

Find out in this report how the two Cloud Cost Management solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
CAST AI has reduced approximately 40% of our AWS bills and AWS cloud bills.
The ROI was visible within a few months through cloud cost reduction alone.
We have seen a return on investment, with money saved equating to approximately 30 to 40% ROI.
The governance and security of CAST AI are solid, providing sufficient visibility into cluster changes and optimization actions.
Response times are reasonable, and the team is very knowledgeable.
I would rate the customer support 10 out of 10.
It is a SaaS platform that will scale automatically.
It scales effectively with cluster growth and increasing workload complexity.
Even with these, the scalability of CAST AI is good.
In most cases, the optimization suggestions are practical and effective.
There are many pros here, including easy onboarding, simple deployment, and excellent Kubernetes visibility, strong spot instance automation, and automated right-sizing.
Some additional improvements needed with CAST AI would include enhanced forecasting capabilities and more detailed workload-level cost analytics, which would be very useful.
Most improvements could include advanced customization, advanced reporting, and documentation on a large scale.
Pricing was reasonable considering the cost savings achieved.
In terms of pricing, I believe the pricing is reasonable because of the amount of savings and operational efficiency it delivers, making it easier to justify the investment.
I have not found the price to be too high for the features it provides.
CAST AI has had a positive impact on my organization through cost reduction. On average, I think the savings are between 15 and 20 percent, and for certain workloads, these savings can be even higher.
In the last Q2 result, because of using CAST AI, we have reduced our manpower, money, and cost by 20 to 30%, which indicates substantial funding reduction.
With CAST AI, nodes are added or removed automatically as workloads change, helping us maintain application performance while reducing unnecessary cloud costs.
| Product | Mindshare (%) |
|---|---|
| IBM Turbonomic | 5.9% |
| CAST AI | 1.6% |
| Other | 92.5% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 1 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 41 |
| Midsize Enterprise | 57 |
| Large Enterprise | 147 |
CAST AI is revered for its powerful cloud optimization capabilities, notably in cost reduction, performance enhancement, and security strengthening. It automates resource management and scales operations efficiently, leading to significant organizational improvements in efficiency, cost savings, and smoother cloud integration and management.
IBM Turbonomic enhances IT efficiency with automation, capacity planning, and reporting features, enabling organizations to optimize resource utilization and improve performance through advanced workload management and scenario analysis.
IBM Turbonomic equips organizations with robust capabilities for dynamic resource allocation and informed decision-making. Its planning module provides scenario analysis, right-sizing recommendations, and a customizable dashboard for tailored insights. Automation features improve workload placements and resource efficiency, while forecasting capabilities enhance performance. Simulation of environments helps in decision-making, leading to significant savings in cloud and hardware management. There is a need for a more intuitive interface, enhanced navigation, and improved customization in reporting with integration potential with third-party applications. Transition to the HTML5 interface and stronger training resources are among anticipated improvements.
What are the key features of IBM Turbonomic?IBMTurbonomic is implemented across industries such as cloud management and virtualization, helping organizations balance clusters, optimize virtual machine performance, and manage Azure configurations. In resource-monitored environments like VMware and XenServer, its features facilitate load balancing, VM rightsizing, and automation shutoffs. Industries can rely on its insights for cost-saving measures, ensuring efficient resource allocation for hybrid and cloud environments.
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