Choosing Your Vector Database

Vector search is powering the next generation of AI applications. But with options ranging from specialized databases to extensions for your existing stack, which one is right for you? This guide helps you decide.

At a Glance Comparison

This chart provides a high-level look at how different solutions stack up across key criteria. Scores are on a 1-5 scale, where 5 is best.

When to Use Which?

Your project's needs are the best guide. Select a use case below to see our recommendations and why.

Select a use case to see our analysis.

Deep Dive

Explore the architecture, setup, and pros & cons of each approach.

The "Add-On": General-Purpose Databases

This approach involves using an extension or a built-in feature to add vector search capabilities to a database you already know and love. It's fantastic for consolidating your tech stack and keeping vector embeddings alongside your primary application data.

PostgreSQL + pg_vector

The reliable relational database, now with vector superpowers via an extension.

How it works:

The `pg_vector` extension adds a new `vector` data type and enables indexing for efficient similarity search using SQL.

Example:

CREATE TABLE items (embedding vector(3));
INSERT INTO items VALUES ('[1,2,3]');
SELECT * FROM items ORDER BY embedding <-> '[1,1,1]' LIMIT 5;

Pros:

  • Data is co-located with business logic
  • Single database to manage and back up
  • Leverages mature PostgreSQL ecosystem
  • Full ACID compliance

Cons:

  • Not purpose-built; may not scale as efficiently
  • Performance depends heavily on indexing and tuning
MongoDB Atlas Vector Search

Integrate vector search into your flexible document model with a built-in aggregation stage.

How it works:

You create a specific vector search index on your collection and then use the `$vectorSearch` pipeline stage to perform queries.

Example:

db.collection.aggregate([
  {
    $vectorSearch: {
      index: 'my_vector_index',
      path: 'embedding',
      queryVector: [0.1, 0.9, ...],
      limit: 5
    }
  }
])

Pros:

  • Seamlessly combines vector search with rich filtering
  • Flexible document model is ideal for metadata
  • Leverages MongoDB's scalability (sharding)

Cons:

  • Primarily available on MongoDB Atlas (cloud)
  • Can be more complex than a simple SQL query

The "Purpose-Built": Specialized Vector Databases

These databases are engineered from the ground up for one primary task: storing and searching billions of vector embeddings at extremely low latency. They are the go-to choice when performance and scale are non-negotiable.

Pinecone

A fully-managed, distributed vector database built for maximum performance and reliability at scale.

Key Features:

  • Low-latency (p99 < 10ms) search
  • Live index updates without downtime
  • Combines vector search with metadata filtering
  • Serverless architecture simplifies operations
  • High scalability for billions of vectors

Pros:

  • Best-in-class performance and scalability
  • Easy to use API and client libraries
  • Managed service reduces operational burden

Cons:

  • Another piece of infrastructure to manage
  • Data is separate from your primary database
  • Can be more expensive for small projects
Chroma DB

The open-source AI-native database focused on developer simplicity and integration.

Key Features:

  • Open-source and self-hostable
  • Simple, Python-first API
  • In-memory and persistent storage options
  • Integrations with LangChain, LlamaIndex, etc.
  • Built-in embedding function support

Pros:

  • Great for local development and prototyping
  • Easy to get started with
  • Strong community and open-source ecosystem
  • Free to use (self-hosted)

Cons:

  • Requires manual setup and scaling for production
  • Not as mature for massive-scale production as managed solutions