Supabase Vector API
The Supabase Vector API provides developers free access to perform efficient vector similarity searches within Supabase-managed databases, ideal for AI and machine learning workflows.
Developed by Supabase
Reference for available routes, request structures, and live examples.
Generates vector embeddings from text
https://project.supabase.co/vectorscurl -X POST 'https://project.supabase.co/vectors' \
-H 'Authorization: Bearer YOUR_API_KEY'{
"text": "The capital of France is Paris",
"model": "text-embedding-ada-002"
}{
"model": "text-embedding-ada-002",
"usage": {
"prompt_tokens": 7
},
"embedding": [
0.1,
-0.2,
0.3
]
}- Semantic search for articles or documentsOptimized Capability
- Recommendation engine for e‑commerceOptimized Capability
- Chatbot retrieval of similar Q&A pairsOptimized Capability
- Image similarity search (with embedding vectors)Optimized Capability
- Clustering and tagging pipelines in ML workflowsOptimized Capability
- ✓ Tight integration with Supabase Postgres backend
- ✓ Fast vector similarity via pgvector
- ✓ Freemium tier allows experimentation at low cost
- ✓ Supports multiple distance metrics and filters
- ✗ Relatively new feature with evolving tooling
- ✗ Limited to regions where Supabase offers vector support
- ✗ Freemium usage caps may restrict large-scale testing
- ✗ REST‑only interface may lack advanced batch support
FAQs
API Specifications
v1Less than 10 minutes to enable vector extension and start indexing
60 requests per minute
Up to 1 million vector operations per month, 5 GB storage included, with rate limit of 30 requests/min
Use Case: Best For
Developers building semantic search, AI‑driven recommendation systems, or embedding‑based retrieval on Supabase
Not Recommended For
Teams requiring on‑premise deployment or ultra‑low latency HPC environments
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