Scaling Your AI Product
Growth exposes every shortcut you took. Fix them before they fix you.
Scaling an AI product is different from scaling traditional software. Your costs scale linearly with users, models change under your feet, and reliability becomes existential.
What you'll learn
- How to reduce AI costs without reducing quality
- Building reliability into AI-dependent systems
- When to move from APIs to self-hosted models
- Growing your team and your product without losing the soul
The AI Cost Curve
At 100 users, API costs are a rounding error. At 10,000 users, they're your biggest line item. At 100,000 users, they determine whether your business is viable. Every AI product hits a cost reckoning. Plan for it before it arrives.
Caching: Many users ask similar things. Cache frequent query patterns and serve identical results instantly. A smart cache can reduce API calls by 30-50% without any quality loss.
Tiered models: Not every query needs your best model. Route simple requests to cheaper, faster models. Use expensive models only for complex tasks. A routing layer that classifies query complexity before choosing a model can cut costs by 40%.
Prompt compression: Shorter prompts cost less. Audit your system prompts quarterly. Remove redundancy. Use examples efficiently. Compress context without losing quality. The difference between a 2,000-token and an 800-token system prompt compounds at scale.
Cost Reduction Playbook
Quick wins: Response caching, prompt compression, output length limits
Medium effort: Model routing (cheap model for simple queries), batch processing, embedding-based pre-filtering
Major investment: Self-hosted open-source models, fine-tuned smaller models, custom inference infrastructure
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