Andrew Midgley

AI workloads can consume millions of dollars in infrastructure before anyone notices whether they're being used efficiently. GPUs sit idle, clusters become overprovisioned, and engineering teams struggle to balance performance with rapidly growing costs.

AI spend often arrives as a black box: a rising bill with little clarity into which teams, models or features drove it. Learn why AI cost behaves differently and how teams use IBM Cloudability to apply FinOps discipline on AWS: categorizing costs, connecting spend to business goals and identifying waste.

Generative AI is transforming industries—and AWS managed services are accelerating that shift—but these workloads can also be some of the most expensive and unpredictable to run.

With cloud spend increasing to new heights and with an uncertain economic climate, it’s never been more important to be efficient with cloud usage and engineering time.

With increasing scale and diverse technology deployments, cloud environments are becoming more complex by the day, requiring a disciplined approach to prevent an accumulation of waste.