Monitoring and Controlling LLM API Costs in Java Production
Fast recall
Quick revision
Core answer
ChatResponse.getMetadata() which exposes Usage with prompt and completion token counts. Expose these as Micrometer Counter metrics pushed to Prometheus/Grafana. Control costs through: token budgets per request via ChatOptions.withMaxTokens(...), prompt caching where supported, model tiering (fast cheap model for simple tasks, larger for complex), and circuit breakers to stop runaway calls. Set cost alerts at the API provider level as a last line of defence.Whether the candidate treats LLM cost as a first-class production concern, not an afterthought
No per-request token tracking — discovering cost overruns at the monthly invoice rather than in real-time metrics
Discuss model tiering strategy, prompt caching for repeated context, and how to implement a token budget enforcement layer upstream of the ChatModel call
Interview-ready guide
Interview answer
LLM costs are driven by token usage (input + output tokens per call). Track them via ChatResponse.getMetadata() which exposes Usage with prompt and completion token counts. Expose these as Micrometer Counter metrics pushed to Prometheus/Grafana. Control costs through: token budgets per request via ChatOptions.withMaxTokens(...), prompt caching where supported, model tiering (fast cheap model for simple tasks, larger for complex), and circuit breakers to stop runaway calls. Set cost alerts at the API provider level as a last line of defence.
Monitoring and Controlling LLM API Costs in Java Production
Direct Answer
LLM costs are driven by token usage (input + output tokens per call). Track them via ChatResponse.getMetadata() which exposes Usage with prompt and completion token counts. Expose these as Micrometer Counter metrics pushed to Prometheus/Grafana. Control costs through: token budgets per request via ChatOptions.withMaxTokens(...), prompt caching where supported, model tiering (fast cheap model for simple tasks, larger for complex), and circuit breakers to stop runaway calls. Set cost alerts at the API provider level as a last line of defence.
Common Mistakes & Pitfalls
No per-request token tracking — discovering cost overruns at the monthly invoice rather than in real-time metrics
Check your understanding
Answer these without looking back at the article.
- How do you implement model tiering — routing simple tasks to a cheaper model automatically?
- How does prompt caching work and how do you structure prompts to maximise cache hits?
- How do you set a per-feature token budget that rejects requests above a threshold?
Looking for a structured roadmap?
Every module on this page is also part of the full Java backend interview track.