Pinecone: Vector Databases

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.

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

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

View official pricing page

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

Run Pinecone in production with Respan

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

Best Pinecone alternatives & competitors

Top companies in Vector Databases you can use instead of Pinecone.

Milvus

Billion-scale vector search