Starter (Free)
Free
- Up to 5 indexes
- 2 GB storage
- 2M write units/month
- 1M read units/month
- US-East-1 only
- 1 project, 2 users
Pinecone — Vector Databases Platform
Founded 2019|New York, NY|51-200 people|Freemium
What is Pinecone?
Pinecone is the most widely used managed vector database, purpose-built for similarity search and retrieval-augmented generation (RAG). Founded in 2019 by Dr. Edo Liberty, former Head of Amazon AI Labs at AWS, Pinecone offers serverless and pod-based architectures supporting billions of vectors with single-digit millisecond query latency.
The platform provides metadata filtering, namespaces, and hybrid search combining dense and sparse vectors. Its managed service eliminates infrastructure complexity, making it the go-to choice for teams building semantic search, recommendation engines, and RAG-powered AI applications.
Headquartered in New York City with 138 employees, Pinecone has raised $138M in total funding including a $100M Series B at a $750M valuation. The company serves over 4,000 customers and is rated 4.7/5 on G2.
Key features
Core capabilities this platform advertises.
- Fully managed serverless vector database
- Hybrid search with sparse and dense vectors
- Metadata filtering
- Namespaces for multi-tenancy
- Real-time index updates
Strengths and tradeoffs
What this tool does well, and the limitations to keep in mind.
Pros
- Industry-leading managed vector database with zero infrastructure overhead
- Sub-10ms query latency at billion-vector scale
- Hybrid search combining dense and sparse vectors
- Strong ecosystem integrations with LangChain, LlamaIndex, and more
- Generous free tier for prototyping and development
Cons
- SaaS-only with no self-hosting option
- Costs can escalate significantly at large scale
- Eventual consistency model may not suit transactional systems
- Free tier limited to US-East region only
Plans & pricing
What's included in each plan, and how the tiers compare.
Standard
Usage-based
Monthly
- Serverless architecture
- Multiple regions
- Higher storage and throughput
- Pay-per-use pricing
Enterprise
Custom
Annual
- Dedicated infrastructure
- SSO/SAML
- SOC 2 Type II
- Custom SLAs
- Priority support
Common use cases
Engineering teams building production AI applications that need managed, scalable vector search
- Production RAG pipelines
- Semantic search at scale
- Recommendation systems
- Multi-tenant SaaS AI features
- Real-time personalization
Using Pinecone with Respan
Respan complements Pinecone by providing observability into the full RAG pipeline. While Pinecone handles vector storage and retrieval, Respan monitors the quality of retrieved contexts, evaluates LLM response accuracy, and tracks end-to-end RAG performance.
- Monitor RAG retrieval quality from Pinecone queries
- Evaluate whether retrieved contexts improve LLM responses
- Track Pinecone query latency and costs in production
- Identify retrieval gaps and optimize embedding strategies
Best Pinecone alternatives & competitors
Top companies in Vector Databases you can use instead of Pinecone.
Milvus
Billion-scale vector search
Qdrant
High-performance open-source vector search
Chroma
Lightweight embedded vector database
Supabase
pgvector hosting
Weaviate
Open-source vector database
Neo4j
Native graph database with Cypher query language
MongoDB Atlas Vector Search
Elasticsearch
Redis Vector
ClickHouse
Analytics + vector
TigerGraph
Neon
Serverless Postgres
Vespa
LanceDB
Turbopuffer
SingleStore
ArangoDB
Compare Pinecone
Side-by-side comparisons with other tools in this category.
Best integrations for Pinecone
Companies from adjacent layers in the AI stack that work well with Pinecone.
Last verified: March 1, 2026