Traditional cost tracking has long forced engineering teams to manually wrap model calls in SDKs or enforce rigid tagging protocols. These methods frequently fail in shared environments, where a single API key might serve multiple models simultaneously, leaving companies with incomplete data and significant cost overruns. Attribute bypasses this by utilizing a lightweight eBPF sensor that monitors activity directly at the operating system kernel. By observing real consumption rather than relying on metadata, the system maps GPU, CPU, and memory usage back to specific containers, pods, and agents.

This approach allows organizations to break down complex costs from providers like OpenAI, Anthropic, Google Gemini, and AWS Bedrock into granular units. According to General Manager Izhak Zimmermann, the shift moves the industry away from the labor-intensive practice of tagging and labeling, which he notes has been the status quo for fifteen years. The sensor installs in approximately 15 minutes, providing automated, audit-ready data on cost per token, request, and customer. Beyond AI, the technology extends to Kubernetes clusters and multi-tenant databases, enabling teams to calculate gross margins and unit economics for shared infrastructure resources automatically.