The Best PerfectScale Alternatives in 2026

The main alternatives to PerfectScale by DoiT are LevelFour, Cast AI, ScaleOps, Kubex (formerly Densify), StormForge by CloudBolt, Zesty, IBM Kubecost, Fairwinds Insights, and an open-source stack of Karpenter, KRR, Goldilocks, and OpenCost. Pick by where you want each change to land. Cast AI, ScaleOps, and Zesty resize workloads live in the cluster, as PerfectScale's automation does. LevelFour and Fairwinds put the change in a pull request your GitOps pipeline applies. Kubecost starts from cost allocation and sizes requests after that, and OpenCost does the allocation as open source.

We read each vendor's own product, documentation, and pricing pages, plus AWS Marketplace listings, in October 2026, and noted what each tool optimizes, how a change reaches the cluster, and how it charges. We make LevelFour, so it comes first among the alternatives. For a wider list with allocation and reporting tools, see the best Kubernetes cost management tools.

What does PerfectScale do?

PerfectScale sells itself as an Automated K8s Optimization and Management Platform that keeps cloud cost low and cluster performance high. PerfectScale. PodFit handles vertical pod rightsizing, and NodeFit, formerly InfraFit, rightsizes nodes. Other modules cover GPUs, Java, Karpenter, bin packing, and alerts, and PerfectScale reads billing data from the AWS CUR, Azure Cost Management, and GCP Cloud Billing. It runs on EKS and EKS Anywhere, GKE, AKS, kOps, private clouds, and other Kubernetes distributions. PerfectScale docs.

For Karpenter, it evaluates your NodePool settings and workload behavior and recommends better bin-packing, instance flexibility, and consolidation. Karpenter optimization. Its InfraFit GitHub Action turns those NodePool recommendations into ready-to-review pull requests. PerfectScale public API.

How PerfectScale applies changes

Automation comes with the Expert plan, and trial accounts get Expert access with automation on one cluster. Introduction to automation. PerfectScale changes resources at the pod level and leaves the parent spec (the Deployment or StatefulSet) as it is, "so ArgoCD doesn't detect any changes." The same page names Argo CD and Flux as GitOps tools the automation works with, and gives the setup steps for Argo CD. Automation with Argo CD. On Kubernetes 1.33 or later with the InPlacePodVerticalScaling feature gate, it resizes pods in place without a restart. In-place right-sizing.

What PerfectScale costs

PerfectScale is free up to 300 vCPU a month. Above that, the Advanced and Expert tiers are priced per vCPU by quote, and a self-hosted deployment is available. PerfectScale pricing. On AWS Marketplace, Advanced lists at the equivalent of $2.00 per monthly vCPU, and Expert, which adds automation, the public API, and SSO, at $4.00. AWS Marketplace listing. The home page offers a 30-day trial.

Who owns PerfectScale

DoiT announced its acquisition of PerfectScale on February 4, 2025. DoiT announcement. Two sibling products carry the name. PerfectScale for Commitments reached general availability on May 21, 2026, in a release where DoiT cites 4,500 customers in 27 countries. It manages Savings Plans, AWS Database Savings Plans, and CUDs on AWS and Google Cloud, with Azure next, and customers pay when it delivers realized savings. PerfectScale for Commitments. PerfectScale for Data Platforms adjusts Snowflake, Databricks, and BigQuery warehouses and clusters. PerfectScale for Data Platforms.

On PerfectScale's customer page, SNCF reports 250 clusters under optimization and about €500,000 in estimated annualized savings, and Luma Health reports a 40% lower EKS cost. PerfectScale customers. Our side-by-side comparison puts it this way: PerfectScale fits Kubernetes teams that want pods rightsized continuously in the live cluster, with ArgoCD and Flux support, and DoiT's sibling products for commitments and data platforms.

Why teams look at other options

Teams that compare alternatives are often after one of four fits:

• Workload changes reviewed as Git pull requests. PerfectScale's workload automation acts at the pod level, and its pull requests cover Karpenter NodePools.
• Automation and API access on a plan below the top tier. PerfectScale puts both on Expert. PerfectScale public API.
• Kubernetes and the rest of the bill (cloud services, SaaS, and AI) in one tool. PerfectScale's flagship is Kubernetes, and DoiT sells commitments and data platforms as sibling products.
• A list price on the vendor's own site above 300 vCPU. PerfectScale quotes there, and its AWS Marketplace listing shows $2 and $4 per monthly vCPU.

The PerfectScale alternatives at a glance

1. LevelFour

LevelFour reads your Cloud, SaaS, and AI bills in one view, finds the savings, and ships each one: applied in your cloud or SaaS account with access scoped to that one change, as a pull request to your infrastructure code, or as manual steps. In Kubernetes, it sizes workloads and nodes to what they use.

• Pod requests and limits sized to real usage, OOMKills fixed, idle pods removed, and nodes consolidated, with cost per namespace for every team. LevelFour vs PerfectScale.
• An in-cluster controller that works on the workload layer (requests and limits, HPA, VPA) and the compute layer (nodes, Karpenter, instance selection, consolidation, and Spot).
• Each change shipped as a pull request to your Helm values or Kubernetes manifests, in the repo your Argo CD or Flux pipeline already watches, so your team reviews it like any other change.
• Cluster cost next to more than 50 providers, from AWS, Google Cloud, and Azure to Datadog, Snowflake, OpenAI, and Anthropic, with Savings Plans and Reserved Instances bought and renewed (Commitments). Savings Audit confirms each saving on the bill.
• A pull request bot that prices each change at your own rates on GitHub, GitLab, Bitbucket, and Azure DevOps, plus an MCP server and a CLI for engineers and their AI agents.

Pricing: Cloud Savings costs 30% of realized savings, and nothing if nothing is saved. Plans start at $30 a month, and you can buy LevelFour through the AWS, Google Cloud, and Azure Marketplaces. See pricing.

Best for: GitOps teams that want Kubernetes and cloud savings merged as code, in the same tool as the rest of the bill.

2. Cast AI

Cast AI optimizes Kubernetes and the resources beneath it on AWS, GCP, Azure, and Oracle Cloud, and supports OpenShift, Tanzu, kOps, and bare metal. Cast AI.

• Workload rightsizing, nodes provisioned from your node templates, and underused nodes consolidated, with Karpenter or Cast AI's own autoscaler. Cast AI.
• Spot handling, one GPU shared across workloads, OMNI Compute for GPUs in other clouds and regions, a Storage Autoscaler, and a Database Optimizer that caches reads and pools connections.
• Eligible changes applied to live infrastructure within your policies. The workload autoscaler evicts pods, and its mutating admission webhook applies the new requests when each pod starts. Workload autoscaling.
• A pull request when a change belongs in your repo, through runbooks that are in private preview. Application Performance Automation.

Pricing: by custom quote. Cost monitoring is free for unlimited clusters, and the 30-day free trial starts without a credit card. Cast AI pricing · Cost monitoring.

Best for: platform teams that want nodes, pods, Spot, and GPUs managed live by one automation layer.

3. ScaleOps

ScaleOps rightsizes CPU, memory, and nodes across VPA, HPA, and Karpenter, and calls itself self-hosted by design. ScaleOps.

• Pod Rightsizing, Replicas Optimization, Karpenter Optimization, Node Optimization, Smart Pod Placement, Spot Optimization, Java Resource Management, and Cost Monitoring.
• CPU and memory requests rightsized in real time, with "No manual tuning. No implementation or code changes." You install it with a single Helm command.
• A GPU Platform with Automated Fractional GPUs.
• Self-Hosted, ScaleOps Cloud, and Air-Gapped deployments, with an AI SRE Agent and ScaleOps MCP.

Pricing: by custom quote, with a free trial that starts from a demo booking. ScaleOps pricing.

Best for: teams that want autonomous rightsizing in production, including self-hosted and air-gapped clusters.

4. Kubex (formerly Densify)

Densify rebranded as Kubex on January 15, 2026. Kubex newsroom. Kubex sizes pods, nodes, GPUs, and cloud instances from one engine, in the cloud or on-prem.

• An Automated Pod Scaler for right-sized requests, run with a human in the loop or fully automated, and applied in the cluster by Kubex's Mutating Admission Controller. Kubex.
• A Node Optimizer, Predictive Pod Scaler, Bin Packer, HPA Optimizer, and a conversational AI Agent. Kubex pricing.
• Predictive instance selection, ASG and VMSS scaling, and RDS optimization on the cloud side, through Terraform, Pulumi, and other IaC frameworks, from a UI, a CLI, or MCP.
• AWS, Azure, GCP, and Oracle, across AKS, EKS, GKE, OKE, ECS, NKP, OpenShift, RKE2, and K3S.

Pricing: free for the first 60 days, then $1 per vCPU a month for Kubernetes Resource Optimization. Enterprise Kubernetes and GPU Optimization is custom, with GPUs priced per GPU. Kubex pricing · Pricing page.

Best for: teams that want a published per-vCPU price and one engine for Kubernetes, GPUs, and cloud instances.

5. StormForge by CloudBolt

StormForge by CloudBolt rightsizes Kubernetes workloads with machine learning, scaling them in two dimensions across HPA and VPA. StormForge by CloudBolt.

• Java heap optimization, Karpenter + StormForge, Kubernetes cost allocation, and GPU optimization in early access.
• An agent in the cluster that applies recommendations three ways: Apply Now in the UI, the live.stormforge.io/auto-deploy annotation, or patches you download from the UI or the StormForge CLI for your CI/CD. During the first 7-day learning period, you apply them manually, for review. StormForge by CloudBolt.
• Integration with Argo CD, Flux, and standard Kubernetes pipelines through admission webhooks. Why StormForge.

Pricing: CloudBolt offers a free trial and guarantees up to an 80% reduction in Kubernetes resource waste within 30 days. Why StormForge.

Best for: teams that want machine-learning rightsizing with a review period first, and patches they can feed to CI.

6. Zesty

Zesty tunes pods, storage, and commitments within your policies and guardrails, and says you can run it wherever your clusters run without changing your existing infrastructure. Zesty.

• Multi-dimensional autoscaling, adaptive pod placement, Java resource management, and spike protection.
• Persistent volume autoscaling and Automated Fractional GPUs.
• AWS and Azure commitment optimization, next to compute cost visibility.

Pricing: usage-based, and "Never more than the value delivered." Zesty prices on actual usage and realized savings, and projects your ROI from your CUR before you commit. Zesty pricing.

Best for: teams that want pods, storage volumes, and AWS or Azure commitments tuned by one vendor and priced against savings.

7. IBM Kubecost

IBM Kubecost shows Kubernetes cost across clusters, teams, namespaces, workloads, and shared resources, and reconciles it with your cloud bill. IBM Kubecost.

• Automated actions that resize containers and create and resize Kubernetes resource quotas. The free tier runs them on one cluster, and on Enterprise the resizing job also works with your Resource Quotas. Savings actions.
• Budgets, forecasting, and anomaly alerts. IBM Kubecost.
• A Kubecost integration listed for IBM Turbonomic. Turbonomic integrations.

Pricing: Kubecost has a free tier. On Kubecost 3.0 it is gated at $100,000 of spend over 30 days, and automated request sizing on it covers up to 250 cores. Kubecost on Amazon EKS. Enterprise Self-hosted is sold through Buy Now, and Enterprise Cloud through Contact Us. IBM Kubecost.

Best for: teams that want bill-accurate allocation first and request sizing second, inside the IBM stack. If Kubecost is the tool you are moving off, see Kubecost alternatives.

8. Fairwinds Insights

Fairwinds Insights brings FinOps governance to Kubernetes: it manages costs, right-sizes containers, and fixes misconfigurations across clusters and teams. Fairwinds Insights.

• Cost allocation by label, namespace, and container, and a library of more than 200 policies.
• Resource recommendations inside developer pull requests, and auto-fix pull requests that remediate 45% of findings.
• An Automated Workload Right-sizer that adjusts resources in the cluster.

Pricing: you can start on Insights for free. Paid Insights is custom, with cluster and node pricing options, automated container right-sizing, and a self-hosted or SaaS option. Insights pricing.

Best for: platform teams that want cost, policy, and security guardrails in developer pull requests.

9. Open source: Karpenter, KRR, Goldilocks, and OpenCost

You can split the same jobs across four open-source projects and run them yourself.

• Karpenter, Apache 2.0 and built at AWS, provisions just-in-time nodes for any Kubernetes cluster in any environment. It removes under-utilized nodes, replaces expensive nodes with cheaper ones, and consolidates workloads, all set through declarative NodePools. Karpenter docs.
• KRR from Robusta, under the MIT license, recommends requests from Prometheus data. It runs as a CLI on your own machine without an agent, or in the cluster with weekly Slack reports. The optional KRR Enforcer, a mutating webhook, patches pod requests and limits, and runs with Robusta and a Robusta UI account (free tier available).
• Goldilocks from Fairwinds, Apache-2.0, creates a VPA in recommendation mode for each workload in a namespace and shows the suggestions in a dashboard.
• OpenCost, a CNCF incubating project, measures and allocates cloud infrastructure and container costs in real time, and stays free and open source.

Pricing: free. Your team hosts, upgrades, and connects the four.

Best for: teams that want to own the tooling, with Karpenter on nodes, KRR or Goldilocks on requests, and OpenCost on allocation.

Also worth naming: Infracost prices an infrastructure change on the pull request before it merges, in GitHub, GitLab, and Azure DevOps. Its free CI/CD tier estimates Terraform, CloudFormation, and CDK changes for 1,000 runs a month, and Starter costs $250 a month. Infracost pricing. It works at review time, next to a runtime rightsizer, as Terraform cost in pull requests shows.

How to choose a PerfectScale alternative

1. Decide where a change should land

Cast AI, ScaleOps, Zesty, StormForge with its auto-deploy annotation, and PerfectScale itself change workloads live in the cluster. LevelFour, Fairwinds Insights pull requests, and StormForge patches put the change in Git for your pipeline to apply, and Kubex automates cloud resources through Terraform and Pulumi. If Argo CD or Flux owns your manifests, find out how each tool writes before you turn its automation on.

2. Pick the layer: workloads, nodes, or both

Pod requests, HPA targets, and replicas live in the workload layer. NodePools, Spot, and consolidation live in the node layer, where Karpenter, Cast AI, and the Kubex Node Optimizer work. Smaller requests lower the bill once the nodes under them consolidate, so plan for both layers.

3. Kubernetes alone, or the whole bill

A cluster-focused tool fits when Kubernetes leads your spend. When the cluster is one line among cloud services, SaaS, and AI, LevelFour keeps it in one record with the rest. When your first job is telling each team what it spends, start with allocation in Kubecost or OpenCost.

4. Do you want the cost of a change on the pull request before it merges?

Runtime rightsizing trims what already runs. A pull-request estimate shows the price of a change while someone can still edit it: LevelFour's pull request bot prices each change at your own rates, and Infracost estimates infrastructure diffs in CI. Either one sits next to the runtime tool you pick.

5. Match the pricing model to your cluster

PerfectScale and Kubex charge per vCPU, Kubex at $1 per vCPU a month. Zesty, and LevelFour's Cloud Savings, charge against realized savings. Cast AI and ScaleOps quote. The open-source projects cost nothing but engineering time. Count your monthly vCPUs before you compare per-vCPU prices.

6. Check where it runs, and what it does with GPUs

ScaleOps, PerfectScale, and Fairwinds offer self-hosted deployments, and ScaleOps adds an air-gapped one. Kubex runs on-prem across distributions such as OpenShift, RKE2, and K3S, and Cast AI supports OpenShift, Tanzu, kOps, and bare metal. For GPUs, ScaleOps and Zesty offer fractional GPUs, Cast AI shares one GPU across workloads, and Kubex prices GPU optimization per GPU on Enterprise.

Common mistakes when you replace a Kubernetes optimizer

• Turning on live automation in a GitOps cluster before you check where the tool writes. PerfectScale resizes pods and leaves the Deployment spec alone; a pull request changes the spec in Git.
• Rightsizing pods and leaving the NodePools as they were. The bill drops when the nodes consolidate under the smaller requests.
• Comparing per-vCPU prices without your own vCPU count. PerfectScale's free tier stops at 300 vCPU a month, and past that the count sets the price.
• Sizing from a few days of data. StormForge opens with a 7-day learning period, a full week of traffic, in which you apply its recommendations only by hand, for review.
• Counting estimated savings as realized. Read the bill after each change, with a tool that reconciles to it, such as Kubecost or LevelFour's Savings Audit.

LevelFour sizes your pods and nodes to real usage, ships each change as a pull request to your Helm values, and confirms the saving on the bill. See what LevelFour can save you in 5 minutes, with read-only access.

Frequently asked questions