Running AI in production without going broke
Getting something working is the easy half. Running it every day, for thousands of people, fast enough that they stay and cheap enough that finance does not intervene, is the other half. It rests on one trade-off you will meet in every decision: quality, speed, and cost, pick two.
Estimate what an AI feature will cost to run at scale, and name three specific ways to bring that number down.
Lessons 1 to 4. The tokens and the two-phase request from lesson 1 do most of the work here.
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Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
Better answers need bigger models. Bigger models are slower and dearer. Every real decision is choosing where on that triangle a particular job belongs.
Everything about running AI in production comes back to one triangle, and you cannot have all three corners at once.
- Quality. Bigger models, and models that think before answering, give better results.
- Speed. Those same bigger models, longer prompts, and longer answers are slower. A chat or voice interface lives or dies on this. An overnight batch job does not care at all.
- Cost. You pay by the , so bigger models, longer contexts, more retrieved passages, multi-step loops, and hidden reasoning tokens all add up. At any real volume this stops being a rounding error.
The job is to place each piece of work at the right point on that triangle, rather than picking one model and using it for everything. A small fast model for sorting requests and answering simple questions. A frontier model only where the quality genuinely justifies it. A only for problems that actually have steps. Sending easy work to a cheap model and escalating only when needed is called routing or , and it is usually the single biggest cost saving available.
The rest of this lesson is part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Why is using one strong model for everything usually the wrong default?
The rest of this lesson
- Working out what it will actually cost
- Making it feel fast
- Keeping it running
- One worked example: a national contact centre
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