Last updated: April 2026
Every AI / RAG application needs a vector database, and the good news in 2026 is that you can run real RAG workloads on $0/month for a long time. Pinecone Standard tier, Qdrant Cloud free tier, Weaviate Cloud sandbox, MongoDB Atlas Vector Search M0, and pgvector via Supabase or Neon all give you genuine free vector storage. Self-hosted options (Chroma, Milvus, Qdrant binary) are unlimited if you can host them. This is the complete map: every free vector database route, exact quotas, hosted vs self-hosted, hybrid search support, and which one to pick for your stage.
For the broader free database ecosystem (Postgres, Mongo, Redis, Snowflake, etc.), see Free Database Credits — every program in 2026. This article is the vector / embedding storage layer specifically.
Free vector databases at a glance
| Database | Free hosted tier | Self-hosted | Hybrid search | Best for |
|---|---|---|---|---|
| Pinecone | Standard tier ($50/mo equivalent free for startup program members) | No (proprietary) | Yes (sparse + dense) | Zero-config, enterprise polish |
| Qdrant | 1 GB cluster, 4 vCPUs free | Yes (binary, Docker) | Yes (full payload + vectors) | Performance + low cost |
| Weaviate | Sandbox 14 days free | Yes (Docker, K8s) | Yes — strongest hybrid | Module ecosystem (text2vec, generative) |
| Chroma | No hosted free | Yes (open source) | Limited | Local prototypes, Python-first |
| Milvus | Zilliz Cloud free tier | Yes (open source) | Yes | Large-scale / billion-vector |
| pgvector | Yes — via Supabase / Neon free tier | Yes (Postgres extension) | Via SQL queries | Bootstrap RAG, no extra moving part |
| MongoDB Atlas Vector Search | M0 free cluster (512 MB) | No | Yes (via Atlas) | Document store + vectors |
Pinecone
The most polished managed vector database; free tier through their startup program.
- Free path: Pinecone Startup Program — Standard Tier ($50/month minimum value) free for accepted startups, plus Pro Support and usage credits
- Eligibility: Fewer than 100 employees, Series A or earlier, new Pinecone customer
- How to apply: pinecone.io/startup-program or email [email protected]
- Free public tier: Limited free starter tier with reduced index sizes; upgrades to Standard required for serious workloads
- Strengths: Easiest setup, strong managed reliability, RBAC, backups, Prometheus metrics
- Caveats: Proprietary (no self-host), pricing escalates fast at scale
Qdrant Cloud
The strongest performance-to-cost ratio in the hosted vector space.
- Free tier: 1 GB of cluster storage, 4 vCPUs, no time limit
- No credit card for free tier
- Open source: Full Qdrant binary (Apache 2.0) — self-hostable indefinitely
- How to start: cloud.qdrant.io → sign up → free cluster instant
- Strengths: Sub-10ms p99 query latency at scale, strong filtering on payload metadata, full hybrid search, Rust-native performance
- Caveats: Smaller ecosystem of integrations than Pinecone or Weaviate, less hand-holding
For most modern RAG stacks, Qdrant is the default choice when paid tiers come into play.
Weaviate Cloud
The strongest hybrid search ecosystem and AI-native modules.
- Free tier: Sandbox cluster — free for 14 days, then must upgrade or self-host
- Open source: Full Weaviate binary (BSD-3) — self-hostable indefinitely with Docker / K8s
- Modules: text2vec-openai, text2vec-cohere, text2vec-jinaai, generative-openai, generative-cohere — built-in pipeline integrations
- How to start: console.weaviate.cloud → create sandbox
- Strengths: Best-in-class hybrid search (BM25 + vector fused), rich module system, strong tooling
- Caveats: Hosted free is time-limited (use self-host for long-term free)
Chroma
The Python-first, local-first vector database. No hosted free tier, but trivially self-hostable.
- Free path: Chroma is fully open source (Apache 2.0); no paid tier needed for self-hosting
- Hosted: Chroma Cloud is in early access; pricing not finalized
- Strengths: Easiest possible setup (
pip install chromadb), tight integration with LangChain, in-memory or persistent - Caveats: Single-process bottleneck for large workloads; not designed for high-throughput multi-tenant production at this stage
For local prototypes and Python notebooks, Chroma is unbeatable.
Milvus / Zilliz Cloud
The billion-scale vector database, with a managed offering via Zilliz.
- Free tier: Zilliz Cloud free tier — limited storage, sufficient for prototypes
- Open source: Milvus (Apache 2.0) — self-hostable, designed for petabyte-scale
- Strengths: Built for billion-vector workloads, distributed by design, GPU acceleration support
- Caveats: Heaviest operational footprint; for small workloads, Qdrant or Weaviate are simpler
pgvector via Supabase or Neon (the bootstrap default)
The "no extra moving part" answer. Postgres with vector extension.
- Free path: pgvector ships in modern Postgres distributions; Supabase and Neon both offer it on their free tiers
- Supabase free: 500 MB database, 50K MAU — supports pgvector indexes natively
- Neon free: 500 MB storage, 100 hours of compute — supports pgvector
- Self-hosted: Postgres +
CREATE EXTENSION vector;— completely free - Strengths: No extra service to operate, transactional consistency with relational data, SQL-native filtering and joins, full ACID
- Caveats: Performance lags dedicated vector DBs at scale (5-10M+ vectors); migration path is real but takes work
For most startups under 1-2M vectors, pgvector inside Supabase or Neon free tier is the answer. You only outgrow it when you outgrow the database itself.
MongoDB Atlas Vector Search
If you already use MongoDB, Atlas Vector Search bundles vector + document store.
- Free tier: M0 cluster (512 MB) — full Atlas Vector Search support
- Plus: MongoDB AI Innovators track adds Voyage AI tokens
- Strengths: Single store for documents and embeddings, hybrid search via Atlas Search + Vector Search
- Caveats: 512 MB is tight; once you grow, paid tiers escalate
Pick by stage
Prototype (under 100K vectors): Chroma local OR pgvector via Supabase free — zero infrastructure.
Early product (100K - 1M vectors): pgvector inside Supabase / Neon free tier — keep it simple.
Growing product (1M - 10M vectors): Qdrant Cloud free tier OR migrate to dedicated vector DB. Pinecone if you have the startup program.
Scale (10M+ vectors): Qdrant paid, Weaviate Cloud, or Milvus / Zilliz. Apply for Pinecone Startup Program if eligible.
Frequently asked questions
Which vector database has the most generous free tier in 2026? Qdrant Cloud free tier is the most generous for hosted vector storage — 1 GB of cluster space and 4 vCPUs free, no time limit. Weaviate Cloud's sandbox is similarly generous (free for 14 days, then upgrade or self-host). For perpetually free without limits, pgvector via Supabase or Neon free tiers gives you Postgres + vector extension on the existing free database quota.
What is the difference between Pinecone, Weaviate, and Qdrant? Pinecone is fully managed proprietary, easiest setup and Pro Support. Weaviate is open-source with a managed cloud — strongest hybrid search and module ecosystem. Qdrant is open-source with a managed cloud — best raw performance and lowest hosted price. For pure speed and cost, Qdrant. For ecosystem and AI-native modules, Weaviate. For zero-config and enterprise support, Pinecone.
Should I use a dedicated vector database or pgvector? For prototypes and small RAG apps (< 1M vectors), pgvector via Supabase, Neon, or self-hosted Postgres is genuinely sufficient — and you skip a moving part. Once you cross 5-10M vectors or need hybrid metadata-and-vector search at scale, dedicated vector databases (Qdrant, Weaviate, Pinecone) outperform pgvector significantly. Most startups should default to pgvector and migrate only when needed.
Are there always-free vector database options? Yes. Self-hosted: Chroma, Qdrant (binary), Weaviate (Docker), Milvus, pgvector — all are open source and free forever if you run them yourself. Hosted free tiers: Pinecone Starter, Qdrant Cloud free tier, MongoDB Atlas Vector Search via M0 free cluster. None of the always-free hosted tiers are unlimited, but they comfortably support prototype workloads.
What free vector database is best for production RAG? If you are bootstrapped and need free production RAG: pgvector inside Supabase or Neon free tier handles up to 1-2M vectors with reasonable latency. If you exceed that, Qdrant Cloud's $25/month tier offers strong throughput at low cost. If you have AI-startup credits (Pinecone Startup Program, Weaviate startup tier), use them — but for purely free production, pgvector is the realistic answer.
Related guides
- Free Database Credits — Postgres, Mongo, Redis, Snowflake, etc.
- Free LLM APIs — pair embeddings models with free LLMs
- Free AI API Credits — model-provider startup programs
- Free Cloud Credits for Developers — host your own vector DB
- Free Startup Credits 2026: Complete Guide
Which vector DB is in your stack? Reply with what you use and what made you pick it — I update this with real production friction notes.
