For organizations running AI workloads on Kubernetes, the sight of a fully utilized GPU pool coupled with a growing batch-job queue might suggest that increasing the pool size is the logical next action.
However, a backlog could indicate issues related to timing, serving capacity, or the specific model and hardware selected. Prior to expanding a GPU pool, operational teams must clearly define success metrics, required completion times, and associated delivery costs. As one source stated, "An allocated GPU, a busy GPU, and a workload producing valuable results are not the same thing."
Understanding the relationship between these three concepts enables teams to maximize current capacity before provisioning genuinely needed additional resources.
Workloads must be analyzed based on their underlying use cases. A customer service chatbot might necessitate constant inference capacity, whereas a model evaluation job's results may only be required before the next internal release synchronization. Documenting the deadline, expected runtime, resource needs, and interruption tolerance for every workload is crucial. Furthermore, defining what constitutes a valuable output is necessary; for instance, an inference service requires a task-specific quality threshold and a latency objective, as a simple completed request does not confirm objective fulfillment.
Scheduling flexibility should be explored before capacity expansion. If four compatible GPUs are available from noon until 5 p.m. for four jobs, each needing two GPUs for one hour, staggering the jobs—running two at noon and two at 1 p.m.—can complete all tasks by 2 p.m. using only the existing four GPUs. This demonstrates that "Staggering reduces peak demand. The jobs still finish on time without expanding the pool."
Beyond scheduling, inference teams must assess how well the model and serving configuration align with the work, as high GPU activity alone does not quantify available throughput. Metrics like running requests and cache occupancy provide context, but a representative load test is needed to measure sustainable capacity. This test involves keeping the model, hardware, and serving settings constant while increasing the arrival rate to observe throughput, quality, and latency.
The FinOps community emphasizes that for AI workloads, cost analysis is most impactful when it links infrastructure expenditure to tangible outcomes. Kubernetes teams can materialize this link by merging workload cost data, serving behavior telemetry, and application results using OpenCost's allocation API. A key metric involves calculating cost per verified outcome: total attributable cost divided by the number of results that meet defined quality requirements and arrive within the deadline.
Consideration must be given to whether spare capacity justifies its cost. A model achieving 90% correctness is unsuitable if a stronger model yields 990 correct and timely results for $2 per hour, resulting in about $2.02 per 1,000 qualifying outcomes. The objective is achieving "a deployment that delivers the required outcomes at a defensible cost, with enough headroom to meet its requirements."
The trend points toward integrating operational performance data with financial accountability. OpenCost proposal 3915 is exploring incorporating serving-engine telemetry into the cost model, though this remains a draft. Ultimately, when comparing models, cost per correct outcome is most valuable when the outcomes being compared share similar value and error consequences.
