Sold by: ScaleOps
Deployed on AWS
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Autonomous Kubernetes optimization that reduces cloud compute costs by up to 80% through real-time pod rightsizing and node optimization - with zero disruption.
4.5
Overview
ScaleOps: The Autonomous Kubernetes Optimization Platform
ScaleOps is the industry-first Kubernetes optimization platform that automatically adjusts compute resources in real-time, delivering a streamlined Kubernetes experience for engineering teams. By continuously right-sizing pods and optimizing node utilization, ScaleOps eliminates up to 80% of Kubernetes cloud spending, frees engineers from repeated manual configurations, and proactively ensures SLAs are always achieved.
How It Works
ScaleOps deploys as a self-hosted platform within your own environment. Installation takes only 2 minutes, and value is delivered immediately. The platform starts with read-only recommendations so your team can review optimization opportunities with zero risk. When you are ready, transition to continuous and automatic pod optimization during runtime with zero disruption to your workloads.
The platform autonomously manages CPU, GPU, and memory allocation across your Kubernetes workloads. ScaleOps handles pod rightsizing, replica scaling, node optimization, Karpenter optimization, spot instance management, and GPU rightsizing - all from a single, unified platform.
Key Capabilities
- Pod Rightsizing: Automatically adjusts CPU, GPU, and memory requests and limits based on real-time workload behavior, eliminating over-provisioning and preventing resource starvation
- Node and Karpenter Optimization: Optimizes node configurations and works with Karpenter to ensure efficient cluster-level resource utilization
- Spot Instance Management: Intelligently leverages spot instances to further reduce compute costs without compromising workload reliability
- Replica Scaling: Dynamically adjusts replica counts based on actual demand patterns
- GPU Rightsizing: Optimizes expensive GPU resources for machine learning and AI workloads, ensuring maximum utilization
- Cost Monitoring: Provides real-time visibility into Kubernetes spending with actionable dashboards showing before-and-after cost comparisons
Why Engineering Teams Choose ScaleOps
- Immediate ROI: See optimization recommendations from the moment you install. No lengthy onboarding or complex configuration required
- Production-Safe Automation: Battle-tested on some of the most critical production workloads in full automation mode. Transition from read-only to automated optimization at your own pace
- All-in-One Platform: Replace fragmented point solutions with a single platform covering pod rightsizing, node optimization, replica scaling, spot management, and cost monitoring
- Self-Hosted Deployment: The platform runs within your own environment, keeping your cluster data under your control
- Zero Disruption: Automatic resource adjustments are applied without restarting pods or causing downtime
Trusted by Industry Leaders
ScaleOps is trusted by leading technology companies including Wiz, Outbrain, and many others who rely on the platform to optimize their most critical Kubernetes production workloads.
Get Started
ScaleOps offers a free trial so you can experience the platform's optimization capabilities firsthand. Deploy in minutes, review read-only recommendations, and scale to full automation when you are ready.
Highlights
- Autonomous pod rightsizing with zero disruption. ScaleOps continuously analyzes CPU, GPU, and memory usage across your containers and vertically scales Kubernetes pods in real time to match actual demand - without restarts or downtime. Unlike visibility-only tools that surface recommendations for manual action, ScaleOps operates in full automation mode on production workloads, progressing safely from read-only recommendations to continuous automatic optimization.
- Self-hosted platform - your data never leaves your cluster. ScaleOps deploys entirely within your own infrastructure, ensuring that sensitive workload data, resource metrics, and cluster configurations remain under your control. This architecture eliminates external API dependencies and supports data residency and compliance requirements, making it suitable for security-conscious organizations running regulated or sensitive workloads.
- Reduce Kubernetes cloud spending by up to 80%. ScaleOps continuously identifies opportunities to remove under-provisioned nodes, replace expensive instances with cost-effective alternatives, consolidate pods onto efficient compute resources, and optimize spot instance and Karpenter configurations. Trusted by companies like Wiz and Outbrain, ScaleOps installs in minutes and delivers measurable savings from day one with its all-in-one cost monitoring and optimization engine.
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Dimension | Description | Cost/month | Overage cost |
|---|---|---|---|
ScaleOps Platform Fee | A fixed rate for ScaleOps platform | $4,167.00 | |
ScaleOps GPU Platform Fee | A fixed rate for ScaleOps GPU platform | $4,167.00 |
Dimensions summary
This listing uses a contract model with two separate platform fees, each billed as a fixed rate per unit. The ScaleOps Platform Fee covers resource management across your Kubernetes clusters. The ScaleOps GPU Platform Fee covers GPU-focused resource management, including fractional GPU allocation for AI workloads. These are independent options, not tiers. You pay for one or both depending on whether you need standard cluster optimization, GPU optimization, or both. Your total scales with the number of units you commit to under the contract.
Top-of-mind questions for buyers
The ScaleOps Platform Fee is a fixed rate per unit for standard Kubernetes resource management. The platform installs as lightweight deployments in a dedicated namespace on your clusters. It runs entirely in your environment with multi-cluster support. Contact the vendor for how a unit maps to clusters or workloads.
The ScaleOps GPU Platform Fee covers GPU-focused resource management. It handles fractional GPU allocation and rightsizes each AI workload based on live usage. Choose this fee for inference and GPU-heavy workloads. Choose the standard ScaleOps Platform Fee for general Kubernetes clusters. You can commit to both if you need each capability.
The two fees bill independently, each as a fixed rate per unit. They add together on the same invoice. The ScaleOps Platform Fee reflects your standard cluster units. The ScaleOps GPU Platform Fee reflects your GPU units. Your total scales with the units you commit to under each.
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In Container Cost Optimization, Cost Allocation and Accountability, Resource Cost Optimization
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
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AI generated from product descriptions
Real-time Pod Resource Optimization
Automatically adjusts CPU, GPU, and memory requests and limits based on real-time workload behavior through continuous analysis and vertical scaling without pod restarts or downtime
Node and Karpenter Cluster Optimization
Optimizes node configurations and integrates with Karpenter to ensure efficient cluster-level resource utilization and node consolidation
Spot Instance and GPU Resource Management
Intelligently manages spot instances and optimizes GPU allocation for machine learning and AI workloads to reduce compute costs while maintaining workload reliability
Self-Hosted Deployment Architecture
Deploys entirely within customer infrastructure as a self-hosted platform, ensuring workload data and cluster metrics remain under customer control without external API dependencies
Dynamic Replica Scaling and Cost Monitoring
Dynamically adjusts replica counts based on actual demand patterns and provides real-time visibility into Kubernetes spending with actionable cost comparison dashboards
Automated Resource Optimization
Automatic deployment of optimal blend of spot instances, reserved instances, and on-demand compute for autoscaling applications without manual tuning
Container and Kubernetes Infrastructure Management
Serverless infrastructure for Kubernetes, EKS, and ECS with automatic scaling, bin-packing, and right-sizing of pods
Reserved Instance and Savings Plan Optimization
Lifecycle management of reserved instances and savings plans using machine learning and automation to maximize portfolio value and minimize on-demand costs
Cloud Cost Analytics and Visibility
Granular cost analytics with integration capabilities for financial accountability and cost optimization tracking across AWS resources
Real-time Resource Optimization
Patented algorithms optimize compute and memory allocation in real time based on actual usage patterns rather than allocated resources, enabling scheduling decisions that reduce waste across Kubernetes and Amazon EMR clusters.
Continuous Intelligent Tuning
Continuous stream of data-driven optimization that automatically tunes cluster resources and dynamically adjusts Cloud Autoscaler responses to changing application workloads without requiring application code modifications.
Multi-platform Support
Supports optimization across Amazon EKS, Spark, Amazon EMR on EKS, and Amazon EMR environments for comprehensive resource management across different deployment architectures.
Automated Workload Consolidation
Enables multiple workloads to run on the same pod by optimizing resource utilization, allowing more applications to execute on fewer instances while maintaining performance.
Zero-touch Configuration
Automatically optimizes node and pod performance without requiring manual tuning, code changes, or manual application of recommendations, eliminating the need for developer intervention in resource management.
Standard contract
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Customer reviews
Jlester Lester
Automation has reduced Kubernetes costs and improves right‑sizing and cluster visibility
Reviewed on Jun 16, 2026
Review from a verified AWS customer
What is our primary use case?
We use ScaleOps for our Kubernetes platform right-sizing our workloads.
We have Kubernetes as our shared platform and we have app teams that launch workloads to Kubernetes and we need to right-size their requests to prevent overspending on these. ScaleOps comes in and takes real-time information about these workloads and is able to optimize the request, thus using less resources. It also is used for packing our nodes, so making sure nodes are utilized at a high percentage instead of a lower percentage and having wasted compute.
We are looking to expand to the new ScaleOps features. We're still working on this, so using job optimization, replica optimization, spot optimization and building out through there.
What is most valuable?
By far the request optimization is the best feature ScaleOps offers. This alleviates the pain of having to monitor these constantly to figure out if they're oversized or undersized. ScaleOps takes that and is able to fully automate it and we've seen great success with that.
The request optimization automation saves us both time and money because we are not spending as much. We've seen a reduced cost of the underlying EC2 instances that are the nodes. By using less resources, we need less nodes, and then our application teams need to spend very little time now to make sure these requests are at a good size and that they're not oversized or undersized.
ScaleOps has positively impacted our organization by helping with the reduction of costs greatly. We've been able to save a lot of money and seen return on investment with using ScaleOps. It has also alleviated oversized requests and the work that app teams would have to do for this. I think it's also allowed us to see more information about the cluster and health. It has a lot of good dashboards within there. I think that helps with monitoring and governance.
What needs improvement?
I think that some of ScaleOps's logging can be improved to be more transparent with what ScaleOps platform is doing. Since using it, I have seen ScaleOps improve in this, but I think they still have some improvements they can make with making their monitors of what ScaleOps platform is doing better.
I think that's the primary thing I'd like to see changed or added. I'm pretty satisfied with the platform outside of that and we've seen improvements there, but I still would like to see more clarity of all the actions that ScaleOps is taking.
For how long have I used the solution?
We've been using ScaleOps at our company for about 12 months now.
What do I think about the stability of the solution?
ScaleOps is active in our production environment and we've seen it as stable.
What do I think about the scalability of the solution?
ScaleOps scales well. We haven't had any issues with it in our production clusters, so we're running it at what we would have as our full scale.
How are customer service and support?
The customer support has been great. Anytime that we've needed help, they've been there for us and we have been able to have a great experience with answering questions and also driving features that we want to see on the platform forward.
Which solution did I use previously and why did I switch?
Previously, we had no solution in place for what ScaleOps does for us today.
How was the initial setup?
I didn't work with the contract directly. That was more done by our management level. But I did work on the initial setup and I found it really easy out of the box and easy to install in our Kubernetes clusters.
What about the implementation team?
We are only the customer.
What was our ROI?
ScaleOps has reduced our costs by about 45 percent. For time, it's very tough to make a declaration of how much time it's saved us, but I do feel it has saved app teams' time because they are no longer having to look at this monitoring week by week or month by month.
We've been able to save about 45 percent on our Kubernetes costs. In terms of employees saved, we would not have been able to build this product in-house without having more employees focused on this.
What's my experience with pricing, setup cost, and licensing?
I don't believe we purchased ScaleOps through the AWS Marketplace. I wasn't involved in those contracts directly, but I believe that we worked with ScaleOps directly.
Which other solutions did I evaluate?
I wasn't involved with the evaluation of other tooling. I know that ScaleOps emerged as a leader and was chosen by our leadership.
What other advice do I have?
It's been a great experience working with ScaleOps.
I think that's pretty much it. It's a really good platform. I'm pretty happy with it. Maybe better use of Helm values, but I think they're coming a long way with that as well.
I would give them the advice of just having a slow rollout and monitoring their platform, making sure that everything continues to look good and healthy, and working with ScaleOps to validate that.
We actually have the AI capabilities fully turned off, so we do not utilize those at my company.
I would give this review a rating of 9 out of 10.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)