AI DevelopmentThe AI Cornerby Ruben Dominguez
Inference engineering is the 80% cost cut most teams miss
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“Two teams ship the same AI feature, on the same model, with the same prompt, and the results split hard. One product replies the instant you hit enter and costs pennies to run. The other stutters through every response and bleeds money month after month.”
Key takeaways
- Inference optimization splits into prefill (compute-bound, reads entire prompt) and decode (memory-bound, writes tokens sequentially) - understanding this split is fundamental to cost and latency control
- Prefix caching effectiveness depends entirely on prompt structure - most teams get zero savings because they don't architect prompts for cache reuse
- Build-versus-buy decision for AI inference has concrete crossover points based on volume, compliance requirements, and workload characteristics - not just cost math
Why this matters for operators: Companies building AI products facing unexpected inference costs and latency issues
I cover AI×GTM intelligence like this every Wednesday.
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This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.