What forces LLM teams to optimize inference when going from MVP to production?
Ok_Philosophy_4031 · reddit · 2026-09-05
A team building in inference optimization asks what actually forces LLM products to rework their stack beyond MVP. Recurring patterns: production traffic dominated by repeated narrow tasks; bills growing despite 50% price cuts as usage, retries and agent loops balloon; and 1-2% failure rates becoming painful once retries, human review or broken downstream workflows compound. They seek data points on scale, the first forcing factor (cost/latency/reliability/vendor lock-in) and what changed (smaller models, caching, routing, fine-tuning, batching), plus counterexamples of teams happily staying on frontier models.
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