Harbr Data captured 34.7% savings without touching engineering's calendar.

What Harbr Data asked of Cloud Capital

Aimee Costello had visibility into Harbr Data's AWS bill. What she didn't have was the bandwidth to act on it. Harbr Data's enterprise data marketplace runs across a split architecture. Customer workloads sit inside customer-owned AWS environments. Harbr's own AWS bill comes from the control plane: development, testing, and orchestration layers that keep the platform running. Cloud was becoming one of Harbr's largest operating expenses, and finance needed to be able to forecast it as confidently as the rest of the P&L.

Engineering wasn't the obstacle. Engineering was already at full capacity, running performance initiatives and shipping product releases. Pulling the team into commitment modeling, tagging cleanup, or coverage analysis would have meant slowing the roadmap, and the roadmap was what kept Harbr's AWS partnership growing. What Aimee needed was a way to capture deeper savings, hold finance to a defensible forecast, and strengthen Harbr's relationship with AWS — all without touching engineering's calendar.

What changed for Harbr Data

Engineering's bandwidth was the hard constraint. No code changes. No tagging projects engineering would have to own. No commitment strategy that required engineering to validate it on an ongoing basis. The split architecture had to be respected, with customer environments untouched and Harbr's control plane optimized on its own terms. Cloud Capital would absorb the downside if actual usage fell short of forecast.

What Harbr Data needed was deeper commitment coverage on the spend they could actually optimize, a forecast that finance could defend, and the financial risk of getting coverage wrong sitting somewhere other than on Harbr's balance sheet. Harbr 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 with Harbr's AWS Organization, mapping spend across production and non-production environments and isolating the customer-specific workloads that sit outside Harbr's optimization scope. The same workflow runs across every Cloud Capital engagement, tuned to that customer's architecture.

The structured view became the foundation for a shared forecasting model. Cloud Capital combined Harbr's business growth projections with planned technical initiatives to create a single forward-looking view of cloud economics, one that both finance and engineering could read and act on. The forecast updated as usage and business conditions changed, so finance never worked from stale numbers and engineering never had to translate roadmap plans into commitment math.

What it changed for the business

Harbr Data's AWS partnership got stronger, not because the team had more time to invest in it, but because commitment utilization improved while the team kept its focus on the product. Finance forecasts cloud spend with the same confidence applied to the rest of the P&L. Engineering ships against the roadmap without commitment modeling on the side.

Cloud is one of the largest line items on Harbr Data'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.

Achieved within weeks. No code changes, no engineering effort, no operational disruption.