AI Workloads Are Breaking Cloud Budgets. Does Managed Cloud Fix That?

Try InMotion Cloud free for up to 30 days: https://inmotioncloud.com/david-linthicum Sponsored by InMotion Cloud. AI is reshaping the economics of cloud computing. It is not just adding more workloads to the cloud — it is changing how infrastructure is consumed, scaled, governed, and paid for. In this episode, David Linthicum explains why AI applications, agents, RAG systems, automated pipelines, testing environments, storage, networking, APIs, and AIOps-driven automation are creating new pressure on cloud budgets. The issue is no longer simply whether public cloud is "too expensive." The real question is whether each workload is running in the right place, under the right operating model, with the right cost structure.

Some AI workloads still benefit from public-cloud elasticity and rapid scaling. Others are more predictable, persistent, and infrastructure-heavy, making them better candidates for managed cloud or managed private cloud models. The goal is not to choose public cloud or private cloud as a philosophy. The goal is to match workloads to the infrastructure model that delivers the best balance of performance, governance, flexibility, and cost predictability. Thanks to InMotion Cloud for sponsoring this video. As you plan your AI strategy, take time to consider the true costs of AI — not just compute, but storage, networking, data movement, automation, operations, and long-term infrastructure control. The best platform is the one that fits the workload, the economics, and the business outcome. If you are evaluating where your AI workloads should run, check out InMotion Cloud.