recommend the best local fit for your stack.

Yes. If your requirement is open source + self-hosted + no cloud subscription, you have several good choices.

My top picks

🥇 Qdrant — my recommendation

I'd start with Qdrant unless you have an unusual scale requirement. It's open source, can run entirely on your own machine/server, and has a straightforward Docker deployment. Qdrant also supports filtering, hybrid search, quantization, and distributed deployments.

You can literally run it locally with:

docker run -p 6333:6333 qdrant/qdrant

Your vectors are stored locally rather than in a cloud service.

It also has Python, JavaScript/TypeScript, Go, Java, .NET, and other client libraries.

🥈 Milvus

Milvus is the better choice if you're expecting millions/billions of vectors or eventually want a distributed architecture. It is Apache 2.0 licensed and supports both standalone and distributed deployments.

It can also run locally through Milvus Lite, which is particularly convenient for Python applications.

The tradeoff is that it's considerably more infrastructure than you need for a small-to-medium RAG application.

🥉 Chroma

Chroma is worth considering if you're building something like:

Python application → embedding model → vector database → RAG

and want the least infrastructure possible.

For a small application, I would actually consider starting with Chroma or Qdrant and moving to something more distributed only when necessary.

What I'd choose

If you tell me roughly how many documents/vectors you expect and whether this is for RAG, semantic search, recommendations, or something else, I can narrow it down to one choice and give you a completely self-hosted architecture (including the embedding model and Docker setup).

Several excellent open-source vector databases can be run entirely on your own hardware or local machine with zero cloud subscription requirements or vendor lock-in.

The best choice depends heavily on your scale, whether you need a dedicated server or an embedded library, and your existing infrastructure.

• pgvector (PostgreSQL Extension)
  • Best for: Teams or apps already running PostgreSQL.
  • Strengths: Adds vector data types and HNSW/IVFFlat indexing directly into a standard PostgreSQL database. You get full ACID compliance, backups, and relational data querying in one place.
  • Scale: Handles up to tens of millions of vectors efficiently on a single instance.
  • Hosting: 100% free and self-hosted via the pgvector GitHub Repository.
• Qdrant
  • Best for: High-performance, filter-heavy production applications and RAG.
  • Strengths: Written in Rust, Qdrant is exceptionally fast and memory-efficient. It features advanced, payload-based filtering that executes in a single stage, avoiding slow post-filtering.
  • Scale: Scales cleanly from a local single-node Docker container to distributed setups.
  • Hosting: Fully open-source core with a straightforward self-hosted Docker setup. Check out the Qdrant Documentation to get started.
• Chroma
  • Best for: Local development, MVPs, and lightweight prototyping.
  • Strengths: Designed to be developer-friendly and acts almost like the "SQLite of vector databases". It can run entirely in-memory or embedded directly inside your Python/Node.js script.
  • Scale: Best kept under a few million vectors; not ideal for heavy, high-availability multi-tenant production clusters.
  • Hosting: Open-source and runs locally with zero configuration via the Chroma Homepage.
• Weaviate
  • Best for: AI-native apps requiring hybrid search (vector + keyword) and multimodal data.
  • Strengths: Stores both objects and vectors together, supports native hybrid search, and offers optional modules that can handle text-to-vector transformations locally.
  • Scale: Highly modular and scalable for production workloads.
  • Hosting: Fully open-source and free to self-host (see the Weaviate GitHub Repository).
• Milvus
  • Best for: Massive, billion-scale enterprise production systems.
  • Strengths: Built from the ground up for massive horizontal scaling, decoupling compute from storage with support for various index types and GPU acceleration.
  • Scale: The gold standard for tens of billions of vectors, though it carries a heavier operational footprint than Qdrant or Chroma.
  • Hosting: Open-source code available via the Milvus Website.
• LanceDB
  • Best for: Serverless, local-first, or data science workflows where you don't want to run a separate database daemon.
  • Strengths: Built on the columnar Lance format, allowing in-process, zero-copy access to data directly on your file system or object storage.
  • Scale: Excellent for local or embedded multi-modal datasets.
  • Hosting: Open-source library detailed on the LanceDB GitHub.

To help narrow down which of these fits best, tell me:

• What is your estimated vector scale (e.g., thousands, millions, or billions)?
• Are you looking for an embedded library (runs inside your app) or a standalone server/Docker container?

If you want an open-source vector database that you can run yourself without a cloud subscription, you have several good options. The best choice depends on your scale and what you are building (RAG app, semantic search, recommendations, etc.).

My recommendations

For a personal project, local AI assistant, or small RAG app

• Choose Qdrant or Chroma.
• They are simple to run with Docker and do not require a managed service.

For a production AI application

• Choose Qdrant if you want simplicity and strong performance.
• Choose Weaviate if you need built-in hybrid search, filtering, and more AI-oriented features.

For millions/billions of embeddings

• Choose Milvus. It is built for large-scale vector workloads and supports self-managed deployments.

For an organization that already runs PostgreSQL

• Use pgvector. It avoids operating another database and works well when vector search is only one part of your application.

A practical default stack today would be:

• Embeddings: local model via Ollama or another embedding model
• Vector DB: Qdrant
• App layer: Python + FastAPI
• Deployment: Docker Compose on your own server

That gives you a fully self-hosted AI search/RAG system with no vector database subscription.