Open source vector databases split into two camps: specialized stores that focus solely on vectors, and unified platforms that combine vector search with caching and operational data. The specialized tools—Milvus, Weaviate, Qdrant, Chroma, pgvector, Faiss—each optimize for scale, filtering, or ease of use, but force you to bolt on a cache and a primary database. Unified platforms like Redis remove those extra systems by handling vectors, session state, rate limiting counters, and feature data in one memory-first layer. Production readiness hinges on more than raw nearest-neighbor speed. You need a store that plays nicely with your RAG pipelines, enforces rate limits on chatbots, and serves real-time features to recommendation engines. That usually means juggling a vector store, a cache, and an operational DB—until you pick a unified option. Deployment style matters, too: some tools shine on Kubernetes, others come as managed cloud services, and a few give you full DIY control. Redis Search uses HNSW and FLAT indexes, hitting 90% precision at 200 ms median latency for top-100 neighbors under 50 concurrent queries and up to 66,000 inserts per second. Push for 95% precision, and you pay about 1.3 s latency. FT.HYBRID merges vector scores with filters on numbers, tags, text or geo, and new ranking algorithms like Reciprocal Rank Fusion show up in Redis 8.4. Redis Iris adds semantic caching—Redis LangCache in preview—caching LLM responses to cut inference calls by roughly 70% in some high-traffic setups. Milvus (Apache 2.0) offers HNSW, IVF, IVF-PQ and SCANN in a cloud-native, microservices design built for billion-vector workloads—if you can manage Kubernetes. Weaviate (BSD-3) tacks on native hybrid search and built-in embedding modules behind GraphQL or gRPC. Qdrant (Apache 2.0) emphasizes Rust-powered memory safety and heavy-filter support. Chroma (Apache 2.0) targets local dev and rapid prototyping in Python. Pgvector plugs into Postgres, and Faiss (MIT) gives you C++ libraries for fully custom infra. Each tool has its sweet spot based on scale, filtering needs, and deployment style.