AI Inference Observability
Full Visibility into GPU Cost, Utilization, and Inference Performance
Your GPU bill keeps climbing, your utilization is a black box even on shared GPUs, and inference issues take days to debug. ScaleOps surfaces per-pod GPU metrics and inference signals like TTFT and prefix cache hit rate, so you can fix production faster and stop overpaying.
GPU Workloads Lack Real Visibility
Cloud Bills Hide the Real Cost Drivers
Cloud bills show total GPU cost. They don’t show which workload is responsible, where capacity is idle, or where waste compounds over time.
No Workload-Level GPU Telemetry
GPU metrics, SM Active, utilization, bandwidth, temperature, power, live in scattered dashboards, never tied to the workload driving pressure, so teams can’t see how a specific model is using the GPU.
Inference Issues are Long to Diagnose
When latency climbs or throughput drops, key inference signals, TTFT, prefix cache hit rate, running and waiting requests, queue size, sit in separate systems with no view of how they correlate.
Know Where Your GPU Spend is Going
Before, your cloud bill showed one line item for GPU and no way to trace it back. ScaleOps maps every dollar of GPU spend to the specific AI workload running on the node, so when costs spike or capacity sits idle, you know exactly which model, team, or workload to look at, not just that the bill went up.
Per-Pod GPU Visibility, Even on Shared GPUs
Tools like DCGM show you the GPU device. ScaleOps shows you the pod. On shared GPUs, you get per-pod visibility into utilization, memory, and performance, so you can see exactly which workload is driving pressure instead of guessing. ScaleOps also correlates pod-level usage with workload metrics and application signals on the same view, so you can tell at a glance whether a slowdown is resource contention, throttling, configuration, or hardware degradation.
Maximize GPU Utilization
Debug Model-Level Serving Performance
ScaleOps pulls inference server metrics from vLLM and Triton into one workflow. TTFT, prefix cache hit rate, running and waiting requests, and queue size are correlated directly against latency and throughput on the same timeline, so you can see whether a slowdown is the model, the batching strategy, the KV cache, or the pipeline upstream of it.
Cloud Resource Management Reinvented
Boost Performance & Reliability
Ensure consistent performance and uptime, even in the most dynamic environments.
Free Your Engineers
Eliminate repeated manual tuning forever, allowing you to focus on innovation.
Cut Costs by 80%
Pay only for the cloud resources you need without compromising performance.
Instant Value with Seamless Automation