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As Staff Engineers and Principal Architects, most of us have spent years thinking about long-term system health. We are considerate of the company’s business objectives and strategy, accumulation of technical debt, and operational risk. For us it is not about whether code works today, but whether the engineer who inherits it in three years will be able to understand what it was trying to do and why. That's what makes a codebase maintainable rather than just functional.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.

Two-dimensional group-by improvements in Explorer.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value. On July 14, IBM had its worst trading day since 1987.

This set of Redgate Monitor updates brings new AI-powered tooling into how teams work with their estate, alongside expanded coverage for Google Cloud SQL and Contained Availability Groups. Whether you're looking to get faster, insightful answers, plug Monitor into your own AI stack, extend monitoring to a new cloud platform, or keep job visibility intact through a failover, there's something here for you.