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CAST AI vs IBM Turbonomic comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

CAST AI
Ranking in Cloud Cost Management
19th
Average Rating
8.0
Reviews Sentiment
9.2
Number of Reviews
1
Ranking in other categories
No ranking in other categories
IBM Turbonomic
Ranking in Cloud Cost Management
1st
Average Rating
8.8
Reviews Sentiment
7.4
Number of Reviews
205
Ranking in other categories
Cloud Migration (3rd), Cloud Management (4th), Virtualization Management Tools (4th), IT Financial Management (1st), IT Operations Analytics (5th), Cloud Analytics (1st), AIOps (11th)
 

Mindshare comparison

As of February 2026, in the Cloud Cost Management category, the mindshare of CAST AI is 1.5%, up from 1.5% compared to the previous year. The mindshare of IBM Turbonomic is 6.3%, down from 14.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Cost Management Market Share Distribution
ProductMarket Share (%)
IBM Turbonomic6.3%
CAST AI1.5%
Other92.2%
Cloud Cost Management
 

Featured Reviews

RA
Global Head - DevOps and AIOps Startegist at a manufacturing company with 10,001+ employees
Automation has optimized Kubernetes costs and right-sizing cuts manual cluster work
CAST AI helps us with automated node provisioning, workload right-sizing, intelligent auto-scaling, and overall cost visibility of the containerized systems that we have on the cloud. The best features CAST AI offers are the Kubernetes auto-scaling mechanism, continuous analysis of the pod-level CPU and memory usage, and ensuring that workload right-sizing is being done and our nodes are not over-provisioned. Identifying inaccuracies in the resource request is what we find quite useful with CAST AI. It definitely saves time and money as well, along with peace of mind because CAST AI continuously analyzes the pod-level CPU and memory usages. This helps us to optimize the request and the limits adjustments on our usage pattern, and overall, right-sizing improves the packing and reduces the wasted compute that we have in the cloud. In terms of overall impact on the organization, CAST AI has definitely helped us optimize our Kubernetes resources and given us automation capabilities. It is definitely helping us reduce the manpower and overall compute which is wasted. We can definitely save these using CAST AI. We will be notified upfront and proactively about any wastages that are happening, or any cost leakages that are happening in our system.
Dan Ambrose - PeerSpot reviewer
Infrastructure Engineer 4 at a tech vendor with 1,001-5,000 employees
Helps visibility, bridges the data gap, and frees up time
We use IBM Turbonomic in a hybrid cloud environment. Although it supports multi-cloud capabilities, we currently operate in a single-cloud setting. Turbonomic offers visibility into our environment's performance, spanning across applications, underlying infrastructure, and protection resources. The visibility and analytics help to bridge the data gap between disparate IT teams such as applications and infrastructure. This is important for awareness collaboration, cost saving, and helping to design and improve our application. Enhanced visibility and data analytics have contributed to a significant reduction in our mean time to resolve. Tools like Turbonomic provide crucial visualization and insights, empowering us to make data-driven decisions instead of relying on assumptions as we did before. This newfound transparency translates to a massive improvement, going from complete darkness to having a clear 100 percent view of the situation. Although our applications are not optimized for the cloud we have seen some improvement in response time. IBM Turbonomic empowers us to achieve more with fewer people thanks to automation. Previously, customers frequently contacted us requesting resource increases to resolve issues. Now, we have a tool that allows us to objectively assess their needs, leading to a deeper understanding of our applications. This solution also generates significant cost savings in the cloud and optimizes hardware utilization within our data centers. Its AI algorithm intelligently allocates servers on hosts, maximizing efficiency without compromising performance. By fine-tuning resource allocation without causing performance bottlenecks, Turbonomic extends the lifespan of existing hardware, postponing the need for new purchases. This effectively stretches our capital expenditure budget. We started to see the benefits of IBM Turbonomic within the first 60 days. IBM is a fantastic partner. Their tech support has been outstanding, and the product itself is excellent - a very solid offering. By automating resource management with Turbonomic, our engineers are freed up to focus on more strategic initiatives like innovation and ongoing organizational projects. Previously, manually adding resources was a time-consuming process that interrupted workflows. Now, automation handles scaling efficiently, saving us thousands of man-hours and significant costs. It has illuminated the need for SetOps. It has highlighted areas of overspending, and the actions we've taken have demonstrated significant cost savings. IBM Turbonomic has positively impacted our overall application performance. IBM Turbonomic has helped reduce both CAPEX and OPEX. It has also significantly reduced cloud build times.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"We observed a 20 to 30% reduction in Kubernetes infrastructure cost, node utilization is improved, and we could see a 60 to 70% reduction in our manual cluster optimization efforts that we used to put initially."
"We have a system where our developers automate machine builds, and that is constantly running out of resources. Turbonomic helps us with that, so I don't have to keep buying hardware. The developers always say, "They don't have enough. They don't have enough. They don't have enough," when they just configured it improperly. Therefore, Turbonomic helps us identify configuration issues on their side so it doesn't cost me money on the other end to buy resources that I don't really need."
"Turbonomic has helped optimize cloud operations and reduced our cloud costs significantly. Overall, we are at about 40 percent savings, and we spend about three million a year just in Azure. It reduces the size of the VMs, putting them into the right template for usage. People don't realize that you don't have to future-proof a virtual machine in Azure. You just need to build it for today. As the business or service grows, you can scale up or out. About 90 percent of all the costs that we've reduced has been from sizing machines appropriately."
"We have VM placement in Automated mode and currently have all other metrics in Recommend mode."
"The most important feature to us is an objective measurement of VM headroom per cluster. In addition, the ability to check for the right-sizing of VMs."
"The tool provides the ability to look at the consumption utilization over a period of time and determine if we need to change that resource allocation based on the actual workload consumption, as opposed to how IT has configured it. Therefore, we have come to realize that a lot of our workloads are overprovisioned, and we are spending more money in the public cloud than we need to."
"In our organization, optimizing application performance is a continuous process that is beyond human scale. We would not be able to do the number of actions that Turbonomic takes on a daily, weekly, and monthly basis. It is humanly impossible with the little micro adjustments that it can make. That is a huge differentiator. If you just figure each action could take anywhere very conservatively from five to 10 minutes to act upon, then you multiply that out by thousands of actions every month, it is easily something where you could say, "I am saving a couple of FTEs.""
"With Turbonomic, we were able to reduce our ESX cluster size and save money on our maintenance and license renewals. It saved us around $75,000 per year but it's a one-time reduction in VMware licensing. We don't renew the support. The ongoing savings is probably $50,000 to $75,000 a year, but there was a one-time of $200,000 plus."
"The recommendation of the family types is a huge help because it has saved us a lot of money. We use it primarily for that. Another thing that Turbonomic provides us with is a single platform that manages the full application stack and that's something I really like."
 

Cons

"The documentation of CAST AI can definitely be improved for first-time users."
"It would be nice for them to have a way to do something with physical machines, but I know that is not their strength Thankfully, the majority of our environment is virtual, but it would be nice to see this type of technology across some other platforms. It would be nice to have capacity planning across physical machines."
"I would love to see Turbonomic analyze backup data. We have had people in the past put servers into daily full backups with seven-year retention and where the disk size is two terabytes. So, every single day, there is a two terabyte snapshot put into a Blob somewhere. I would love to see Turbonomic say, "Here are all your backups along with the age of them," to help us manage the savings by not having us spend so much on the storage in Azure. That would be huge."
"Recovering resources when they're not needed is not as optimized as it could be."
"We don't use Turbonomic for FinOps and part of the reason is its cost reporting. The reporting could be much more robust and, if that were the case, I could pitch it for FinOps."
"Additional interfaces would be helpful."
"Enhanced executive reporting standard with the tool beyond the reports that can be created today. Something that can easily be used with upper management on a monthly or quarterly basis to show the impact to our environment."
"Remove the need for special in-house knowledge and development."
"The automation area could be improved, and the generic reports are poor. We want more details in the analysis report from the application layer. The reports from the infrastructure layer are satisfactory, but Turbonomic won't provide much information if we dig down further than the application layer."
 

Pricing and Cost Advice

Information not available
"The pricing and licensing are fair. We purchase based on benchmark pricing, which we have been able to get. There are no surprise charges nor hidden fees."
"If you're a super-small business, it may be a little bit pricey for you... But in large, enterprise companies where money is, maybe, less of an issue, Turbonomic is not that expensive. I can't imagine why any big company would not buy it, for what it does."
"IBM Turbonomic is an investment that we believe will deliver positive returns."
"It's worth the time and money investment if you can afford it."
"I consider the pricing to be high."
"The product is fairly priced right now. Given its capabilities, it is excellently priced. We think that the product will become self-funding because we will be able to maximize our resources, which will help us from a capacity perspective. That should save us money in the long run."
"I'm not involved in any of the billing, but my understanding is that is fairly expensive."
"Licensing is per socket, so load up on the cores rather than a lot of lower core CPUs."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
10%
Computer Software Company
10%
Manufacturing Company
9%
Insurance Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business41
Midsize Enterprise57
Large Enterprise147
 

Questions from the Community

What is your experience regarding pricing and costs for CAST AI?
In terms of setup cost, licensing, and pricing, I find the experience good. It's enterprise-grade, and the pricing is usage-based with no heavy upfront setup cost, which makes the onboarding straig...
What needs improvement with CAST AI?
The documentation of CAST AI can definitely be improved for first-time users. When we are onboarding a new user, the team needs some time to tune the policies and build confidence in automation bec...
What is your primary use case for CAST AI?
Our main use case for CAST AI is that we use it as a cloud provider and for Kubernetes clusters. We are using secure access roles and all those requirements for right-sizing the containers' workloa...
What is your experience regarding pricing and costs for Turbonomic?
It offers different scenarios. It provides more capabilities than many other tools available. Typically, its price is set as a percentage of the consumption of some of our customers' services. The ...
What needs improvement with Turbonomic?
The implementation could be enhanced.
What is your primary use case for Turbonomic?
We use IBM Turbonomic to automate our cloud operations, including monitoring, consolidating dashboards, and reporting. This helps us get a consolidated view of all customer spending into a single d...
 

Also Known As

No data available
Turbonomic, VMTurbo Operations Manager
 

Interactive Demo

Demo not available
 

Overview

 

Sample Customers

Information Not Available
IBM, J.B. Hunt, BBC, The Capita Group, SulAmérica, Rabobank, PROS, ThinkON, O.C. Tanner Co.
Find out what your peers are saying about IBM, Nutanix, Apptio and others in Cloud Cost Management. Updated: January 2026.
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