Pinecone
Reference vector database for RAG and semantic search — Starter tier is free up to 2GB
Overall score
About
Pinecone is the most-used vector database in production AI applications — fully managed, serverless, with sub-50ms query latency at scale. Most builders pick Pinecone first for RAG and semantic search because the developer experience and operational maturity are unmatched in the category, then migrate to Weaviate/Qdrant only if they need self-hosting or have specific cost optimizations.
Best for: Any team building RAG, semantic search, or recommender features and wanting a managed vector DB without infrastructure overhead. Starter tier is genuinely useful — 2GB and 1M reads/mo covers a lot of POCs and side projects before you pay anything.
Pricing
Starter
- Monthly
- Free
- Annual /mo
- Free
- Billing
- flat
- Notes
- Up to 2GB storage;2M write units/mo;1M read units/mo;5 indexes;100 namespaces per index;Community support · Free forever. Genuinely usable for POCs and small side projects.
Builder
- Monthly
- $20
- Annual /mo
- $20
- Billing
- flat
- Notes
- Up to 10GB storage;5M write units/mo;2M read units/mo;10 indexes;1,000 namespaces per index;Free support · Flat $20/mo with bumped limits. Best value for individual developers and small teams.
Standard
- Monthly
- $50
- Annual /mo
- $50
- Billing
- usage
- Notes
- Pay-as-you-go on top of $50/mo minimum;Storage at $0.33/GB/mo;Read units at $16-$18/M;Write units at $4-$4.50/M;Unlimited storage;20 indexes;100,000 namespaces per index;HIPAA available as $190/mo add-on · $50/mo minimum then PAYG. 3-week trial with $300 in credits.
Enterprise
- Monthly
- $500
- Annual /mo
- $500
- Billing
- flat
- Notes
- Everything in Standard;99.95% uptime SLA;Private networking;Customer-managed encryption keys;Pro support;200 indexes per project · $500/mo minimum. Contact sales for custom commitment.
| Tier | Monthly | Annual /mo | Billing | Notes |
|---|---|---|---|---|
| Starter | Free | Free | flat | Up to 2GB storage;2M write units/mo;1M read units/mo;5 indexes;100 namespaces per index;Community support · Free forever. Genuinely usable for POCs and small side projects. |
| Builder | $20 | $20 | flat | Up to 10GB storage;5M write units/mo;2M read units/mo;10 indexes;1,000 namespaces per index;Free support · Flat $20/mo with bumped limits. Best value for individual developers and small teams. |
| Standard | $50 | $50 | usage | Pay-as-you-go on top of $50/mo minimum;Storage at $0.33/GB/mo;Read units at $16-$18/M;Write units at $4-$4.50/M;Unlimited storage;20 indexes;100,000 namespaces per index;HIPAA available as $190/mo add-on · $50/mo minimum then PAYG. 3-week trial with $300 in credits. |
| Enterprise | $500 | $500 | flat | Everything in Standard;99.95% uptime SLA;Private networking;Customer-managed encryption keys;Pro support;200 indexes per project · $500/mo minimum. Contact sales for custom commitment. |
Startup offer
Included in hub programs
Key features
- Serverless and pod-based deployments
- Sub-50ms query latency at scale
- SOC 2, GDPR, ISO 27001 compliance
- HIPAA add-on for regulated workloads
- Multi-region with customer-choice data residency
- Up to 100,000 namespaces per index
- Native LangChain and LlamaIndex integrations
- 3-week trial with $300 credits on Standard
Integrations
Trust & compliance
- Stage range
- MVP → Growth
- Founded
- 2019
- Status
- active
- SOC 2
- yes
- GDPR
- yes
- Data residency
- customer_choice
- External rating
- n/a
- Last verified
- Jun 2026
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People who've discussed Pinecone
See all people →Curated mentions from podcasts, posts, and public stacks. Editorial coverage; not endorsements.
David Sacks
General Partner; Co-host All-In; White House AI & Crypto Czar
Pinecone is in Craft Ventures's portfolio as the vector-database play in Sacks's AI infra book, alongside Glean, Writer, OpenAI, Anthropic, Grok and Chroma. Sacks publicly groups these as 'companies that have a chance to win big categories' in his enterprise-AI thesis.
Martin Casado
General Partner
Pinecone is in a16z's infrastructure thesis; Casado has covered vector databases and Pinecone specifically in his commentary. He frames vector databases as one of the foundational primitives in the AI infrastructure stack, alongside frontier models and the agent-orchestration frameworks built on top.