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Pinecone pricing & features
Managed vector database for AI — RAG, semantic search, recommendations
Managed vector DB for production AI. Serverless architecture, sub-100ms queries at billions-of-vectors scale. Inference (hosted embedding models) + Assistant (RAG-as-a-service) add-ons.
Data sourced from vendor documentation · Last updated May 2026
Pricing
| Tier | Price | Notes |
|---|---|---|
| Starter (Free) | Free | 2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes. us-east-1 AWS only. |
| Standard | $50/mo | $50/month minimum. Unlimited storage ($0.33/GB/mo) + writes ($4-4.50/M) + reads ($16-18/M). 20 indexes/project. Multi-region, multi-cloud. |
| HIPAA Add-on | $190/mo | $190/month add-on for HIPAA-eligible workloads. |
| Enterprise | $500/mo | $500/month minimum. Higher per-unit rates for dedicated infra + SLA. 200 indexes. |
Limits
| Tier | Metric | Value | Notes |
|---|---|---|---|
| — | architecture | Serverless (pay-per-use) or pod-based (dedicated) | Architecture options |
| — | backups | $0.10/GB/month | Backup storage |
| — | clouds supported | AWS, Azure, GCP (Starter only AWS us-east-1) | Cloud support |
| — | import from s3 | $1/GB | Bulk import pricing |
| — | query latency | <100ms at billion-vector scale (typical) | Typical performance |
| — | read units rate | $16-18/M (Standard), $24-27/M (Enterprise) | Read unit pricing |
| — | standard trial | $300 credits / 3 weeks on Standard | Standard trial |
| — | storage rate | $0.33/GB/month | Standard storage |
| — | write units rate | $4-4.50/M (Standard), $6-6.75/M (Enterprise) | Write unit pricing |
Features
- Backups + PITR — Automated + manual backups.
- HIPAA Eligible — BAA available via add-on.
- Metadata Filtering — Filter vectors on metadata at query time.
- Monitoring — Metrics endpoint, export to Datadog/Prometheus.
- Namespaces — Multi-tenancy inside an index. Isolate vectors per customer.
- Pinecone Assistant — RAG-as-a-service: upload docs → get a ready chat endpoint. · docs
- Pinecone Inference — Hosted embedding models (multilingual-e5, llama-text-embed-v2, etc.) inside data plane. · docs
- Pod-Based Indexes — Dedicated pods (p1, s1, p2) for consistent low-latency workloads.
- Private Networking — AWS PrivateLink / VPC peering on Enterprise.
- RBAC — Per-project + per-API-key roles.
- Rerank (Cohere-backed) — Optional reranker on top of vector search.
- Serverless Indexes — Pay per use. No provisioning. Auto-scales. · docs
- Sparse-Dense Vectors — Hybrid search: sparse (keyword) + dense (semantic) together.
Developer interfaces
| Slug | Name | Kind | Version |
|---|---|---|---|
| sdk-node | @pinecone-database/pinecone | sdk | 4.x |
| data-api | Data Plane (per-index) | rest | 2024-10 |
| cli | Pinecone CLI | cli | 0.x |
| rest-api | Pinecone Control Plane | rest | 2024-10 |
| mcp | Pinecone MCP | mcp | — |
| sdk-dotnet | Pinecone.NET | sdk | 3.x |
| sdk-go | go-pinecone | sdk | 3.x |
| sdk-python | pinecone (Python) | sdk | 5.x |
| sdk-java | pinecone-java-client | sdk | 3.x |
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