Monitoring AI-Driven Applications: What to Track and Why

You can't fix what you can't see. And with AI-driven applications, there's a whole category of things you're probably not seeing.

Traditional monitoring covers the basics. Error rates. Response times. CPU usage. Uptime. These metrics tell you whether your servers are alive and whether your code is crashing. They've been table stakes for a decade.

But AI applications have a second layer of complexity. Your API might return 200 OK while the AI generates complete nonsense. Your response time might be acceptable while the token cost of that response bankrupts your monthly budget. Your error rate might be zero while the AI confidently gives users dangerously wrong information.

Standard monitoring misses all of this.

The Monitoring Stack You Actually Need

Start with the foundation. Sentry or a similar error tracking tool captures exceptions with full context. Stack traces, user actions that led to the error, browser environment, request payload. When something breaks, you need to reproduce it in minutes, not hours.

Performance monitoring tracks Core Web Vitals. Largest Contentful Paint, First Input Delay, Cumulative Layout Shift. These metrics directly correlate with user satisfaction and search ranking. If your LCP is above 2.5 seconds, users are leaving before your page fully renders.

Application Performance Monitoring (APM) gives you the server-side picture. Database query durations, external API call latencies, memory allocation patterns. When a page is slow, APM tells you which database query or API call is the bottleneck.

All of this is standard. This is the monitoring you'd set up for any application. AI applications need everything above plus a dedicated AI observability layer.

AI-Specific Metrics That Matter

Token usage per interaction is your cost metric. Every AI call consumes tokens, and tokens cost money. Track input tokens and output tokens separately, because they're priced differently. Aggregate by feature, by user segment, and by time period. This data tells you which features are expensive, which users are heavy consumers, and whether your costs are trending up or down.

Response latency for AI features deserves its own tracking. AI calls are inherently slower than database queries. Your users tolerate this if the result is valuable, but tolerance has limits. Track the P50, P95, and P99 latencies for every AI-powered feature. If your P95 exceeds three seconds, you're losing users.

Response quality is the hardest metric to track but the most important. Are the AI outputs actually good? You can measure this indirectly through user behavior. Do users accept the AI's suggestion or override it? Do users complete the flow that follows the AI output? Do users come back to the feature?

Hallucination detection requires structured validation. If your AI generates JSON, validate the schema. If it generates URLs, verify they resolve. If it references your product's features, check that those features exist. Automated validation catches the most egregious hallucinations before users see them.

The Cost Optimization Conversation

Here's a conversation I've had with every team that ships AI features: "Our AI costs are 10x what we budgeted."

It always happens. And it always has the same root causes.

No caching. The same prompt with the same context generates the same response, but you're paying for it every time. Implement a semantic cache that stores AI responses keyed by the input hash. Cache hit rates of 30-50% are common, and each cache hit saves the full token cost.

Wrong model for the task. You're using GPT-4 to classify support tickets when a fine-tuned small model would be faster, cheaper, and more accurate. Not every AI task needs the most capable model. Match the model to the task complexity.

Wasteful context. You're stuffing the entire user profile, conversation history, and product catalog into every prompt. Most of that context is irrelevant to the specific task. Trim your context to what's actually needed and your token costs drop significantly.

No batching. You're making individual AI calls for each item when you could batch twenty items into a single call. Batching reduces overhead, improves throughput, and often produces better results because the model has more context about the overall pattern.

AI agents implement these optimizations systematically. They analyze your token usage, identify waste, and restructure your AI calls for efficiency. Teams typically see 50-70% cost reduction after optimization without any degradation in output quality.

Alerting That Doesn't Cry Wolf

Bad alerting is worse than no alerting. If your team ignores alerts because 90% of them are false positives, the real alert gets lost in the noise.

Set alerts on rates of change, not absolute values. A sudden 3x increase in error rate is meaningful regardless of whether your baseline is 0.1% or 1%. An error rate of 1.5% might be normal for your application, so a static threshold at 1% would fire constantly.

Alert on business metrics, not just technical metrics. If your checkout conversion rate drops 20% in an hour, that's a production incident even if your error rate is zero. The AI might be generating confusing product descriptions or the recommendation engine might be surfacing irrelevant results.

Page the right person. AI-specific incidents need someone who understands the AI stack, not just the infrastructure. Route AI quality alerts to the ML team and infrastructure alerts to the platform team.

Build the Dashboard First

Before you ship AI features to users, build the monitoring dashboard. See every AI call, its cost, its latency, and its output. Watch the dashboard as real users interact with the feature. You'll catch issues in the first hour that would have taken weeks to surface through support tickets.

Monitoring is not overhead. It's the difference between operating a system and hoping a system works.