{"id":"pinecone-api","name":"Pinecone","homepage":"https://www.pinecone.io","repo_url":"https://github.com/pinecone-io/pinecone-python-client","category":"vector-database","subcategories":["vector-search","ai-infrastructure","semantic-search","rag"],"tags":["pinecone","vector-database","embeddings","semantic-search","rag","ai","machine-learning","rest-api","sdk"],"what_it_does":"Managed vector database for storing and querying high-dimensional embeddings at scale, enabling semantic search and retrieval-augmented generation (RAG) for AI applications.","use_cases":["Retrieval-augmented generation (RAG) — store document embeddings and retrieve semantically relevant chunks for LLM context","Semantic search over large text corpora with k-NN and ANN algorithms","Recommendation systems using embedding similarity","Long-term agent memory with semantic retrieval across sessions","Multimodal search over image and text embeddings"],"not_for":["Exact keyword search or BM25 ranking (use Elasticsearch or Typesense)","Relational or structured data queries (use a SQL database)","Small datasets where SQLite with pgvector suffices","Cost-sensitive workloads at high scale (vector DBs can be expensive)"],"best_when":"You need a fully managed, production-grade vector store for RAG or semantic search and want minimal infrastructure management with a developer-friendly API.","avoid_when":"Your retrieval needs are purely keyword-based, your dataset is tiny, or you need self-hosted open-source control.","alternatives":["qdrant-api","weaviate-api","chroma-api","elasticsearch-api"],"af_score":84.7,"security_score":null,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github"],"priority":"low","status":"evaluated","version_evaluated":"current","last_evaluated":"2026-03-01T09:50:06.078335+00:00","performance":{"latency_p50_ms":10,"latency_p99_ms":80,"uptime_sla_percent":99.95,"rate_limits":"Serverless: varies by plan; Standard: 1000 upserts/second, unlimited queries","data_source":"llm_estimated","measured_on":null}}