Responsible K8s optimization your SRE lead will actually leave on
Switch from Cast AI. Pay 50% Less.
Where Cast AI optimizes for cost and treats risk as an afterthought, PerfectScale makes reliability the foundation of savings, with safeguards at every stage.
Trusted by DevOps, SRE, and Platform Engineering teams around the world.
Cast AI optimizes the invoice. PerfectScale optimizes the system.
// CAST AI
Cost-centric automation
CAST AI is designed with a strong focus on cost reduction, aggressively adjusting resources in ways that may introduce production risks for engineering teams. Relying on runtime signals without accounting for code revisions, it makes decisions based on historical behavior that may no longer reflect the current workload, introducing unsafe resource changes. "Sometimes the suggestions are too aggressive for nodes and it may lead to workload discrepancy." — G2 review
// PERFECTSCALE
Reliability-first, performance-driven
- PerfectScale calls it "responsible cost optimization for responsible adults”. PerfectScale is built with reliability in mind: by continuously right-sizing resources and bringing performance into every optimization decision, it helps teams achieve cloud savings without compromising stability, giving engineering teams the confidence to optimize in production.
- PS! PerfectScale is backed by the DoiT platform.Full cloud bill in scope, with cloud experts included. It's an asymmetry point-solution that competitors can't match.
See why teams prefer PerfectScale over Cast AI
75% K8s cost reduction
- 200+ microservices optimized
- Six-figure annual savings
- No performance compromise
50%+ savings
- Eliminated OOMKills
- Reduced CPU throttling
- Removed SLA breaches
30% savings
- Realized ~ €500K annualized savings
- Supported 30% more projects without increasing spend.
- Improved cluster stability alongside the savings
PerfectScale allowed us to grow capacity without growing cost. We effectively absorbed 30% more usage for free.
Thomas Comtet, Senior Staff Engineer at SNCF
Where the two platforms part ways
| Capability | PerfectScale | CAST AI |
|---|---|---|
| Comparison | ||
| Default permissions | Read-only Kubernetes RBAC, no cloud credentials | Read-only Phase 1; node automation requires cross-account cloud IAM write access |
| Node provisioning | Your autoscaler stays in control | CAST provisions, drains, and terminates nodes |
| Workload rightsizing | PodFit, policy-driven, in-place on K8s 1.33+ | Workload Autoscaler, in-place supported |
| Automation safety | Immediate scale-up, gradual scale-down, automatic rollback of unschedulable changes | Gradual rollout; compaction model evicts pods and removes nodes |
| Cost attribution | Per customer and per feature, observed at runtime, network paths included | Cluster, namespace, workload, and label reporting |
| Commitment purchasing | Automated, risk-aware, with guardrails | Not offered; imports existing commitments only |
| Pricing model | Published flat per-vCPU; half your CAST bill for 24 months under this offer | Commonly savings-based plus CPU-based fees; terms vary |
| Human expertise | Forward Deployed Engineers and FinOps consultants | Support and customer success |
Production-safe cost optimization
Continuous optimization engineers can trust in production
PerfectScale delivers context-aware, health-first automation that supports FinOps best practices.
Reliability-first automation
Reliability is built into every cost optimization decision, keeping application health at the center.
Context-aware right-sizing
Adapts every action to the workload’s actual context, including performance baselines, traffic patterns, business criticality, deployment revisions, and rollout status, so resources are optimized without compromising application health.
Granular governance and controls
Define policies, guardrails, and optimization rules by workload criticality, environment type, and operational requirements, keeping every action aligned with business priorities and reliability goals.
GitOps-friendly automation
Apply optimization changes through industry-standard Git workflows, without relying on workload-level annotations that create maintenance overhead at scale.
Exceptional visibility and FinOps practices support
Connect Kubernetes efficiency, cost, and performance trends to understand optimization impact, forecast future cloud spend, and make informed FinOps decisions.
Still mid-contract?
We'll buy your contract, so you never pay two vendors for the same cluster.
The terms, in plain language
Every current CAST AI customer qualifies, and your latest CAST invoice sets your price.
PerfectScale at half your current bill
We price the full product at 50% of what you pay today, including per-customer and network cost attribution.
That price is fixed for 24 months
It is not indexed to cluster growth or to savings. The number you sign is the number you pay through month 24.
We run your migration
That covers the parallel install, side-by-side validation, autoscaler cutover, and automation guardrails.
Frequently asked
questions
Do I qualify?
Yes, if you are a current CAST AI customer on a paid plan. Your most recent CAST invoice proves eligibility and sets your price.
How is the 50 percent calculated?
We take your average monthly CAST spend across your last three invoices and halve it. That becomes your fixed monthly PerfectScale price for the next 24 months.
What exactly do I get for half my CAST bill?
PerfectScale for Kubernetes in full: rightsizing automation, resilience analysis, per-customer and network cost attribution, multi-cluster and GPU visibility. FDE-led migration is included. PerfectScale for Commitments is available alongside it under its own terms, and your FDE will show you the numbers before you decide.
What if my clusters grow during the 24 months?
The price does not move. It is not indexed to vCPUs, savings, or usage, which is the point of leaving a savings-based fee model.
What happens at month 25?
You move to our published flat per-vCPU pricing, and you will see that figure during onboarding, two years before it applies. If you walk away instead, the free Community tier keeps covering up to 300 monthly vCPUs.
I am mid-contract with CAST. Should I wait for renewal?
No. Install read-only now, validate in parallel, and cut over when you are ready. Your 24 months at half price start at cutover, so the overlap period costs you nothing on our side.
Do I need to remove CAST before installing PerfectScale?
No. The two run side by side during validation. You remove CAST at cutover, once your autoscaler is back under your control and the numbers have held for a few weeks.
Who does the migration work?
A DoiT Forward Deployed Engineer, in your environment, with your platform team. The heavy piece is re-establishing node provisioning through Karpenter or your managed autoscaler, because CAST currently owns that layer. That work is included.
What permissions do you need, and when?
Read-only Kubernetes RBAC on day one: get, list, watch. No cloud credentials, ever. If you enable workload automation later, changes run through Kubernetes RBAC and an admission webhook. The agent cannot launch or terminate an instance at any point.
Isn't switching a big, disruptive rip-and-replace project?
No. PerfectScale works inside the autoscaling stack you already run — Karpenter, Cluster Autoscaler, HPA — so there's no cluster rebuild, no forked networking, and no new node autoscaler to adopt. Migration doesn't touch your networking or node-management model, which is why it takes days, not quarters.
How long does migrating to PerfectScale actually take?
Migration takes days, not quarters. Because PerfectScale works with the autoscaling stack you already run — Karpenter, Cluster Autoscaler, HPA — there's no cluster rebuild, no forked networking, and no new autoscaler to learn.
We use Canary or Blue-Green rollouts — does PerfectScale understand that?
Yes. PerfectScale's rollout-aware optimization feature identifies the individual revisions and ReplicaSets participating in a rollout, understands the rollout strategy and current state, and adapts automation accordingly — rather than applying stable-revision resource settings to a Canary with very different behavior.
Cut cloud cost, keep peak performance
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