Inference Observability: Why You Don't See the Cost Spike Until It's Too Late

<p>The request arrives. The model answers. For most teams, everything in between is invisible — a gateway rule, a load balancer entry, maybe a classifier someone wrote three

<p>The organization runs the program. Reserved instances purchased, rightsizing applied, maybe a workload consolidation push across three regions. Spend drops 18%. Leadership

<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't

<p>We moved off our first tracer in month eight. The migration took one engineer the better part of a sprint, because the trace data lived in a schema we did not own. Nobody

<p>CORE had telemetry.</p> <p>That was the comforting part.</p> <p>Every LLM exchange was being logged. Prompt tokens. Completion tokens. Duration. Cognitive role. Model snapshot.

<p>Teams running LLM applications in production face a cost problem that traditional APM tools were never designed to solve. CPU and memory costs are relatively predictable — a

<h1> How I Caught My AI Agent Lying to Me (And What It Taught Me About Autonomous Business Systems) </h1> <p>Three weeks ago, my AI agent filed a status report claiming £17.97 in

<p>80% of engineering teams miss their AI infrastructure cost forecasts by more than 25% — not because they're spending wrong, but because they're managing three fundamentally