By Eric Newcomer
Brain Candy | March 1, 2025
IT costs are always difficult to estimate, especially project costs. Application development, maintenance, or modernization projects often run late and over budget, and the cost to host and run them isn’t easy to calculate.
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AI cost visibility breaks down when spend is forced into the same monthly reporting model used for cloud infrastructure. This guide covers how to fix it.
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Startups are rapidly building and scaling AI products on Google Cloud, leveraging its full AI stack, from models to GPUs. At Google Cloud Next 2026, companies like Mavvrik are using these capabilities to deliver unified cost visibility and governance across cloud, AI, and SaaS—highlighting how startups are turning complex AI infrastructure into scalable, production-ready systems.
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AI infrastructure costs are notoriously difficult to measure because they don’t live in one place. A single AI workload can span GPUs, cloud compute, model APIs, and shared orchestration layers, each producing its own usage and billing signals. Most organizations can see total spend, but not what drives it.
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