How to Monitor LLMs in Production
Most teams log LLM calls and call it monitoring. Real production monitoring has 5 levels, from basic logs to AI-powered optimization. Here is how to get there.
You shipped your AI feature. It works in staging. Users are hitting it. Now what?
Most teams add console.log and call it monitoring. That works until your LLM costs spike 300% overnight, response quality silently degrades, and users leave without telling you why.
Real LLM monitoring has five levels. Most teams never get past level two.
Level 1: Basic Logging
What: Log every LLM call: input, output, model, and timestamp.
You get: A record of what happened. If something breaks, you can search logs and find it.
You miss: Everything quantitative. You know GPT-4 was called 10,000 times yesterday, but not how much that cost, how long it took, or whether users found the responses useful.
Tools at this level: Application logs, CloudWatch, Datadog basic.
Almost every team starts here. The problem is that many stay here.
Level 2: Metrics
What: Track quantitative measurements: token counts, latency (P50/P95/P99), error rates, cost per call, and model distribution.
You get: Dashboards. You can see trends, set alerts, and identify anomalies. "GPT-4 P95 latency increased 40% this week."
You miss: Context. You know latency spiked, but not why. You know costs are up, but not which workflow step or which user cohort is driving it.
Tools at this level: Prometheus + Grafana, Langfuse basic, Helicone.
This is where most "AI observability" tools operate. It's necessary but insufficient.
Level 3: Distributed Traces
What: Trace the full lifecycle of a request across multiple services and model calls. Each trace shows the complete journey: API request → preprocessing → model call → postprocessing → response.
You get: Cause and effect. "This request was slow because the RAG retrieval step took 3 seconds, not the LLM call." You can see where time is spent, which model was chosen, and how each step contributed to the final output.
You miss: The user perspective. Traces show what your system did. They don't show what your user experienced or whether the response was useful.
Tools at this level: OpenTelemetry, Jaeger, Arize Phoenix, Langfuse traces.
Level 3 is where engineering teams start to feel confident. But there's a gap between system performance and user satisfaction that traces don't bridge.
Level 4: User Behavior Correlation
What: Connect LLM performance data with user behavior. Did the user accept the AI suggestion? Did they retry? Did they leave? Did response time affect conversion?
You get: The missing link. "Users who get RAG responses in under 2 seconds have 3x higher task completion rates." "Users who trigger the GPT-4 fallback path report higher satisfaction than those who get the Mistral fast path."
You miss: What to actually do about it. You have the data. You need the recommendations.
Tools at this level: Custom analytics pipelines (usually PostHog + Langfuse + custom glue code). Very few teams build this, because it requires unifying two different data systems.
This is where the insight quality jumps dramatically. But almost nobody gets here because it requires bridging AI metrics and product analytics, two systems that traditionally don't talk to each other.
Level 5: AI-Powered Optimization
What: Use AI to analyze your AI. Automatically detect anomalies, suggest model swaps, recommend routing changes, and identify optimization opportunities.
You get: Actionable intelligence. "Switch Step 3 from GPT-4 to Llama-3-70B for queries with confidence > 0.8. Estimated savings: $2,400/month with less than 1% quality degradation." "Users in the 'enterprise' cohort need P95 latency under 1.5s, but the current P95 is 2.1s. Recommend adding a caching layer for repeated queries."
What makes this different: Levels 1-4 give you data and dashboards. Level 5 gives you recommendations and predicted outcomes. You spend less time staring at graphs and more time making decisions.
Tools at this level: Almost nothing on the market does this comprehensively.
The Monitoring Stack Problem
Here's what a Level 4-5 monitoring stack looks like if you build it yourself:
| Layer | Tool | Cost | |-------|------|------| | Logging | ELK Stack / Datadog | $$-$$$$ | | Metrics | Prometheus + Grafana | $ (self-hosted) | | Traces | OpenTelemetry + Jaeger | $ (self-hosted) | | LLM-specific metrics | Langfuse / Helicone | $$-$$$ | | User analytics | PostHog / Mixpanel | $$-$$$ | | Correlation layer | Custom code | Engineering time | | AI recommendations | Custom ML pipeline | Significant engineering time |
That's 5-7 tools, 3-4 data pipelines, and weeks of integration work. And the correlation between AI metrics and user behavior? That's the custom code nobody has time to build.
The Unified Approach
The reason most teams stay at Level 2 isn't laziness; it's fragmentation. Each level requires different tools, different data formats, and different expertise. Unifying them is an engineering project in itself.
Sinapsis AI collapses all five levels into one platform:
- Levels 1-2: Built-in logging and metrics for every model call, with per-step cost tracking
- Level 3: OpenTelemetry-native distributed tracing across workflows
- Level 4: User behavior analytics (heatmaps, funnels, session replays) correlated with AI performance data
- Level 5: AI-powered recommendations, including model swap suggestions, routing optimization, and cost reduction opportunities
No tool sprawl. No custom correlation pipelines. No weeks of integration.
Getting Started
If you're at Level 1-2, here's the practical path forward:
- Add per-step cost tracking. This alone changes how teams think about AI spending.
- Implement distributed traces. OpenTelemetry is the standard. Instrument your AI calls.
- Connect user behavior. Start with basics: did the user accept/reject the AI output?
- Automate insights. Once you have data from levels 1-4, use it to drive recommendations.
Or use a platform that gives you all five levels from day one.
Monitor what your models do. Monitor what your users do. Connect the two.