Pinecone is a managed vector database designed specifically for handling vector embeddings in machine learning applications, enabling efficient similarity search at scale. It provides a simple API for storing and querying vectors, making it easier to build and deploy AI-powered applications that require fast and accurate vector similarity matching, such as recommendation systems, image retrieval, and natural language processing tasks.
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Segment |
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Deployment | Cloud / SaaS / Web-Based |
Support | Chat, Email/Help Desk, FAQs/Forum, Knowledge Base |
Training | Documentation, Videos, Webinars |
Languages | English |
- Good documentation and usage examples - Easy-to-use Python SDK - Production-ready with low latency at our scale (10-20M vectors) - Good integration with the AI/LLM ecosystem
- did not find an easy way to export all vectors that we needed for data science/cleaning - will get expensive when hosting 100s of millions of vectors
We use Pinecone as a vector database for retrieval augmented generation using LLMs.
Ease of deployment! It takes just a few minutes to get an index set up and deployed.
The web-based API console could be improved, for example for experiments with metric (cosine vs dotproduct vs euclidean).
Storing embeddings for RAG.