4 posts tagged with "telemetry"

A user's calibration of an AI feature is one of the most expensive things you ship. It costs them weeks of attention: learning which prompts work, where the model's reliable, when to double-check, what to ignore entirely. Then a single visible failure — a wrong number in a generated report, a hallucinated citation the user pasted into a deck, a confidently-incorrect recommendation they acted on — can vaporize all of it in one session. The recovery curve isn't symmetric. The user's prior was "this is reliable," and the update doesn't land as a data point. It lands as a betrayal.

The team measuring DAU sees nothing for weeks. The user keeps opening the app out of habit, runs a few queries, doesn't act on the output, and then quietly stops. By the time engagement metrics flinch, the trust event that caused it is two months old and nobody on the team remembers shipping it.

A team I talked to last quarter spent six weeks chasing a memory pressure alert on their agent platform. The agents were cheap — a few cents a run. The traces were not. Their telemetry pipeline was eating three times the budget of the LLM calls it was instrumenting, and most of the spend went to fields nobody had read in months: full prompt bodies stored on every span, tool outputs duplicated across parent and child traces, and an LLM-judge evaluator that re-paid the inference bill on every captured trace.

This is the AI observability cost crisis in miniature. A 2026 industry write-up modeled a customer support bot with 10,000 conversations and five turns each — that comes out to 200,000 LLM invocations, 400 million tokens, and roughly a million trace spans per day. Datadog users widely report observability bills jumping 40-200% after they instrument AI workloads on the same backend that handled their REST APIs. The pipeline is paying twice for the same tokens: once to generate them, once to remember them.

The fix is not "log less." The fix is to treat observability for AI systems as a workload with its own unit economics, separate from the request-response telemetry traditional services emit. Traditional logging is structured fields you can compress and forget; AI logging is unbounded text bodies that re-enter the inference budget every time something reads them. That distinction is what "token-aware logging" means.

There is a broken prompt in your system right now that affects roughly three percent of traffic, and your dashboards do not know it exists. The p99 latency chart is green. The error rate is flat. The model-call success metric is at four nines. The only place the failure shows up is in a customer support ticket the platform team cannot reproduce, and by the time the ticket reaches a debugging session, the trace has been sampled away.

This is not a monitoring gap. It is a category mistake. The APM you are running was designed for a world in which dimensions are bounded sets — endpoint, status_code, region, service — and the cost of an additional label is at most a few new time series. LLM workloads do not fit that shape at all. The interesting dimensions are the user's prompt, the retrieved context IDs, the tool-call sequence, the model revision, the prompt template version, the tenant, the locale, the eval bucket the request fell into. Every one of those is high-cardinality, and any subset of them is enough to detonate the metrics store the moment you tag a span with it.

The cost spike that paged finance at 3 a.m. was not a cost spike. It was a span rename. Someone on the agent platform team decided that llm.completion.synthesis should really be llm.generate.answer because it read more naturally, opened a small PR, ran their tests, and shipped. Three days later finance's monthly token-spend dashboard showed a 60% drop. Nobody had cut spending. The aggregation rule still grouped by the old name, and the new spans flowed past it into an "other" bucket that the dashboard didn't even render. The bill didn't move. The dashboard did.

This is a class of incident I keep watching teams rediscover. Span names and attribute keys are not labels for humans to read in a trace UI. They are the public schema of an undocumented API, with consumers that the producer team has never met — eval pipelines that filter on them, cost dashboards that group by them, SLO alerts that fire on their durations, FinOps reports that sum their token attributes. A "harmless rename" inside one team is a wire-protocol break for four other teams that never saw the PR.