From flying blind on cloud costs to product margin visibility

Accrete deployed Attribute™ to observe real runtime behavior using eBPF, attributing cost based on actual system activity rather than tags or estimates. Deployment required no changes to existing workloads, tagging strategies, or pipelines. Within days, the platform team had cost visibility at the workload and service level, insight into traffic patterns driving spend, the ability to correlate AI usage with specific services, and a unified view of infrastructure and AI-related costs.

Accrete operates a shared, multi-tenant AWS platform optimized for high utilization across many workloads. While top-level spend and allocation were clear, attribution broke down inside shared Kubernetes compute. Costs could not be reliably tied to individual workloads, services, or customers. Cost anomaly investigations required manual correlation across multiple systems, and AI usage could not be easily mapped to the services consuming it. Tags and billing tools provided top-level insights but did not reflect how resources were actually consumed at runtime.

Accrete deployed Attribute™ to observe real runtime behavior using eBPF. As Jason Moore, Principal DevOps Engineer at Accrete, put it: 'It's like a network monitor for your budget.' Deployment required no changes to existing workloads, tagging strategies, or pipelines. Within days, the platform team had cost visibility at the workload and service level, insight into traffic patterns driving infrastructure spend, the ability to correlate AI usage with specific services, and a unified view of infrastructure and AI-related costs.

Attribute™ surfaced a previously invisible network traffic pattern driving significant infrastructure cost. A high-throughput service was routing large volumes of data through an inefficient network path. Once identified, the team implemented a targeted fix to optimize routing. Monthly cost for the service dropped from $63K to $17K, eliminating roughly $46K per month in unnecessary spend. Without runtime-level visibility, identifying this issue would have required significant manual investigation and may have persisted unnoticed.

Runtime attribution also revealed inconsistencies in API key usage across environments, enabling the team to identify policy deviations, correct usage patterns, and reduce operational and compliance risk. For the first time, Accrete established a consistent view of cost across engineering and finance. They can now understand cost per workload and service, align engineering decisions with financial impact, and move beyond estimated allocation toward actual consumption.

With runtime attribution in place, Accrete is extending its model to incorporate customer-level identifiers. This will enable accurate cost per customer, margin analysis at the account level, and data-driven packaging and pricing decisions grounded in actual consumption. Accrete continues to expand this use case, moving from infrastructure cost visibility into product margin analysis and pricing strategy. With true cost-to-serve visible at the account level, pricing and product teams now have the data to set rates that reflect how the platform is actually used.

By shifting from tag-based estimation to runtime-based attribution, Accrete gained visibility into how resources are truly consumed within shared infrastructure. This enabled immediate cost optimization and established a foundation for aligning platform operations with business economics at scale. As Accrete expands into product margin analysis and pricing, runtime attribution gives their teams a single source of truth: not just what the platform costs, but what it costs to serve each customer, and what that should mean for how they price.