The cost, quality, latency, and utilization tradeoffs that shape model serving and the unit economics of AI features.
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27 articles in this tag.
A team has an open-weight model in production. The outputs are wrong in a specific, repeatable way. Someone says the words "fine-tuning run," and suddenly…
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The demo is no longer the hard part. The hard part is the fifth revision, when the client wants the same character, the same lighting, and one changed word…
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A feature that looks profitable on a per-call spreadsheet can lose money in production. The spreadsheet counts requests. Production counts attempts,…
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A capacity plan built from average QPS and a single latency target will survive the spreadsheet and die on the first traffic spike. The number was never…
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The prototype proved the idea. Now the bill decides whether the idea gets to exist.
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Two accelerators can sit within a few percent of each other on the spec sheet and produce very different monthly bills. The gap is not fraud, and it is not…
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The hard part was never the model call. It is everything you have to build around it.
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The expensive serving decision is the one you make before you have traffic data.
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The cluster is provisioned, the dashboards are green, and the inference bill is still climbing while latency drifts. That combination — healthy…
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A benchmark score is a result under agreed test conditions. Production reliability is what remains when your inputs, your missing fields, and your…
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A leaderboard rank tells you how a model performed under someone else's test conditions. It cannot tell you whether the model will survive yours.
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Peak FLOPS is the number everyone quotes and the number that predicts the least.
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A cascade is a deferral policy. If you cannot price the deferral, you are not engineering — you are gambling with a Grafana panel.
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Your dashboard is green. GPU utilization sits in a healthy band, average latency looks fine, and nobody has filed a complaint this week. Then the invoice…
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You have a model file, a GPU, and a demo that works on your laptop. Now answer the only question that matters: what breaks first when this leaves your…
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The model loads. The first prompt returns in two seconds. Then the context grows, a second request arrives, and the whole thing crawls.
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The demo ends the moment the model answers. The operations commitment begins the moment it answers twice, at 2 a.m., on a Tuesday, while the one engineer…
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A context window is a bigger desk, not a better memory. The desk still has to be loaded.
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A knowledge base outgrows the prompt, and two camps start shouting. One says the context window is finally big enough, so stop building retrieval…
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A modality is not a feature. It is an evidence channel with its own latency, cost, privacy surface, and failure modes.
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Your app was built around a string. A user types something, you send it to a model, you get a string back, you render it. One input type. One output type.…
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A prototype works on a hosted API, then someone says "let's just run it locally." That sentence usually bundles three different decisions into one, and…
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A model that fits in VRAM, loads in seconds, and answers instantly — then fails on the one task you actually needed.
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A retrieval system is a context-selection system. The model has a limited desk, and every irrelevant chunk takes space away from the evidence it actually…
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A team ships a feature on a frontier model. It works. Then the bill arrives, p95 latency drifts past the interactive threshold, and someone says the…
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A general model is the safest bet only while you are still discovering what the task is.
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A model can name every object in a video and still get the story wrong. That gap is the whole problem.
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