Frontier Cost Optimization: Caching, Compression, And Fallback

When a frontier bill is too high, the levers that move the most are caching, compression, and fallback — usually in that order. Caching reuses prior compute. Compression sends fewer tokens. Fallback sends some traffic to a cheaper model.

Lever 1 — Prompt caching

• Vendors offer prompt cache discounts when the same prefix repeats — sometimes 80-90 percent off
• Structure prompts so the static parts come first and the variable parts last
• Cache a long system prompt or a corpus snippet that you reuse across many requests
• Measure your cache hit rate; aim for 70%+ on high-volume endpoints

Lever 2 — Prompt compression

• Strip redundant whitespace and verbose instructions
• Replace examples with rules where you can
• Summarize long context before sending — small models do this well
• Use structured output schemas to avoid 'please respond in JSON' boilerplate

Lever 3 — Model fallback

• Route easy tasks to a smaller model first
• Use a small model to detect when the task is hard, then escalate
• Run the cheap model in parallel with a confidence check, fall back to frontier on low confidence
• Cap the number of frontier calls per user per session

Applied exercise

1. 1Pull last month's frontier spend by endpoint
2. 2Pick the top two endpoints by cost
3. 3Apply caching to one and fallback to the other
4. 4Measure the new bill in 30 days. Repeat with the next two endpoints

The big idea: optimize the costliest endpoints with the cheapest lever that moves them. Repeat.