Vectors store meaning, not files
A vector database stores embeddings derived from text, images, or records so similar items can be retrieved by semantic distance.
Vector databases and RAG storage
Best Free Vector Databases 2026 | Free Tier Comparison
Vector databases are the retrieval layer behind RAG, semantic search, recommendations, and AI knowledge bases. A good free-tier choice depends less on raw storage and more on vector count, metadata filters, memory, cold starts, and how you will rebuild indexes.
Fast answer
Use Supabase pgvector when vectors and business data belong together.
Use Qdrant or Pinecone when retrieval is a dedicated subsystem.
Do not use vector storage as the only copy of your documents.
Last Updated: 2026-05-22
Vector search mental model
RAG needs retrieval plus source truth
The vector database finds likely chunks, but object storage or SQL should still hold the original documents and canonical metadata.
Metadata filters decide usefulness
Tenant IDs, timestamps, permissions, document type, and language filters often matter as much as cosine similarity.
Indexing has memory cost
HNSW and hybrid indexes can make queries fast, but free tiers may hit RAM, storage, or cold-start limits quickly.
Quick recommendations
Best dedicated free vector DB
Qdrant Cloud
Good for learning real vector database concepts: payload filters, collections, distance metrics, and memory-aware indexing.
Best serverless RAG starter
Pinecone
Good when you want managed serverless vector search and clear read/write unit accounting.
Best SQL plus vector path
Supabase pgvector
Good for small apps where business rows, auth, metadata, and embeddings should live in one Postgres project.
Best edge-light retrieval
Upstash Vector
Good for small indexes called from edge functions, routing rules, personalization, or short knowledge snippets.
Free vector database comparison table
Use the table for storage, query, connection, and index constraints. For RAG, also evaluate chunking, metadata filters, rebuild jobs, and citation quality.
| PROVIDER | FREE STORAGE | MONTHLY BANDWIDTH | SPECS / COMPUTE | CONNECTION LIMITS | KEY CONSTRAINTS | ACTION |
|---|---|---|---|---|---|---|
Qdrant CloudRUST NATIVE VECTOR | 4 GB Storage | Includes 1 GB dedicated RAM limits | Ultra-fast Rust-based engine; advanced payload filtering mechanism extensions | Highly scalable API endpoint pooling | Hard 1GB RAM ceiling; approaching limits causes memory swapping and performance cliff | Go to Site ↗ |
PineconeSERVERLESS VECTOR | 2 GB Storage | 1M Read Units / 2M Write Units per month | Supports hybrid dense/sparse indexing, max 5 indexes per org under Starter plan | Stateless AWS single-region endpoints | Metadata strictly locked at 40KB per vector; severe cold start query latencies on idle tiers | Go to Site ↗ |
Supabase (pgvector)POSTGRES EXTENSION | 500 MB Shared | Shared project egress allocations | Enables relational SQL JOIN queries directly between custom user schemas and vector coordinates | Bound to primary PostgreSQL connection limits | Shared storage metrics; heavy vector index generation (HNSW) aggressively drains main database memory | Go to Site ↗ |
Upstash VectorSERVERLESS EDGE VECTOR | 10,000 Vectors | 10,000 Queries + Writes per Day | 100% native REST HTTP API framework, designed for seamless calls from Cloudflare Workers | Connectionless REST execution pool | Extremely low 10k vector volume ceiling; meant for edge firewalls, not large doc RAG | Go to Site ↗ |
How to choose vector storage
Estimate chunks before choosing
A thousand source documents can become tens of thousands of vectors after chunking. Free vector ceilings disappear faster than raw file size suggests.
Keep permissions in metadata
For multi-tenant apps, every vector should carry tenant and permission metadata so retrieval never leaks private content.
Choose embedding model early
Dimension size, language quality, cost, and model upgrades affect every stored vector. Re-embedding later is a batch job.
Measure recall with real questions
Do not judge vector search by demo queries only. Build a small evaluation set with expected source chunks and citations.
Vector database traps
Storing full text in metadata
Metadata fields are often size-limited and can make indexes heavy. Store chunk text only when the provider supports it cleanly.
Ignoring deletes and re-indexing
RAG systems need lifecycle jobs for deleted files, updated documents, embedding migrations, and stale chunks.
Using vector search as a database
Vector stores are optimized for retrieval, not transactional business data. Keep canonical records in SQL or document storage.
Skipping citations
Without source citations, users cannot verify generated answers and you cannot debug bad retrieval.
Recommended RAG patterns
Small SaaS RAG: Supabase + pgvector
Use Postgres rows for tenants and permissions, pgvector for embeddings, storage buckets for source files, and edge functions for ingestion.
Document assistant: Object Storage + Qdrant
Store PDFs and markdown in object storage, push chunks to Qdrant, and keep an ingestion status table in SQL.
Edge search: Upstash Vector + Workers
Use a tiny vector index near edge functions for routing, short snippets, personalization, or semantic allowlists.
Related categories
Vector database FAQ
What is a vector database used for?+
It stores embeddings so apps can find semantically similar documents, products, tickets, images, or chunks. Common uses include RAG, semantic search, recommendations, deduplication, and clustering.
Do I need a vector database for every AI app?+
No. If the model can answer from the user prompt and a small amount of structured data, SQL or cache may be enough. Use vector search when retrieval over many unstructured documents becomes necessary.
What should I store with each vector?+
Store a stable ID, source document ID, chunk position, tenant/permission fields, language, timestamps, and enough metadata to rebuild citations.
Why are vector results sometimes wrong?+
Poor chunking, weak metadata filters, the wrong embedding model, stale indexes, and missing reranking can all return plausible but irrelevant chunks.