

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.
I have seen a return on investment, and the ROI was visible within a few months through cloud cost reduction alone.
The ROI was visible within a few months through cloud cost reduction alone.
CAST AI has reduced approximately 40% of our AWS bills and AWS cloud bills.
I would rate the customer support 10 out of 10.
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 knowledgeable.
It is a SaaS platform that will scale automatically.
CAST AI's scalability is very good; it scales effectively with cluster growth and increasing workload complexity.
It scales effectively with cluster growth and increasing workload complexity.
In most cases, the optimization suggestions are practical and effective.
CAST AI has proven to be stable and reliable in production environments.
There are many pros here, including easy onboarding, simple deployment, and excellent Kubernetes visibility, strong spot instance automation, and automated right-sizing.
More detailed documentation and deeper visibility into certain optimization decisions would also be helpful.
To improve CAST AI, I would like to see more granular reporting, deeper cost allocation insights, and additional customization options for optimization policies.
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.
Pricing was reasonable considering the cost savings achieved
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.
CAST AI has positively impacted our organization by reducing cloud costs, improving resource utilization, and allowing our engineering team to spend less time managing infrastructure and more time on platform improvements.
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 | 10 |
| Midsize Enterprise | 2 |
| Large Enterprise | 3 |
| 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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