Pinecone
The vector database to build knowledgeable AI
Founded
2019
Starting Price
Free
Category
Developer Tools
Last updated February 10, 2026
About Pinecone
Pinecone is a fully managed vector database purpose-built for AI applications. It stores, indexes, and searches high-dimensional embeddings at scale with millisecond latency, enabling developers to build semantic search, RAG pipelines, and recommendation systems without managing infrastructure. Available on AWS, GCP, and Azure with serverless and dedicated deployment options.
Pros & Cons
Pros
- Fully managed serverless architecture eliminates all infrastructure management burden
- Millisecond query latency even at billion-vector scale with consistent performance
- Generous free tier with 2 GB storage makes it easy to prototype and experiment
- Integrated inference API for embeddings and reranking reduces external dependencies
- Multi-cloud availability (AWS, GCP, Azure) with BYOC option for strict compliance needs
Key Features
Serverless Vector Database
Fully managed infrastructure that auto-scales based on usage with no provisioning, patching, or capacity planning required
Low-Latency Similarity Search
Millisecond query performance using approximate nearest neighbor algorithms across billions of vectors
Hybrid Search
Combine dense and sparse vector search with metadata filtering in a single query for superior relevance
Integrated Inference
Built-in embedding and reranking models so you can go from text to search results without external model hosting
Pinecone Assistant
Upload documents and get an AI assistant with RAG capabilities out of the box, no pipeline building needed
Multi-Cloud Deployment
Available on AWS, Google Cloud, and Microsoft Azure with region selection for data residency compliance
Bring Your Own Cloud (BYOC)
Deploy Pinecone in your own cloud account for maximum security, compliance, and data isolation
Pricing
Starter
Free
- 2 GB storage
- 2M write units/month
- 1M read units/month
- 5M embedding tokens/month
- Up to 5 indexes
Best For
Retrieval-Augmented Generation (RAG)
Power LLM applications with relevant context by storing and retrieving document embeddings for grounded AI responses
Semantic Search
Build search experiences that understand meaning and intent rather than just keyword matching across documents, products, or support tickets
Recommendation Systems
Deliver personalized content, product, or media recommendations based on embedding similarity at scale
AI Chatbots & Assistants
Give conversational AI agents access to organizational knowledge bases for accurate, context-aware responses
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Featured In
#3
7 Best RAG Frameworks for Building AI-Powered Knowledge Bases (2026)
Best managed vector database for RAG — the fastest path to production for teams that want zero infrastructure overhead and maximum retrieval performance
#1
7 Best Vector Databases & Embedding Platforms for AI Apps (2026)
Best overall for production AI applications — the fastest path from prototype to scale with zero operational overhead, ideal for teams that value shipping speed over infrastructure control
