How do I monitor API usage spikes across endpoints?

To monitor API usage spikes across endpoints, focus on rate, latency, errors, and unusual distribution changes per endpoint.

1) Track the right metrics per endpoint

At minimum, collect these dimensions for each endpoint:

• Request count / RPS: total traffic over time
• Latency: p50, p95, p99
• Error rate: 4xx, 5xx, timeouts
• Payload size: request/response bytes
• Auth/user/client ID: who is generating traffic
• Status by route/method: e.g. GET /v1/orders

2) Baseline normal behavior

Spikes are only meaningful relative to a baseline:

• Compare current traffic to the same time last week
• Use rolling averages and percentiles
• Maintain per-endpoint baselines, because each endpoint has different normal patterns

3) Detect anomalies

Common ways to identify spikes:

• Threshold alerts: e.g. traffic > 2x normal for 5 minutes
• Percent change alerts: endpoint traffic up > 50% vs baseline
• Statistical anomaly detection: z-score, EWMA, seasonal decomposition
• Top-N changes: alert when an endpoint jumps into the top traffic consumers

4) Segment by endpoint and caller

A spike may be localized:

• One endpoint only
• One tenant/customer
• One API key or IP
• One region
• One method (POST spike vs GET)

This helps distinguish:

• legitimate product growth
• noisy client retries
• abuse / scraping
• bug-induced loops

5) Build dashboards

Create dashboards with:

• Total traffic and per-endpoint traffic
• Top endpoints by absolute volume
• Top endpoints by growth rate
• Error and latency overlays
• Heatmaps by hour/day
• Breakdown by client/IP/tenant

6) Alert on multiple symptoms

A usage spike is more actionable when paired with other signals:

• traffic spike + latency spike
• traffic spike + 5xx increase
• traffic spike + one client dominating traffic

7) Use logs, metrics, and traces together

• Metrics tell you something changed
• Logs show which requests changed
• Traces help identify downstream bottlenecks

8) Practical tooling

Common setups:

• Prometheus + Grafana + Alertmanager
• Datadog
• New Relic
• CloudWatch + dashboards/alarms
• OpenTelemetry for instrumentation

9) Example alert rule

A useful rule might be:

• Alert if requests per minute for any endpoint is > 3 standard deviations above the 24h baseline for 10 minutes
• Or if one endpoint exceeds 30% of total API traffic suddenly

10) Watch for false positives

Exclude or handle:

• deploys / batch jobs / cron traffic
• retries from clients
• known marketing campaigns
• timezone/seasonality effects

If you want, I can also give you:

1. a Prometheus/Grafana setup,
2. a Datadog monitor example, or
3. a SQL/query approach for detecting endpoint spikes.