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TokenAtlas

How to Reduce AI Cost

Twelve levers, ranked by typical savings.

Highest-impact levers

1. Route to cheaper models per task (40–80% savings). 2. Enable prompt caching (50–90% off cached input). 3. Use Batch APIs for async work (50% off). 4. Compress system prompts.

Medium-impact levers

5. Cap output length. 6. Truncate context aggressively for RAG. 7. Switch to fine-tuned smaller models for repeated tasks. 8. Use embeddings for retrieval instead of stuffing context.

Operational levers

9. Set per-user token caps. 10. Log everything and audit weekly. 11. Negotiate enterprise commits above $50K/mo. 12. Re-run TokenAtlas scenarios monthly — prices change.

Frequently asked questions

What's the single biggest lever?
Model routing. Most teams over-use frontier models for tasks a small model would handle.
Is caching hard to implement?
OpenAI is implicit; Anthropic needs explicit breakpoints. Both are <1 day of engineering.
How do I prove savings?
Run a 7-day A/B and compare token spend per outcome.

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