Milvus Cuts Memory Costs for Large-Scale RAG using Tiered Storage!

🚨 Stop loading all your vector data into memory. For many teams running AI systems in production, memory — not compute — is one of the biggest hidden costs. As data grows, loading everything into memory quickly becomes inefficient and expensive. In Milvus 2.6, we introduced tiered storage to solve this problem at the architecture level: • Hot data is cached locally for low-latency queries • Cold data stays in object storage (such as S3) without consuming memory • Multi-tenant and large-scale workloads become far more cost-efficient In our latest webinar, James explains how Milvus separates hot and cold data, why this matters for real-world RAG and agent workloads, and how teams can significantly reduce memory usage without sacrificing usability. ▶ Full Milvus 2.6 webinar replay: https://www.youtube.com/watch?v=Guct-UMK8lw?utm_source=linkedin ▼ ▽ JOIN THE COMMUNITY - MILVUS Discord channel Join this active community of Milvus users to get help, learn tips and tricks on how to use Milvus, or just get to be part of a vibrant community of smart developers! https://discord.com/invite/8uyFbECzPX ▶ CONNECT WITH US X: https://twitter.com/zilliz_universe LINKEDIN: https://www.linkedin.com/company/zilliz/ WEBSITE: https://zilliz.com/ PODCAST: https://creators.spotify.com/pod/show/chloe-williams8/episodes/Inside-the-AI-Agent-Revolution-e2ug0dh/a-abp1mtd