Keywords AI
Supabase vs Turbopuffer
Compare Supabase and Turbopuffer side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | freemium | — |
| Best For | Full-stack developers building AI apps | — |
| Website | supabase.com | turbopuffer.com |
| Key Features |
| — |
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Supabase
The #1 platform for pgvector. Open-source Firebase alternative with built-in vector search via Postgres.
About Turbopuffer
Turbopuffer is a serverless vector database designed for speed and cost efficiency.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
Browse all Vector Databases tools →Frequently Asked Questions
What is a vector database?▾
A vector database stores high-dimensional numerical representations (embeddings) of data like text, images, or audio, and enables fast similarity search across millions or billions of vectors using approximate nearest neighbor algorithms.
Do I need a dedicated vector database or can I use pgvector?▾
For small to medium datasets (under 10 million vectors), pgvector in PostgreSQL works well and avoids adding another service. For larger datasets or when you need advanced features like hybrid search and real-time indexing, a dedicated vector database is recommended.
How do I choose the right embedding model for my vector database?▾
Match the embedding model to your use case. For general text search, models like OpenAI text-embedding-3 or Cohere embed-v3 work well. For domain-specific applications, consider fine-tuned models. Always benchmark with your actual data.