Vector DatabasesStore & Search AI Embeddings
Think of it like: A regular database is a filing cabinet with labeled folders. A vector database is a room where similar items naturally cluster together — you just point at something and say "find me more like this."
What Are Vector Databases?
What is a vector database?
A vector database stores embeddings — lists of numbers that represent meaning. Unlike regular databases that find exact matches, vector databases find SIMILAR items. "Find documents like this one" instead of "Find documents with this exact word."
Why can't I use a regular database?
Regular databases (MySQL, PostgreSQL) search by exact values. They can't efficiently answer "What's most similar to this?" Vector databases are built specifically for similarity search across millions or billions of vectors.
What are they used for?
Semantic search, RAG (giving AI knowledge of your documents), recommendation systems, image search, duplicate detection, and anomaly detection. Any time you need to find "things like this."
How It Works
The basic flow of using a vector database
Create Embeddings
Turn your text/images into vectors using an embedding model (OpenAI, Cohere, etc.)
Store in Vector DB
Upload vectors with metadata (original text, source, timestamps, etc.)
Query with a Vector
Embed your search query and ask: "Find the 10 most similar vectors"
Get Results
Receive matches ranked by similarity score, with their metadata
Popular Vector Databases
The main options and what makes each unique
Pinecone
The most popular choice. Fully managed, scales automatically, generous free tier. Just works.
Pros
- ✓ Zero ops needed
- ✓ Fast to start
- ✓ Great documentation
Cons
- • Cloud-only
- • Costs scale with usage
Weaviate
Feature-rich with built-in ML models. Can vectorize data automatically. GraphQL API.
Pros
- ✓ Self-host or cloud
- ✓ Built-in vectorization
- ✓ Hybrid search
Cons
- • More complex
- • Heavier resource usage
Chroma
Lightweight and embeddable. Perfect for prototypes and local development. Python-native.
Pros
- ✓ Super easy setup
- ✓ Embeds in your app
- ✓ Great for RAG
Cons
- • Less mature
- • Limited scale
Qdrant
High-performance with advanced filtering. Written in Rust. Production-ready.
Pros
- ✓ Very fast
- ✓ Rich filtering
- ✓ Good for production
Cons
- • Smaller community
- • Less documentation
pgvector
Add vector search to your existing PostgreSQL. No new database needed.
Pros
- ✓ Use existing Postgres
- ✓ SQL familiar
- ✓ No new infra
Cons
- • Less specialized
- • Performance limits
Milvus
Enterprise-grade, built for massive scale. Used by major tech companies.
Pros
- ✓ Billion-scale
- ✓ Production proven
- ✓ Rich features
Cons
- • Complex setup
- • Heavy resources
Use Cases
What you can build with vector databases
RAG (Retrieval-Augmented Generation)
Store your documents as vectors. When users ask questions, find relevant chunks and feed them to GPT.
Example: Company knowledge base chatbot
Semantic Search
Search by meaning, not keywords. "How to make pasta" finds "Italian cooking recipes" even without the word "pasta."
Example: Documentation search, e-commerce
Recommendation Systems
Find similar products, articles, or content based on what users liked or viewed.
Example: "More like this" features
Duplicate Detection
Find near-duplicates even with different wording. Plagiarism detection, content deduplication.
Example: Content moderation, data cleaning
Anomaly Detection
Find outliers that don't match the normal pattern. Fraud detection, quality control.
Example: Security monitoring, error detection
Which Should You Choose?
Quick recommendations based on your situation
Just starting / prototyping
Install with pip, runs locally, no account needed. Perfect for learning.
Building a real product
Managed service, generous free tier, scales automatically. Focus on your app, not infrastructure.
Already using PostgreSQL
Add vector search without new infrastructure. Keep your existing setup.
Need full control / privacy
Self-host on your own servers. Full control over data and costs.
Enterprise scale (billions)
Built for massive scale with enterprise features and support.
Best Practices
Tips for getting the most out of vector search
Store metadata generously
Include the original text, source, timestamps, categories. You'll want it for filtering and display.
Use the right chunk size
Split documents into 100-500 word chunks. Too small = no context. Too large = diluted meaning.
Choose the right metric
Cosine similarity for text (most common). Euclidean for some image use cases.
Add filters when possible
Narrow search with metadata filters before vector search. Faster and more relevant.