Pinecone
Build knowledgeable AI with Pinecone. Fast retrieval, accurate results, lower costs. Start for free.
Editor's Verdict
Key Takeaways
- Fast Retrieval
- Automatic Indexing
- Scalable Namespaces
- Filtered Search
In-Depth Review: What is Pinecone?
Pinecone is a fully managed vector database built for AI, enabling fast semantic search, RAG pipelines, and agent memory at any scale. Writes are instantly searchable, indexing is automatic, and queries stay fast with consistent p99 latency. Trusted by enterprises, it offers SOC 2, HIPAA, and GDPR compliance. Start building knowledgeable AI in seconds.
Core Features
Fast Retrieval
Sub-100ms writes and consistent query speed at any scale, with p50 latency as low as 31ms for 1B vectors.
Automatic Indexing
No manual tuning required; algorithms are selected and upgraded automatically based on data size.
Scalable Namespaces
Isolated memory per agent with up to 1.7M namespaces per index, enabling efficient multi-tenant applications.
Filtered Search
Filtered results at the same speed as unfiltered, with p50 latency of 12ms.
Fully Managed Infrastructure
Writes are instantly searchable, indexing is automatic, and queries stay fast at any scale without operational overhead.
Enterprise Security
Encryption at rest and in transit, SSO, RBAC, CMEK, private networking, and compliance with SOC 2, HIPAA, GDPR, ISO 27001.
Pricing
Starter
- Up to 2 GB storage
- Up to 2M write units/month
- Up to 1M read units/month
- Up to 1GB egress/month
- Dense, Sparse, and Full-Text Indexes
- Console Metrics
- Community Support via Discord
- 1 project, up to 2 users
- Up to 5 indexes
- 100 namespaces per index
- All available embedding models
- bge-reranker-v2-m3 only for reranking
- 1GB Assistant storage included
- 500k/mo input tokens, 300k/mo output tokens for Assistant
- 5M tokens/mo for embedding inference (llama-text-embed-v2, multilingual-e5-large, pinecone-sparse-english-v0)
- 500 requests/mo for reranking (bge-reranker-v2-m3)
Builder
- Up to 10 GB storage
- Up to 5M write units/month
- Up to 2M read units/month
- Up to 10GB egress/month
- Dense, Sparse, and Full-Text Indexes
- Console Metrics
- Prometheus and Datadog monitoring
- Free support included
- 5 projects, up to 5 users
- Up to 10 indexes per project
- 1,000 namespaces per index
- All available embedding models
- bge-reranker-v2-m3 only for reranking
- 3GB Assistant storage included
- 2M/mo input tokens, 1M/mo output tokens for Assistant
- 10M tokens/mo for embedding inference
- 1,000 requests/mo for reranking (bge-reranker-v2-m3)
Standard
- Unlimited storage ($0.33/GB/mo)
- Unlimited write units ($4-$4.50 per million)
- Unlimited read units ($16-$18 per million)
- Unlimited egress ($0.10/GB, 100GB/mo included)
- Import from object storage ($0.25 per GB)
- Backup and Restore ($0.10/GB/mo for backup, $0.15/GB for restore)
- Dense, Sparse, and Full-Text Indexes
- All available models for reranking
- SAML SSO
- HIPAA add-on ($190/mo)
- Free support included
- 20 projects, unlimited users
- Up to 20 indexes per project
- 100,000 namespaces per index
- All available embedding and reranking models
- Unlimited Assistant storage ($3/GB/mo)
- Unlimited input tokens ($8/million), output tokens ($15/million), context tokens ($5/million), evaluation tokens ($8-$15/million)
- Ingestion units ($0.0005/unit, $0.001/multi-modal)
- Unlimited inference tokens for embedding ($0.08-$0.16/M tokens)
- Unlimited reranking requests ($2/1k requests)
Enterprise
- Everything in Standard
- 99.95% Uptime SLA
- Bring Your Own Cloud (BYOC)
- Private Networking
- Customer Managed Encryption Keys
- Audit Logs
- Service Accounts
- Admin APIs
- HIPAA Compliance included
- Pro support included
- Up to 200 indexes per project
- Dedicated Read Nodes
- Higher pricing dimensions: write units $6-$6.75/million, read units $24-$27/million
- All other features same as Standard
BYOC (Bring Your Own Cloud)
- Pinecone in your cloud account
- Zero-access operations (no SSH, VPN, or inbound access required)
- Outbound-only operations with auditable trail
- Pro support included
- Private connectivity via AWS PrivateLink, GCP Private Service Connect, Azure Private Link
- Custom pricing based on usage
Pros and Cons
Pros
- Fast and ScalablePinecone provides sub-100ms write acknowledgment and consistent low-latency queries (e.g., 31ms p50 for 1B vectors) at any scale, making it ideal for real-time AI applications.
- Fully ManagedNo need to manage infrastructure; indexing is automatic, algorithms are self-optimizing, and writes are instantly searchable. Developers focus on building, not operations.
- Multi-Tenancy SupportSupports up to 1.7M namespaces per index, enabling isolated memory for each agent or tenant without separate indexes, reducing cost and complexity.
- Enterprise-ReadyOffers SOC 2, HIPAA, GDPR, ISO 27001 compliance, encryption at rest/transit, SSO, RBAC, CMEK, and private networking, meeting enterprise security needs.
- Flexible PricingGenerous free tier (Starter) and a flat-rate Builder plan ($20/mo) for small teams, with pay-as-you-go options for production. Also supports BYOC for maximum control.
Cons
- Cost at ScaleWhile starter plans are free or low-cost, production usage (Standard/Enterprise) can become expensive due to per-million pricing for write/read units, storage, and inference tokens.
- Limited Free TierStarter plan is restricted to 2GB storage, 5 indexes, and 1M read units per month, which may not suffice for even moderate workloads without upgrading.
- Reranking Model RestrictionStarter and Builder plans are limited to bge-reranker-v2-m3 only for reranking; advanced models are only available on higher tiers.
- Minimum CommitmentStandard and Enterprise plans require a minimum monthly spend ($50 and $500 respectively), which may be a barrier for small projects or startups.
- Learning CurveNew users may need time to understand vector databases, embedding models, and Pinecone-specific concepts like namespaces, indexes, and the console.
Use Cases & Recommended Professions
AI Engineer→ View Toolkit
Needs to build and deploy AI agents or RAG pipelines with fast, scalable vector search for knowledge retrieval.
Data Scientist→ View Toolkit
Requires efficient similarity search on large datasets for tasks like recommendation, clustering, or anomaly detection.
Machine Learning Engineer→ View Toolkit
Integrates vector databases into ML pipelines for semantic search, few-shot learning, or memory-augmented models.
Search Engineer→ View Toolkit
Builds and optimizes search systems that need low-latency, high-relevance results across billions of vectors.
Product Manager (AI Features)→ View Toolkit
Defines product requirements for AI features that rely on fast, accurate similarity search (e.g., chatbots, content recommendation).
DevOps / Platform Engineer→ View Toolkit
Manages infrastructure for AI applications; Pinecone's fully managed service reduces operational burden while ensuring compliance and security.
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ℹ️ Curation Disclosure: The overview and features of Pinecone were synthesized using AI and fact-checked by our curation team to ensure accuracy.
