Monitor Latency and Uptime for Your API Integration
An average response time of two seconds can still mean one in twenty users waits eight seconds. Latency is a distribution, so track percentiles rather than a single average.
What to measure 📊
| Metric | What it captures | Why it matters |
|---|---|---|
| Time to first token | How fast streaming starts | Perceived speed for chat UIs |
| p50 latency | The typical request | Baseline health |
| p95 / p99 latency | Slow tail requests | Where users give up |
| Error rate | Failed calls over time | Early warning of an outage |
| Uptime | Successful checks | Contract and trust |
Splitting latency by phase 🔍
Break each call into network time, time to first token and generation time. If the first token is fast but the full answer is slow, the model is simply verbose. If the first token is slow, the bottleneck is upstream.
Set meaningful alerts 🔔
- Alert on p95 latency, not the average
- Alert when the error rate crosses a small threshold
- Use a short evaluation window so you hear about outages quickly
- Route alerts to a human, not to a channel nobody reads
Test from where your users are 🌐
A check that runs from the same region as your server can look perfect while users on another continent suffer. Run at least one external probe from a different region so routing problems show up.
Frequently asked questions ❓
How often should I check uptime?
Every one to five minutes is enough for most products and catches problems before most users notice.
What is a good p95 target?
It depends on the model and the prompt. Measure your own baseline first, then alert on changes relative to it.
Should I retry failed checks immediately?
Retry once with a short delay to rule out a blip, but do not hammer an endpoint that is already struggling.
Do I need paid monitoring tools?
No. A scheduled script that stores results and a simple threshold check will catch most issues.
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