Sold by: ScaleOps
Deployed on AWS
All-in-one resource management and optimization platform for Kubernetes
4.5
Overview
ScaleOps is the industry-first Kubernetes Optimization Platform that automatically adjusts Compute Resources to changes in real-time, streamlining a new Kubernetes experience for engineering teams.
ScaleOps eliminates 80% of Kubernetes cloud spending, frees engineers from repeated ongoing configurations, and proactively ensures SLAs are always achieved.
Installation takes only 2 minutes, and value is provided immediately. Starting from read-only recommendations to continuous and automatic pod optimization during runtime with zero disruption.
Battle-tested on some of the most critical production workloads in full automation mode. Trusted by industry leaders, such as Wiz, Outbrain and many others!
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Highlights
- Automated Pod right-sizing. ScaleOps continuously analyzes your containers compute usage and vertically scales K8s pods to meet demand in runtime
- Self hosted platform
- Continuously lowers clusters compute costs by looking for opportunities to remove under provisioned nodes, replace expensive nodes with cheaper alternatives, and consolidate pods onto more efficient compute resources
Details
Sold by
Delivery method
Deployed on AWS
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Dimension | Description | Cost/month | Overage cost |
|---|---|---|---|
ScaleOps Platform Fee | A fixed rate for ScaleOps platform | $4,167.00 |
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Delivery details
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Updated weekly
By ScaleOps
By Spot
By Pepperdata, Inc.
Top
10
In Analytic Platforms
Top
10
In Application Servers
Top
25
In Container Cost Optimization, Cost Allocation and Accountability, Resource Cost Optimization
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Automated Pod Resource Optimization
Continuously analyzes container compute usage and vertically scales Kubernetes pods to meet demand during runtime with zero disruption
Node Cost Optimization
Identifies opportunities to remove under-provisioned nodes, replace expensive nodes with cheaper alternatives, and consolidate pods onto more efficient compute resources
Real-Time Resource Adjustment
Automatically adjusts compute resources in response to real-time changes in workload demand
Read-Only to Automated Scaling Progression
Supports graduated deployment model starting from read-only recommendations and progressing to continuous automatic optimization
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
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.
Automated Workload Scaling
Dynamically tunes Cloud Autoscaler to respond to changing application workloads in real time, automatically adjusting cluster capacity without manual intervention or application code modifications.
Continuous Intelligent Tuning
Provides continuous stream of performance data that enables optimization of node and pod performance automatically, eliminating the need for manual application tuning or recommendation implementation.
Multi-Platform Support
Supports optimization across Amazon EKS, Spark, Amazon EMR on EKS, and Amazon EMR environments with unified resource management capabilities.
Utilization and Performance Metrics
Increases resource utilization up to 80%, improves application performance by 20% on average, and reduces infrastructure costs by 30% on average through automated optimization.
Standard contract
No
No
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