标签：#opensearch

I was on a call last month with a startup CTO who had just gotten their AWS bill. They had built a beautiful RAG application: semantic search, conversational AI, the works. Their vector index was humming along with about 50 million embeddings. Then they hit product-market fit. Within six weeks, they scaled to 500 million vectors. Their monthly infrastructure costs went from $2,000 to $20,000. The real kicker? When we looked at the access patterns, over 80% of those vectors were queried less than once a week. They were paying hot-storage prices for data that was, by any honest measure, cold. The standard advice here is "just use a cheaper vector database." The more interesting question is: why are you storing all your vectors at the same temperature in the first place? The Cost-Recall-Latency Triangle Vector search forces a three-way tradeoff. You can optimize for cost, recall, and latency, but you only get to pick two. Want high recall and low latency? That costs money (in-memory HNSW graphs with full-precision vectors eating RAM). Want high recall at low cost? Latency goes up. Want cheap and fast? Recall suffers. Most teams pick a single point on this triangle and apply it uniformly to every vector in their index. That decision made sense when vector databases offered a single storage tier. It makes the same amount of sense as storing your entire filesystem on NVMe SSDs because some files need fast access. The conventional wisdom says you pick your point on the triangle and live with it. But the conventional wisdom was written before vector storage got interesting. The better approach: tier your vectors the same way you already tier your storage. Different access patterns deserve different economics. The same embedding that costs $0.12/month in RAM might cost $0.004/month on disk and $0.0002/month in object storage. When you have 500 million of them, those decimals matter. The Hot Tier: In-Memory HNSW and Exact k-NN For vectors that get hit constantly (your user-fa

Uber explained how it keeps its OpenSearch deployments running during a zone outage. It does this by using OpenSearch's built-in shard allocation and its own isolation-group system, which relies on the Odin container orchestration platform. This way, it maintains both query and ingestion capabilities. By Claudio Masolo

Amazon Web Services has recently announced the general availability of the next generation of Amazon OpenSearch Serverless, with a redesigned architecture that enables 20 times faster resource provisioning than the previous serverless architecture, true scale-to-zero capability, and up to 60% lower cost than a provisioned cluster for peak loads. By Gianmarco Nalin

As AI agents move from experiments to production, AWS, Cloudflare, and others are redesigning cloud infrastructure for a future dominated by machine-generated internet traffic instead of human users.