Redis vs Milvus: A comparison guide

Milvus and Redis represent two distinct architectural approaches to vector search, each with unique strengths and tradeoffs. Milvus, an open-source vector database developed by Zilliz and hosted under the LF AI & Data Foundation, offers a disaggregated, cloud-native architecture with separate components for ingestion, compaction, indexing, and query serving, which facilitates scalable compute independent of storage but adds operational complexity. It supports a variety of index types and is well-suited for workloads with complex ANN algorithm tuning needs. In contrast, Redis, known for its sub-millisecond response times and unified real-time data platform, integrates vector search, caching, streaming, and operational data into one system, reducing the overhead of managing separate databases and offering semantic caching natively through LangCache. Redis's architecture simplifies deployment and operational overhead, making it ideal for applications with mixed real-time operations, such as chatbots and AI agents, where vector similarity search is just one component. While Milvus allows fine-grained tuning for vector workloads at the cost of greater deployment complexity, Redis provides a consolidated infrastructure with fewer moving parts and built-in semantic caching, making it a compelling choice for many AI applications concerned with LLM cost reduction and operational simplicity.

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