CloudBolt Brings GPU Optimization and Bill-Accurate GPU Cost Allocation to StormForge
ROCKVILLE, Md. -- CloudBolt Software announced at KubeCon + CloudNativeCon North America 2026 the general availability of GPU optimization in StormForge, its Kubernetes optimization product, along with bill-accurate GPU cost allocation for platform and FinOps teams.
The release allocates GPU costs to the namespaces, workloads and labels that used GPU capacity, including shared and time-sliced GPUs, using figures sourced from the cloud provider’s bill rather than list-price estimates. It provides per-workload visibility into GPU use and waste, recommendations to reduce that waste, and analysis of node usage patterns with suggestions for better-fit node types. GPU optimization had been in early access since September.
Node Pool Reporting and Reliability Recommendations
StormForge also adds node pool cost and idle reporting, with Reserved Instance and Savings Plans benefits attributed in billing data. Teams can drill down from each cluster to a single node pool and instance type to see cost, idle capacity, Spot usage and commitment coverage. New reliability-focused recommendations account for CPU contention so latency-sensitive workloads get the CPU they need, read namespace LimitRange policies so recommendations comply with them, and improve memory recommendations for Java workloads to reduce out-of-memory errors.
“GPU spend is growing faster than most teams’ ability to explain it,” said Yasmin Rajabi, chief operating officer at CloudBolt. “Platform teams need a number that matches the invoice before they can have a real conversation to drive change and they need automation they trust enough to stand behind.”
More information is available on CloudBolt’s announcement.
