Technical architecture diagram illustrating three distinct vector database approaches converging toward a central AI neural network hub. The left pathway represents Pinecone's managed cloud architecture with floating servers and API connections symbolizing infrastructure abstraction. The center pathway shows Milvus's distributed architecture with Kubernetes containers, microservices components, and scalable cluster imagery representing high-performance computing. The right pathway depicts pgvector's PostgreSQL integration with traditional database icons, SQL symbols, and integration layers showing the evolutionary database enhancement approach. The three architectural paths converge toward a central artificial intelligence brain visualization, emphasizing how each solution enables AI-powered applications through different technical philosophies. The illustration uses a modern technology color palette of blues, teals, and orange accents on a clean white background with an isometric perspective to convey technical sophistication and clarity for software engineering audiences.

Vector Database Architecture Comparison: Pinecone vs Milvus vs pgvector

2025 guide to choosing Pinecone, Milvus, or pgvector for production vector databases. Performance benchmarks, cost analysis, migration strategies, and decision frameworks for AI infrastructure teams.

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Vector Databases Demystified: Choosing Between Pinecone, Milvus, and pgvector for Production AI Workloads