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Category: AI Cost Optimization
Strategies to reduce infrastructure and inference costs in AI systems, with real-world cost breakdowns, trade-offs, and optimization techniques.
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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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Token costs: what actually moves the needle in production
The real problem If your LLM bill surprised you last month, it probably was not the flashy features. It was the quiet stuff you never show the user: bloated system…
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Where Your AI Budget Quietly Leaks (and How to Plug It)
The quiet bleed Most AI invoices don’t explode. They bleed. A few extra tokens here, a lazy top_k there, a GPU pool idling at 6 percent because someone hard-coded min…
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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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