Pinecone
Context / RAG · Managed vector database for production-scale RAG and semantic code search
At a glance
| Setup effort | Medium |
| Released | 2021 |
| Open source | No |
| Hosting | Cloud |
| Privacy | Cloud index |
| Update mode | On-demand |
| Staleness | manual |
| Index type | Embeddings |
| Index limit | Large |
| Capabilities | Embedding-based search, Vector storage, API, Serverless, Namespaces, Metadata filtering |
What Pinecone does
Embedding-based search, Vector storage, API, Serverless, Namespaces, Metadata filtering
Best for
Managed vector database for production-scale RAG and semantic code search
Works well with
LLM Provider / Model
Integration
Agent / Orchestration
Conflicts & caveats
- Privacy conflict: Self-hosted Llama 3 (Ollama/Groq) sends code to cloud Pinecone. Use local context (Continue indexing, ChromaDB, LanceDB, pgvector, Vespa self-hosted) for true privacy.
- ⚠️ SWE-agent with on-demand context "Pinecone" may act on stale code — prefer real-time context (Cursor @codebase, Greptile, GitHub Copilot indexing, Augment Context, CocoIndex, turbopuffer).
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