We added five LLM-powered features to our learner platform last year. Our monthly inference bill peaked at 11% of revenue, then dropped to 3.6% — without removing a single feature. Here is the playbook, in the order we'd apply it again.
Most teams cache the prompt. We cache the prompt + relevant model state. A 30-day cache with intelligent invalidation cut our token spend by 47% before we touched anything else. Hash on the part of the input that actually drives the answer, not the whole payload.
Build a tiny eval set per feature — 50 inputs with known good outputs. Test every cohort upgrade against it. Half our features run on Haiku at 1/15th the cost of the larger model, with zero quality regression. The eval set is the wedge.
For chat-shaped features, stream tokens and stop generating as soon as the user has the answer they need. Add a stop sequence aligned with how the UI actually consumes the output. Saved us another 18%.
Log every prompt, every response, every cost — and review the bottom-decile by quality every Friday. The bugs that drive cost are almost always quality bugs in disguise. Once you fix the quality, the cost falls out.
Full StackSix months ago we ripped out 14 REST endpoints. Here's the type-safety dividend, the cold-start gotcha, and the migration plan that didn't break a single learner login.
Data ScienceIf you can grok dot products, you can grok embeddings. Build a movie-recommender from scratch in 60 lines — no PyTorch, no TensorFlow, no jargon.
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