LLM monitoring: Why your AI can be up while completely failing

Your LLM responds in 200 milliseconds. Error rate is zero. Throughput is perfect.

Users are getting completely useless outputs.

This is the silent failure mode that traditional monitoring misses entirely. Deloitte warns that more than 40% of agentic AI projects could be cancelled by 2027 due to unanticipated cost, complexity, or unexpected risks. Most teams discover quality problems weeks after deployment when user complaints spike. By then, the damage is done.

LLM monitoring and observability requires a different approach than traditional application monitoring. You’re not just checking if the system is up. You’re verifying that it delivers actual value to users.

Why uptime monitoring misses LLM failures

Traditional monitoring answers one question: Is the system responding?

For web servers, databases, and APIs, this makes sense. If the system responds within acceptable latency and returns data without errors, it’s probably working.

LLMs break this assumption completely.

Datadog’s LLM Observability platform now provides end-to-end tracing across AI agents, structured experiments, and quality evaluations. But teams using only traditional metrics still miss critical quality degradation in production systems. The API responded fine. Latency was normal. Error rates were low.

The model was hallucinating customer data and mixing up user contexts.

Your monitoring dashboard showed green. Your users were furious.

This happens because LLM outputs are non-deterministic. The same prompt can produce different responses. Some perfectly useful. Some completely wrong. The system never throws an error for bad outputs.

The math gets worse with multi-step workflows. Research on agent reliability shows that error rates compound exponentially: 95% reliability per step yields only 36% success over 20 steps. Best AI agents achieve goal completion rates below 55% with CRM systems.

Running Tallyfy’s AI features taught me that the most dangerous failures are the ones that look healthy on dashboards. A customer support bot confidently giving wrong information does more damage than one that times out.

What to actually monitor

Effective LLM monitoring and observability tracks three layers simultaneously: technical performance, output quality, and user satisfaction. 89% of agent teams have now implemented observability, outpacing evaluation adoption at 52%.

The critical primitives for LLMOps are tracing (logging inputs, outputs, and intermediate steps), evaluation (LLM-as-a-judge and human feedback), and prompt management (versioning and testing). The most effective stacks prioritize traceability - the ability to link a specific evaluation score back to the exact version of the prompt, model, and dataset that produced it.

Technical metrics you probably already track. Latency, throughput, token usage, API costs. These matter for operational reasons, but they tell you nothing about whether the AI is helping users.

Output quality metrics require more work. Hallucination detection systems check if responses contradict the provided context. MLflow 3.0’s LLM-as-a-Judge evaluators now provide research-backed automated assessment of factuality, groundedness, and retrieval relevance. Coherence scores measure if outputs make sense. Toxicity filters catch inappropriate language.

But what actually predicts problems: user behavior.

When users repeatedly regenerate responses, they are telling you the first output was useless. When they immediately close the session after receiving an answer, task completion failed. When they switch back to manual workflows, the AI stopped adding value.

Research on production LLM monitoring shows these behavioral signals detect quality degradation faster than automated metrics. Key metrics to track include trajectory quality (evaluating action sequences reveals inefficient patterns), hallucination rate, latency distributions across multi-step workflows, and task completion success rates. A 15% drop in task completion rates over three days signals systematic issues, not random variation.

Track completion rates by task type. Measure time from AI response to user action. Count how often users ignore or override AI suggestions. These patterns reveal quality shifts before users complain.

One metric we found critical: edit distance between AI output and what users actually used. Small edits mean helpful suggestions. Complete rewrites mean wasted time.

Building monitoring that catches real problems

Start with human review, not just automation.

Sample random AI outputs daily. Have someone who understands the task evaluate them. Does this response actually help? Would you use this yourself? Is anything factually wrong?

Industry research on LLM evaluation confirms that human review remains the gold standard for quality assessment. By end of 2026, evaluation platforms will evolve from niche utilities into core infrastructure, moving toward multi-agent evaluations that simulate dynamic interactions. Automated metrics complement human review, they don’t replace it.

For technical monitoring, track token-level latency (how fast the model generates), model-level throughput (batch performance), and application-level response time (what users experience). Langfuse, the most-used open-source LLM observability tool with 7M+ monthly SDK installs and 8,000+ monthly active self-hosted instances, shows all three matter because they fail independently.

Cost tracking becomes critical in production. Helicone, which has processed over 2 billion LLM interactions, maintains a 300+ model cost database and provides built-in caching that reduces API costs 20-30%. Modern LLM observability platforms track token consumption at the request level, letting you spot expensive prompts and inefficient API usage before the bill arrives.

Set up graduated thresholds rather than binary alerts. Warning at 70% of your critical threshold. Alert at 90%. Critical escalation at 100%. This gives you time to investigate before users are affected.

When and how to alert

LLMs produce variable outputs by design. Alerting on single bad responses creates noise that teams ignore.

Alert on patterns, not points.

One hallucinated response? Log it. Three hallucinations in the same user session? Warning. Hallucination rate above 5% for an hour? Page someone.

Google’s Vertex AI monitoring approach tracks drift and quality metrics over time windows, triggering alerts when trends exceed thresholds rather than reacting to individual events. Arize Phoenix, with 7,800+ GitHub stars, provides OpenTelemetry-native observability with advanced drift detection for embeddings and LLM outputs.

For user satisfaction, correlate AI quality metrics with business outcomes. If task completion rates drop 20% while hallucination detection shows no issues, your quality metrics are measuring the wrong thing.

Response time matters differently for LLMs than traditional apps. Users tolerate 3-5 seconds for AI responses but abandon after 10 seconds. Set your latency alerts based on user behavior data, not arbitrary thresholds.

The alert that matters most: sustained quality degradation. If your 7-day rolling average for any quality metric drops below baseline, investigate immediately. New Relic’s AI Monitoring shows 30% quarter-over-quarter growth in adoption, with features like Agent Service Maps providing complete views of all interactions between AI agents and AI Trace Views showing every step including agent calls, latency, and errors.

Start small, scale systematically

You don’t need a full observability platform on day one.

Start with basic tracking: log every prompt and response, capture user feedback, review random samples manually. This gives you baseline data and reveals what actually matters for your use case.

Add automated quality checks as you identify patterns. If manual review keeps finding hallucinations about product pricing, build an automated check for that specific issue.

Layer in user behavior tracking once you understand normal patterns. What does successful task completion look like? How do users interact with good outputs versus bad ones?

Many organizations use multi-layered stacks: open-source loggers like Langfuse for raw data, evaluation platforms like Braintrust, and infrastructure alerts from Datadog or New Relic. For solo or small teams (1-10), Langfuse’s generous free tier works well. Mid-size teams (10-50) benefit from Phoenix for evaluation plus Portkey for routing. Enterprise teams (50+) typically choose Datadog or New Relic for unified observability, or self-hosted Langfuse for data control.

The teams that succeed with LLM monitoring and observability don’t start by implementing standard practices. They start by understanding their specific failure modes, then build monitoring that catches those failures early.

Your LLM being “up” means nothing if it isn’t helping users. Monitor the quality, not just the uptime.