Colossyan turned unpredictable AI infra into 38% savings and financial control

The challenge

Before Cloud Capital, Colossyan’s AWS usage was expanding quickly to keep up with product growth. The platform’s workloads - AI inference, model training, and video rendering - each followed different demand patterns that often spiked when new models or features were released.

The team’s priority was shipping product and advancing AI capabilities, not managing cloud commitments. Cost reviews were handled manually whenever spend appeared higher than expected, but without forecasting tools or dedicated coverage models, it was difficult to see where savings could be captured safely. Finance could monitor total spend, yet lacked the ability to forecast it or connect it directly to revenue and usage metrics.

Cloud Capital was brought in to provide structure and visibility, and to turn what had been a series of reactive reviews into a predictable process aligned with both product and financial planning.

Business impact

Cloud Capital’s impact went beyond cost reduction, giving Colossyan a lasting framework for financial predictability.

By integrating directly with Colossyan’s AWS Organization, Cloud Capital analyzed usage across inference, training, and rendering workloads, then modeled safe commitment strategies based on recurring demand patterns. Colossyan achieved a **38% savings rate on new commitments**, with coverage continuing to expand as workloads stabilize.

The impact extended beyond savings. Cloud Capital replaced manual, engineering-driven reviews with automated forecasting and monthly reporting. Engineering’s time spent managing costs dropped from hours each week to roughly 30 minutes per month, freeing the team to focus on performance and feature delivery while finance gained dependable forecasts for planning and margin analysis.

“Cloud Capital helped us bring predictability to one of our biggest operating expenses. It’s now part of how we plan growth, not just something we react to.” — Imre Nagy, VP Engineering, Colossyan

Inside the model

Cloud Capital began by structuring Colossyan’s cloud forecast model around its unique workload patterns. The platform’s AI pipelines were segmented into inference, training, and rendering layers, each tagged and mapped to product metrics such as video minutes rendered, active user sessions, and model deployments. This mapping allowed Cloud Capital to translate technical usage into financial insight that both engineering and finance could act on.

To establish a durable foundation, Cloud Capital integrated directly into Colossyan’s AWS environment, migrating its existing setup into a forecast model designed for scale. A dedicated payer account was introduced alongside a commitments account to manage Reserved Instances and Savings Plans securely, ensuring that new environments could inherit the same structure without additional configuration.

IAM and delegated admin roles were configured to preserve Colossyan’s full control over infrastructure and security while enabling Cloud Capital to manage commitments and forecasting safely. Once integration was complete, Cloud Capital normalized tagging across AWS Cost Explorer and Colossyan’s internal analytics, resolving inconsistencies that had previously limited visibility. With clean data in place, Cloud Capital built detailed forecasting models to project how spend would evolve with product growth, model updates, and usage spikes.

Forecasting is now automated. The platform ingests AWS usage data daily, maps it to workload-specific cost layers, and generates rolling projections that adapt to Colossyan’s evolving workloads. Monthly reviews align engineering and finance around these forecasts, which now serve as the foundation for commitment planning and optimization.

This structure allows Colossyan to capture the benefits of commitment-based savings without adding operational overhead, maintaining full compliance with AWS best practices while achieving lasting financial predictability.