How to Build AI Products That Don't Bankrupt Your Business
About this session
Every AI builder is having the same conversation: our GenAI feature works, but the cloud bill is unsustainable. Model inference costs are the silent budget killer of enterprise AI initiatives, and most teams don't discover it until they are already in production.
This talk shares the real architecture patterns from an enterprise marketplace AI system that went from $3,200/month to $3/month - a 99.9% cost reduction without sacrificing accuracy or scale.
I’ll break down practical, production-tested patterns every AI builder needs: tiered inference routing (cheap heuristics first, expensive ML only when needed), hash-based change detection, on-demand model scheduling, multi-jurisdiction compliance architecture, and semantic caching for LLM workloads.
Real architecture diagrams. Real numbers. Zero slides-only theory.
Speaker
Key takeaways
- Learn about Dynamic Cost Optimizer framework
- How to translate the same patterns to LLM cost optimization in 2026-2027
- Common architectural patterns and best practices for reducing AI inference costs by 90%+ without sacrificing accuracy or scale