Vector DBRAGPineconeWeaviateQdrantAIDatabase
Vector Databases for RAG: Pinecone vs Weaviate vs Qdrant
Why Vector Databases?
Vector databases store embeddings to enable semantic search. They power RAG pipelines by retrieving context relevant to a user query.
Comparison at a Glance
- Pinecone: managed, high-performance, multi-tenant, pay-as-you-go
- Weaviate: open-source + cloud, modular schema, hybrid search
- Qdrant: open-source + cloud, strong filtering and payload indexing
Key Capabilities
Look for HNSW/IVF indexes, hybrid search (sparse+dense), filters, metadata payloads, sharding/replication, and streaming updates.
Operational Considerations
- Throughput/latency under concurrent RAG loads
- Cost vs scale (dimension count, replicas, persistence)
- Backups, durability, multi-region
- SDKs and ecosystem integration (LangChain, LlamaIndex)
Recommendation
For enterprise managed simplicity choose Pinecone. For open-source flexibility with strong hybrid search choose Weaviate. For cost-efficient, filter-heavy workloads choose Qdrant.