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Vector databases are the backbone of RAG systems and semantic search. They store numerical representations (embeddings) of text, images, or other data, and find the most similar items using distance calculations.
Popular vector databases in 2026: Pinecone (managed, easiest to start), pgvector (PostgreSQL extension, free, great for existing Postgres users), Weaviate (open-source, feature-rich), Chroma (lightweight, good for prototyping), Qdrant (high-performance, Rust-based).
Key selection criteria: hosting (managed vs self-hosted), cost at scale, integration with your existing stack, filtering capabilities, and hybrid search (vector + keyword).
We use pgvector (PostgreSQL) for production RAG systems — it integrates with existing infrastructure, costs nothing extra, and handles millions of vectors efficiently. We help clients choose the right vector DB for their scale.
Our AI-First engineers build production systems using Vector Database technology. Talk to us.
Tell us about your project and we'll get back to you within 24 hours with a game plan.
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One senior engineer, AI-accelerated — owns architecture, security, and the last 20% AI tools leave broken. No recruitment, no ramp-up.
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