ML Inference Latency and Cost Evaluation Platform
Internal tool for profiling latency, throughput, and $/req of models in production
One-liner: Built internal platform that reduced $/req by 43% and stabilized p99 latency by standardizing model profiling and cost monitoring.
What the system does in simple terms
Problem: Teams deployed models without unified monitoring standard. GPUs were idle, latency fluctuated, costs were not tracked. No visibility into $/req per model.
Solution: Platform with Prometheus, Kubecost, and Torch/ONNX profiling provides visibility into latency, throughput, load, and $/req at model level. Standardized deployment process with cost tracking.
Savings: $/req decreased by 43%, p99 latency stabilized. Better GPU utilization, cost transparency per model.
ML part: System uses PyTorch/ONNX profiling, Prometheus for metrics, Kubecost for cost tracking, Grafana for visualization.
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FAQ
What was the core outcome of this ML cost platform?
It reduced cost per request by 43 percent while keeping p99 latency stable through standardized profiling and cost visibility across deployed models.
Which metrics mattered most for operational decisions?
Latency percentiles, throughput under load, GPU utilization, and cost per useful request were tracked together to avoid one-sided optimizations.
Why build this as an internal platform instead of ad hoc scripts?
A shared platform enforces consistent measurement and release criteria, so teams can compare models objectively and avoid repeated tuning mistakes.
