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Tag: AI Infrastructure
Cloud, compute, and system infrastructure considerations for AI deployments.
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The real cost breakdown of running LLM apps on AWS
The part of your LLM bill you do not see in the demo The first time most teams see their real LLM bill is not a happy day. The token…
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Build vs Buy in AI: A Real Decision Framework That Holds Up in Production
The honest problem Most AI teams waste quarters arguing about build vs buy, then end up doing both in the worst way: they buy a black-box API and still build…
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The true cost of self‑hosting LLMs vs using APIs
The real bill usually arrives at p95 I keep seeing the same pattern: a team proves out a feature on an API, gets a scary bill, then someone says “we…
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GPU vs CPU for AI Workloads: The Real Cost-Performance Trade-offs
The painful question I get every quarter We are spending a fortune on GPUs. Can we move inference to CPUs and cut cost without blowing up latency? I have walked…
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Designing low latency AI for real time: what actually works
The real problem with “real time” AI Your p50 looks fine. Your users don’t care. They feel the p95. I’ve walked into teams with a neat demo, then watched the…
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LLM Latency In Production: What Actually Works
The spinner is lying to you If your LLM app shows a typing effect in under 300 ms but p95 completes at 6 to 10 seconds, users feel the lag….
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Why AI Costs Scale Nonlinearly And What To Do About It
The uncomfortable truth about scaling AI Your POC looks cheap. A few cents per request. Then you ship to 100k users, layer in retrieval, add tool use, tighten SLOs, and…
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