Pinecone

What does Pinecone do?

Pinecone handles vector retrieval by separating storage from query processing, so data can scale independently without server management. Its serverless object-storage architecture supports dense, sparse, and full-text indexes, while metadata filtering and distributed query execution keep searches fast as workloads grow. The product also includes Pinecone Assistant and Pinecone Inference for building AI applications around retrieval and generation. At scale, Pinecone reports under 100ms acknowledgment, search visibility within seconds, and benchmarked performance across 138M embeddings, 2.8B vectors, and 65.2B+ vectors indexed in a single production deployment. The company says it serves more than 9,000 customers and 800,000 developers worldwide, with customers including OpenAI, Gong, The Washington Post, and ZoomInfo. The API-first platform is queryable through one API and includes API keys plus Admin APIs, with deployment options spanning serverless, dedicated, and BYOC on supported clouds.

Why use Pinecone?

• Object-storage-backed serverless architecture removes cluster management and lets storage and compute scale independently.
• Metadata filtering runs inline with search, which reduces post-processing and keeps relevance rules in the query path.
• Dedicated, serverless, and BYOC deployment options let teams match performance and compliance needs to their infrastructure.
• Security controls include SSO, RBAC, private endpoints, and customer-managed encryption keys for tighter access control.
• Enterprise reliability includes a 99.95% uptime SLA, backup and restore, and deletion protection for production workloads.

Who is Pinecone for?

• AI product teams who need retrieval infrastructure that scales without manual sharding.
• Platform engineers who want managed vector search with security and operational controls.
• Search and recommendations teams who need low-latency semantic, keyword, and hybrid retrieval.
• Developers building agents who need namespaces, filtering, and real-time indexing.
• Enterprise teams who need compliance, RBAC, and private networking for production AI.

What are Pinecone's key features?

What does Pinecone integrate with?

• Claude Code
• Cursor
• Copilot
• Codex
• Gemini
• CLI
• MCP
• SAML
• Slack
• AWS
• Azure
• Google Cloud

What are Pinecone's use cases?

How does Pinecone work?

1. Connect your first data source or API feed, then create an index in the Pinecone console. Choose the retrieval mode you need, such as Semantic search, Keyword search, Full-text search, or Hybrid search.
2. Ingest vectors and metadata through the API, then let Real-time indexing keep new content searchable quickly. Use namespaces and filtering to separate tenants, projects, or agent workflows without extra infrastructure.
3. Query the index from your app or agent and inspect results in Your indexes, always visible. Tune for Fast retrieval and Accurate results as you validate relevance against real user prompts.
4. Move production workloads onto Serverless or Bring your own cloud, then add Secure, Compliant, and Access controls settings. Enable Backup and restore, Deletion protection, and 99.95% uptime SLA for operational confidence.
5. Monitor usage and performance over time with Usage & Cost Management and built-in visibility. Scale indexes, regions, and teams as demand grows, while keeping retrieval Reliable and cost-efficient.

How much does Pinecone cost?

Frequently asked questions