API Performance Monitoring: Latency, Throughput, and When to Care
Your API is technically 'up' — it returns HTTP 200 to every request. It's also taking 4 seconds to respond. Your users have already abandoned the page.
Not all slowness is worth waking up for. Learn which API performance metrics actually matter, how to set meaningful thresholds, and when latency becomes a real problem.
The metrics that actually matter
p95 response time — The response time for the 95th percentile of requests. Averages hide tail latency.
Error rate — The percentage of requests returning 4xx/5xx.
Throughput — Requests per minute. A sudden drop often indicates a problem upstream before error rates spike.
Setting meaningful thresholds
Run your monitoring for 2 weeks before setting thresholds. Then set: - Warning: 2× your normal p95 - Critical: 5× your normal p95 or your SLO limit
For a typical CRUD API with p95 of 150ms: warn at 300ms, alert at 750ms.
Endpoint-level vs aggregate monitoring
Aggregate API latency is nearly useless for diagnosis. Monitor critical paths individually: - Authentication endpoints - Your highest-traffic endpoints - Revenue-critical flows (checkout, upgrade)
Detecting gradual degradation
The most dangerous performance problems creep. A query that took 50ms in January takes 200ms in June because your data grew.
Set up weekly baseline snapshots and compare against 30-day averages.
Correlation with deployments
Always annotate your response time graphs with deploy events. If your p95 doubled on Thursday at 14:32 and you deployed at 14:30, you have your root cause.
Feature Guide
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AlertsDock gives teams uptime monitoring for websites, APIs, TCP checks, DNS checks, SSL expiry, and fast alert routing without enterprise overhead.
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