Autoscaling · Kubernetes ·
Any cloud
Eliminate cloud waste.·Boost application performance.·Reduce risk.
Trusted by Industry Leaders
Teams cut cloud bills, wipe out idle capacity, and hand off the manual tuning and on-call risk to CloudPilot — keeping every dollar working without slowing the business down.
Netvue rebuilt its GPU inference on Kubernetes and moved from GCP to Spot GPUs on AWS — co-locating compute with its data and scaling cleanly through day-and-night traffic swings.
Trenz moved real-time analytics and Spark jobs onto Spot Instances — CloudPilot's 45-minute interruption prediction and same-AZ scheduling held throughput and data accuracy steady.
A global generative AI leader ran EKS at under 7% CPU utilization. CloudPilot's full-stack autoscaling rightsized workloads in place and reshaped the nodes underneath — halving the bill without a single code change.
The problem
The teams above started exactly where you are: waste you can see, JVMs that need tuning, Spot you'd run if you trusted it. Three problems every team can name — and can't keep on top of.
75% of cluster CPU sits idle. But reclaiming it takes constant tuning nobody has time for.
Heap too small means OOM. Limits too tight mean throttling. Both knowable. Neither gets fixed at scale.
70% off is real. But nobody can predict when it interrupts.
Workload Autoscaler tunes CPU and memory requests from historical and real-time usage — with safety buffers built in. With in-place pod resizing (K8s 1.33+), it adjusts live pods on the fly, ending both overprovisioning and OOM risk.
-Xmx) and container memory together, GC-risk aware.Right-sized pods from Workload Autoscaler give Node Autoscaler the exact signal it needs to decide. With deep awareness of your workload patterns and the spot market, it selects the best-fit instance types from 800+ options across zones and families — bin-packing those pods so loose, half-empty nodes collapse into the fewest, cheapest ones.
JVM heap and container memory are one problem, not two. Most autoscalers only see the container — and leave 40–45% of your Java memory on the table. We optimize both together.
-Xms / -Xmx managementWe inject recommended heap flags straight into your Java containers and coordinate them with Pod memory in one loop — no hand-tuned JVM args, no tribal knowledge.
Read GC pressure and pause-time before resizing. Heap shrinks only when GC budget allows.
Startup needs far more than steady state. We boost Requests during the startup window, then drop back once the app stabilizes — reliable concurrent starts, zero steady-state waste.
Across production Java clusters, actual memory usage dropped 40–45% — the savings come from right-sizing over-provisioned JVM heap parameters down to their true values, not from paper changes to Requests and Limits.
Read the engineering breakdown →-Xms / -Xmx managementMost optimization vendors run from a centralized control plane. If they go offline, your scaling stops with them. We don't. The agent that does the work lives in your cluster.
Control plane dies, autoscaling stops, load overwhelms the nodes — the whole cluster goes with it.
The in-cluster agent falls back to native autoscaling and Karpenter adds capacity — workloads keep serving.
Before security review asks: here's the full list. Metadata and resource shape — enough to do the work. Nothing about your application, your users, or your code.
we collect — the minimum to do the work
we never collect — nothing about your app
Install in two steps. Phase 1 is read-only — we analyze, nothing changes. Phase 2 grants scoped, least-privilege execution — only when you say so.
AWSAzureGCPK8sStart with a free proof of concept — see your exact waste in Phase 1 at no cost. You only ever pay a share of the savings we actually deliver.
Price per vCPU
per core per month
Savings
Saving monthly
We charge ~30% of savings
Running 10k+ cores or need self-hosted / regulated deployment? Book a demo for volume pricing, SLAs, and on-prem.