ScaleOps

What ScaleOps does

ScaleOps manages Kubernetes resource allocation autonomously. Instead of engineering teams manually setting CPU and memory limits — or relying on Kubernetes' built-in VPA, which is notoriously slow to react — ScaleOps continuously adjusts allocations based on real workload behavior.

The result is that clusters stop wasting money on over-provisioned resources and stop falling over from under-provisioned ones. The platform handles this without anyone setting thresholds or writing rules.

Why it matters in 2026

As AI/ML workloads moved into production, GPU resource management became a separate headache. ScaleOps extended its optimization to cover GPU allocations — which are far more expensive to over-provision than CPU or memory. For teams running model inference or training in Kubernetes, this is a meaningful cost lever.

The company raised $130M in a Series C in March 2026, valuing it at over $800M. That's not a signal of hype — it reflects real enterprise traction from companies using it in production.

Who uses it

Engineering teams running large Kubernetes clusters where resource waste is measurable in dollars per month. Organizations with AI/ML workloads in production where GPU costs are significant. Platform engineering teams who want less manual toil around capacity management.

Pricing

Enterprise pricing via sales. Typically structured as a percentage of demonstrated cost savings, which aligns incentives between ScaleOps and the customer.