Vector databases · Pinecone Systems, Inc.

Pinecone

Fully managed, cloud-only vector database for similarity search and retrieval-augmented generation at scale.

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Pinecone is a fully managed vector database purpose-built for storing embeddings and running approximate nearest-neighbor similarity search at low latency and large scale, most commonly as the retrieval layer behind retrieval-augmented generation (RAG) applications, recommendation systems and semantic search. It separates storage and compute, supports metadata filtering alongside vector search, and offers both serverless (pay-per-use) and pod-based (provisioned) index types. Pinecone is cloud-only with no self-hosted option, which trades deployment flexibility for operational simplicity, and it runs on AWS, GCP and Azure regions. It also offers integrated embedding and reranking models so teams can generate vectors without a separate provider. Pricing is usage-based with a free Starter tier for small projects, flat per-month plans at low volumes, and minimum monthly spend at higher tiers.

At a glance

Vendor Pinecone Systems, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud
Open source No
Best for Teams building production RAG or semantic search applications who want a managed vector store with no infrastructure to run.

Pricing

Free Starter tier for small projects; Builder is a flat monthly fee; Standard and Enterprise are usage-based with a monthly minimum spend; BYOC is custom-quoted.

Plan Price Notes
Starter Free For trying out and small applications
Builder $20/month flat For solo developers and small teams
Standard $50/month minimum usage Includes $300 credit on a 3-week trial; for production applications
Enterprise $500/month minimum usage For mission-critical production applications

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Approximate nearest-neighbor vector search
  • Metadata filtering alongside similarity search
  • Serverless and pod-based index types
  • Integrated embedding and reranking models
  • Multi-cloud availability (AWS, GCP, Azure)
  • Namespace-based multi-tenancy

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Weaviate Open-source vector database with built-in hybrid (vector + keyword) search, available self-hosted or as managed cloud. Qdrant Open-source vector database written in Rust, focused on filtered similarity search with a free managed cloud tier. Milvus Open-source vector database built for billion-scale similarity search, governed under the Linux Foundation. Zilliz Cloud Fully managed cloud version of Milvus, built and operated by Milvus's original creators.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Retrieval-augmented generation (RAG) An architecture that retrieves relevant documents at query time and feeds them to a language model so its answers are grounded in sources. Semantic search Search that matches results to a query's meaning rather than its exact keywords, using vector similarity between embeddings. Vector database A database optimized to store numeric embeddings and quickly find the ones most similar to a given query vector.

Related guides

How to choose a vector database Pick a vector database by deployment model, scale and search type, not by benchmark charts — most teams' real constraint is who runs the cluster.