The Visual Guide to Vector Search Databases
In the world of AI, finding similar items—from images to text—is everything. Vector search makes this possible. But which database should you use? Let's break it down.
High-Level Showdown
How do the top contenders compare across the most important metrics? Here's the 30,000-foot view. (Higher score is better)
There Are Two Main Paths
Your choice boils down to a fundamental question: Do you add vector capabilities to your current system, or adopt a specialized tool built for the job?
Path 1: The "Add-On"
Integrate vector search directly into the general-purpose database you already use. Best for simplicity and unified data management.
PostgreSQL + pg_vector
Add a simple, powerful extension to the world's most trusted relational database. Your vectors live right next to your application data.
MongoDB Atlas Vector Search
Leverage a built-in feature to combine vector search with complex filtering across flexible JSON documents in the cloud.
Path 2: The "Purpose-Built"
Use a database engineered from the ground up for one thing: blazing-fast vector search at massive scale. Best for performance-critical applications.
Pinecone
A fully-managed, serverless vector database that offers single-digit millisecond latency for billions of vectors. The choice for production at scale.
Chroma DB
An open-source, AI-native database focused on developer ease-of-use. Perfect for local development, prototyping, and getting started fast.
How to Choose?
Follow this simple flowchart to find the right starting point for your project.