Choosing the Right Vector Database for RAG and AI Applications

Short summary

Vector databases are critical infrastructure for RAG and semantic search applications, enabling efficient storage and retrieval of high-dimensional embeddings at scale. This guide evaluates databases across performance, scalability, cost, and developer experience dimensions. Selection depends on your specific application requirements, expected scale, and infrastructure constraints.

• •Vector databases store and retrieve high-dimensional embeddings for RAG and semantic search
• •Key evaluation criteria: performance, scalability, cost, developer experience
• •Choice should align with application scale, requirements, and deployment environment

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