Audits token usage, model selection, caching strategy, and prompt compression to prevent runaway inference costs as AI features scale. This is especially important for high-volume agentic workflows where repeated calls compound quickly, and where the gap between a well-optimized and a careless implementation can be orders of magnitude in cost.
Use cases
- A high-volume API endpoint that calls an LLM on every request and is approaching a significant billing threshold
- An agentic workflow where the same context is re-sent on every step of a multi-step conversation, multiplying token costs
- Evaluating whether to fine-tune a smaller model for a specific task versus continuing to use a large general-purpose model
- A product team that wants to add AI features but is uncertain about the cost implications and needs a cost model
- Auditing an existing AI feature that has been running for 90 days and understanding the actual token consumption patterns
Key features
- Log token usage per feature, per user session, and per model variant to establish a cost baseline before optimizing
- Identify the top token consumers: often these are the longest prompts, the highest-frequency calls, or the most expensive models being used where cheaper ones would suffice
- Apply prompt compression techniques: remove redundant context, use concise instructions, and leverage system-level caching where model responses can be reused
- Benchmark cheaper models on non-critical task paths and measure whether quality is acceptable for the specific use case—often 80% of calls can move to a cheaper model with negligible quality loss
- Implement semantic caching to avoid re-issuing semantically equivalent queries that were recently answered at lower cost
When to Use This Skill
- When AI feature costs are approaching budget thresholds and you need to understand where tokens are being consumed
- When designing new AI features and wanting to make model selection decisions based on cost-efficiency data
- When an agentic workflow is suspected of having token waste due to repeated context re-sending
Expected Output
A cost audit report with per-feature token breakdowns, identified optimization opportunities, and a cost model for proposed changes.
Frequently Asked Questions
- What is the biggest source of unexpected AI cost overruns?
- Agent loops—scenarios where an agent repeatedly calls the model without converging on an answer. Implement max-turn limits, result caching, and early stopping conditions to prevent runaway loop costs.
- How do I decide between a cheaper model and a more expensive one?
- Run an evaluation: measure the quality difference on your specific task using your evaluation harness. If the cheaper model is within 5% of the expensive model on your task metrics, use the cheaper one for that task. Route tasks to the most cost-effective model that meets your quality bar.
- Does caching really make a meaningful difference?
- Yes—semantic caching can reduce costs by 30-70% in retrieval-augmented workflows where similar queries repeat. Exact-match caching has even higher hit rates for deterministic use cases. Measure your cache hit rate and estimate the savings before dismissing caching as premature optimization.
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