Why did my LLM API bill spike unexpectedly?
Look for code or configuration changes
A single line of code—like a retry loop without a backoff, or a missing cache—can multiply your API calls. If you recently deployed, compare the spike's start time with your deployment logs.
Also check if you accidentally switched to a more expensive model (e.g., from GPT-3.5 to GPT-4) or increased max tokens. These changes can dramatically increase costs.
Analyze usage patterns
Use your provider's dashboard to see which API keys, models, or endpoints are driving the increase. Often, a single feature or customer is responsible. If you use a proxy, you can drill down to individual users.
Look for unusual times—e.g., a spike at 3 AM could indicate a scheduled job or a bot. Also check for repeated identical requests, which might suggest a caching opportunity.
- Infinite loops or retries without limits
- Lack of caching for repeated queries
- Increased user traffic or a viral feature
- Switching to a higher-priced model
- Large batch jobs or data processing
- Testing in production with real API calls
Prevent future spikes
Implement rate limiting, caching, and budget alerts. Use a proxy to set hard limits per user or feature. Regularly review your code for inefficient API usage, and consider cheaper models for non-critical tasks.
Set up automated alerts so you're notified within hours, not days, of an anomaly.
Common mistakes
- Assuming the spike is due to a price increase when it's usually a usage change.
- Not checking for a bug that causes duplicate or unnecessary API calls.
- Ignoring the impact of output token length—longer responses cost more.
