Best practices
Monitoring prompt compression
Once compression is deployed, monitoring is essential to ensure quality is maintained and savings are realized. Here is the monitoring stack.
Key metrics to monitor
| Metric | What It Tells You | Alert Threshold |
|---|---|---|
| Compression ratio | How much is being removed | Below 30% or above 90% |
| Tokens saved per day | Cost impact | Sudden drop suggests compression failure |
| Average latency | Compression overhead | Above 500ms indicates issue |
| User satisfaction | Quality proxy | Statistically significant drop |
Frequently asked questions
What tools should I use for monitoring?
Any observability platform works. Log compression metrics alongside your LLM call logs.
How often should I review compression performance?
Daily for the first week after deployment, then weekly.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.