The Langfuse migration that cost us a sprint: how I now budget LLM observability

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

<p>For the last few years the tech industry has quietly assumed this<br /><br /> AI is powerful almost free and it will eventually pay for itself </p> <p>Turns out that story is

<p>This post from Anthropic is causing a lot of angst among its most ardent users. While it is disguised as a credit, it really is them tightening API token limits (aka charging

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<p>The price page hasn't moved. Five dollars per million input. Twenty-five per million output. Identical to Opus 4.6.</p> <p>Run the same Claude Code call on both, though, and

<p>Four agents entered an infinite loop in November 2025. They ran for 11 days. The bill was $47,000. Nobody noticed until it was over.</p> <p>The team was running a market

<p>Last week my research agent failed at step 15 of a 30-step run. Langfuse showed me exactly where it broke. The writer sub-agent hallucinated, citing a stale 2019 population

<p><em>*Originally published on cubeapm.com</em></p> <p>As organizations adopt cloud-native architectures, Kubernetes, and microservices, systems have become more distributed and