RAG, semantic search, embeddings — which vector DB fits?
Pinecone, Weaviate, Qdrant, Chroma, Milvus, pgvector — the vector database landscape is moving fast. We help you pick the right one.
Why this decision matters
Your vector database determines the quality and speed of your AI-powered search, chatbot, or recommendation system. The wrong choice means slow queries at scale, expensive re-indexing, or poor recall on similarity search.
What we evaluate
We look at hosting model (managed vs self-hosted), hybrid search support (vector + keyword), filtering capabilities, scalability, pricing per million vectors, and ecosystem integration with LangChain, LlamaIndex, and embedding providers.
Common mistakes
Using a full vector database when pgvector would suffice for your scale. Choosing managed-only when you need data sovereignty. Ignoring hybrid search capabilities when your users need both semantic and keyword matching.
Get a personalized recommendation
Tell us about your project — team size, existing stack, specific requirements — and we'll determine the right fit in under a minute.
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