Colossyan turned unpredictable AI workloads into 38% savings.

What Colossyan asked of Cloud Capital

Imre Nagy was running engineering at a company where every product release moved the AWS bill. Colossyan's AI video-generation platform is built on three workload types that each behave differently. Inference scales with how customers use the product. Model training spikes around research experiments and new model releases. Rendering moves with content production volume. None of them follow the patterns historical extrapolation expects, and any of them can pull the bill in a direction finance didn't model.

The engineering team's mandate was the product. Shipping new model capabilities, improving inference performance, expanding rendering throughput. Cost reviews happened reactively, when a number came in higher than expected. There were no forecasting tools that understood AI workload shapes. There was no coverage strategy that could survive the next model release. Finance could see the total spend climbing but had no way to project where it was going or why.

The instinct on the engineering side was to build the forecasting internally. Tag the workloads, instrument the pipelines, build the models. But that work would have come at the cost of the product roadmap, and even done well, it wouldn't have changed the underlying problem: aggressive commitment coverage on AI infrastructure required absorbing variance Colossyan didn't want to hold.

What changed for Colossyan

Engineering's requirements were clear. No infrastructure changes. Full control of accounts and IAM stays with the team. No commitment strategy that handcuffs the platform to today's workload shape, because tomorrow's models will run differently. Compliance with AWS best practices, non-negotiable. What Colossyan needed was a forecast that understood AI workloads on their own terms, a commitment strategy that adapted as the product evolved, and someone else holding the financial risk of getting the coverage call wrong.

Colossyan would capture the savings from aggressive AWS commitment coverage. Cloud Capital would absorb the downside if actual usage fell short of forecast.

Implementation was limited to billing and account setup, IAM configuration, and data alignment. Cloud Capital integrated directly into Colossyan's AWS environment. A dedicated payer account and a separate commitments account were established to manage Reserved Instances and Savings Plans. IAM and delegated admin roles were configured to keep Colossyan in full control of infrastructure and security while Cloud Capital ran the commitment workflow. No code changes, no workload moves.

Cloud Capital's forecasting model segmented Colossyan's pipelines into inference, training, and rendering layers, then mapped each layer to the product metrics that drive its demand: video minutes rendered, active user sessions, model deployments. Tagging was normalized across AWS Cost Explorer and Colossyan's internal analytics so the same numbers showed up in both places. With clean data, the forecasts became projections of how spend evolves with product growth, model updates, and usage spikes, not extrapolations of last quarter. The same workflow runs across every Cloud Capital engagement, tuned to that customer's workload shape.

What it changed for the business

Colossyan's AI infrastructure used to be the line item that moved most unpredictably. Now it's a planning input. Engineering ships new models and inference improvements without triggering a cost-review fire drill. Finance forecasts gross margin alongside the rest of the P&L.

Coverage continues to expand as workloads stabilize, and the forecast updates itself as the product evolves. AI infrastructure is one of the largest line items on Colossyan's P&L. Now it's managed like one, with finance and engineering working from the same forecast, and the financial risk of optimization sitting where it belongs.