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Introduction
Your AI model is in production. How do you know it is working? Scale AI monitoring answers that question. This post covers key metrics, alerting, and observability tools. You will learn to catch problems before users notice.
Why Monitoring Matters
At scale, things break constantly. Models degrade. Data changes. Infrastructure fails. Without monitoring, you are flying blind.
Consequences of poor monitoring:
- Slow responses drive users away
- Wrong answers damage trust
- Cost spikes ruin budgets
- Security breaches go unnoticed
For infrastructure basics, see scale AI infrastructure.
Metrics to Monitor
System metrics:
- Latency (p50, p95, p99)
- Requests per second
- Error rate (HTTP 5xx, timeouts)
- GPU/CPU utilization
Model metrics:
- Prediction confidence
- Distribution of inputs (drift)
- Accuracy on labeled samples (if available)
- Hallucination rate (for LLMs)
Business metrics:
- User satisfaction (thumbs up/down)
- Task completion rate
- Cost per request
For LLM-specific metrics, see GPT-3 limitations.
Setting Alerts
Do not monitor everything. Alert only on actionable issues.
Critical alerts (page someone):
- Error rate > 5% for 5 minutes
- Latency p99 > 5 seconds
- Model confidence drops below threshold
Warning alerts (email):
- GPU utilization > 90% for 1 hour
- Cost per request up 20% week-over-week
- Data drift detected
Use tools like PagerDuty or Opsgenie for on-call rotations.
Model Drift Detection
Models become less accurate over time. Data changes. This is drift.
Types of drift:
- Data drift – Input distribution changes. Example: New user demographics.
- Concept drift – Relationship between input and output changes. Example: Pandemic changes shopping behavior.
- Label drift – Ground truth changes over time.
Detect drift using statistical tests (Kolmogorov-Smirnov, KL divergence). Retrain when drift is significant.
Observability vs Monitoring
Monitoring tells you what is broken. Observability tells you why.
Observability means you can explore:
- Which specific inputs cause errors?
- Why did latency spike at 3 PM?
- Which model version is misbehaving?
Achieve observability with structured logging, distributed tracing, and rich dashboards.
Tools: Prometheus + Grafana, Datadog, New Relic, Honeycomb.
For chatbot monitoring, see chatbot AI guide.
Real-World Monitoring Example
Scenario: E-commerce recommendation API.
Monitored metrics:
- 10,000 requests/minute
- p95 latency: 200ms (normal)
- Error rate: 0.1% (normal)
Alert triggers:
- p95 > 500ms for 2 minutes → page on-call
- Error rate > 2% → page on-call
- Cost per request > $0.01 → email warning
When an alert fires, check logs and traces to find root cause.
For cost metrics, see scale AI cost optimization.
Logging Best Practices
Log everything. But log smartly.
What to log:
- Request ID (for tracing)
- User ID (anonymized)
- Input prompt (truncated)
- Model output (truncated)
- Latency
- Model version
What not to log:
- Passwords or API keys
- Raw personal data (unless encrypted)
- Full model weights
Store logs for 30–90 days depending on compliance needs.
Dashboard Examples
Create dashboards for different audiences:
| Audience | Dashboard Focus |
|---|---|
| Executives | Cost, user satisfaction, uptime |
| Engineers | Latency, error rate, GPU usage |
| Data scientists | Drift, accuracy, confidence |
Use Grafana or Datadog to build visualizations.
FAQ
1. How often should I check monitoring?
Automated alerts should page you immediately. Check dashboards daily.
2. What is a good error rate for AI systems?
Aim for <1% for most tasks. Medical or financial systems need <0.1%.
3. Can I monitor without paying for tools?
Yes. Prometheus and Grafana are free and open source. However, you need to host them.
4. Where can I learn more?
Return to scale AI guide.
Conclusion
Scale AI monitoring is essential. Track latency, error rates, cost, and model drift. Set alerts for critical issues. Use observability tools to debug. Review dashboards daily. Good monitoring prevents outages and saves money.
