#finops

The fix for a poison-pill row re-billing a model every sixty seconds forever was not a rewrite. It was one INT column, capped at five, incremented atomically in the database. The fix itself shipped with a gap, and that gap is the actual lesson.

A post framed fine-tuning as a 2026 interview trap. Here is the AWS version: the ladder before you touch weights, the four job types and three serving paths Bedrock forks 'fine-tune' into, and what each one costs, priced today.

The most expensive AI agent failures don't throw an error, they hide. One ran at a six-figure-a-year rate for days while every dashboard stayed green, because the signals that catch it are the ones nobody watches. The two instruments you are missing.

Strands' BedrockModel ships with prompt caching off. Two kwargs turn it on, one per-model gotcha catches you, and a 10-turn driver measures 99.9% and 99.8% hit ratios against an 8,156-token production system prefix. The usage block proves it in seconds.

Three findings from a real customer engagement on AWS Bedrock: what a load test was actually doing, why p95 latency was 45 seconds, and the prompt-caching default that costs every team money. Plus the three CloudWatch metrics that catch all three.