Actionable Automation: I like that it doesn't just give alerts; it executes real-time actions to prevent performance bottlenecks.

Reduced Manual Effort: It eliminates the "firefighting" by automatically right-sizing VMs and moving workloads before issues impact users.

Trusted Decisions: The AI-driven recommendations are accurate enough to move from "manual approval" to "full automation" quickly. Review collected by and hosted on G2.com.

Steep Learning Curve: The platform is incredibly powerful, but it takes significant time for new users to understand the dashboards and navigate the complex resource relationships.

Complex Initial Configuration: Setting up the tool and tuning it to specific workloads can be a lengthy process, especially in large-scale hybrid or multi-cloud environments.

Aggressive Recommendations: Sometimes the AI-driven suggestions feel too aggressive; you often need to manually validate them before trusting the "auto-execute" feature. Review collected by and hosted on G2.com.

I love how IBM Turbonomic accurately analyzes workloads and provides clear, actionable recommendations. The automation is impressive because it manages resource adjustments in real time, eliminating the need for constant manual checks. I appreciate the visibility it offers across both cloud and on-prem environments, making performance and cost management much more straightforward. Its accurate workload analysis is invaluable to my work as it takes the guesswork out of resource planning, specifying exactly which VM, service, or application needs more or fewer resources. This feature minimizes unnecessary time spent on troubleshooting. I value the proactive approach of IBM Turbonomic, which prevents performance issues before they arise, thereby ensuring a more stable and cost-efficient environment. It’s this forward-thinking capability that truly increases the platform's value for me. Review collected by and hosted on G2.com.

One thing that could be improved in IBM Turbonomic is the initial setup and configuration; it can feel a bit complex for new users. Some dashboards also take time to get used to because there’s a lot of data to interpret. I’d also like to see faster syncing with certain cloud providers, as sometimes recommendations take a little time to reflect real-time changes. Additionally, more customizable alerts would be useful so I can fine-tune notifications based on specific workloads. A cleaner, more modern UI would make navigation simpler, especially for new users. Adding deeper integrations with third-party monitoring tools could make the platform even more seamless and reduce context-switching. Review collected by and hosted on G2.com.

I appreciate IBM Turbonomic for its performance assurance capabilities, which is the main feature that stands out to me. It is designed to automate resource allocations across complex hybrid and multi-cloud environments, ensuring secured application performance. I find the AI-driven closed-loop automation particularly impressive as it not only generates alerts or recommendations but proactively executes policy-driven actions automatically. This level of automation streamlines processes and assures performance, adding significant value. Additionally, the initial setup was quite easy for us, simplifying the integration with our existing processes. Overall, IBM Turbonomic’s ability to manage and assure the performance of applications efficiently in diverse cloud environments is something I like a lot. Review collected by and hosted on G2.com.

I find IBM Turbonomic to be somewhat complex in nature, which might make it challenging to navigate or fully utilize without improvement. Additionally, the running costs associated with using IBM Turbonomic are a concern for me. It seems that the cost structure could be optimized to improve the overall trust and adoption of the product. Review collected by and hosted on G2.com.

I really like how IBM Turbonomic automatically manages workload placement and movement (VMs and pods) seamlessly, ensuring stable performance even during peak demand. Its real-time scaling decisions—both vertical and horizontal—help optimize resources and prevent performance congestion, while streamlining cluster consolidation.

IBM Turbonomic’s automation and predictive intelligence are standout features, helping to reduce manual overhead and anticipate performance issues before they occur. The support team’s responsiveness and expertise make resolving configuration or scaling challenges much easier. In daily use, Turbonomic has proven to improve workload efficiency and optimize operational costs effectively Review collected by and hosted on G2.com.

One area that could be improved is the user interface, which can feel a bit complex for new users. It takes some time to get comfortable navigating the dashboards and interpreting all the data. Additionally, while customer support is helpful, response times can occasionally be slower during peak periods. Another minor drawback is that the initial setup and integration with hybrid or multi-cloud environments can require extra configuration effort. Once it’s up and running, though, the system performs very well and delivers great visibility and control. Review collected by and hosted on G2.com.

I love IBM Turbonomic for its powerful automation that significantly contributes to cost savings and guarantees application performance, which is crucial for my operations. It solves performance issues efficiently, reduces costs by eliminating unnecessary resources, and replaces the need for manual tuning with its automation capabilities. This not only saves me time and money but also reduces headaches, ensuring that my applications run smoothly and effectively. I appreciate how much it has enhanced the performance optimization and cost-saving strategies within my organization, making it a valuable tool in my toolkit. Review collected by and hosted on G2.com.

I find the ease of setup to be an area that could use improvement. The UI design feels lacking and could be more intuitive. Additionally, the explanations for recommendations aren't as clear as I'd like, making it difficult to fully understand some automated suggestions. Integration with other tools and the reporting features also leave room for enhancement. Lastly, the pricing structure could be more straightforward and transparent. Review collected by and hosted on G2.com.

The best thing about it is it helps me optimize my cloud costs by identifying over-provisioned resources Review collected by and hosted on G2.com.

The only problem is the initial setup and configuration. It is difficult for someone new Review collected by and hosted on G2.com.

I like IBM Turbonomic for its automation and data-driven recommendations. It removes the guesswork from infrastructure management by continuously analyzing workload demand and providing clear insights. I also appreciate its ability to integrate well with cloud platforms and monitoring tools like VMware and public cloud providers, which makes it versatile. Review collected by and hosted on G2.com.

Initial setup and fine tuning can be complex and time-consuming, the interface can also feel overwhelming at times. Some recommendations would be improving UX and simplifying onboarding. Review collected by and hosted on G2.com.

I find IBM Turbonomic to be a powerful platform that optimizes infrastructure performance across multiple environments. I really appreciate its automated real-time resource allocation decisions, which lead to cost efficiency. I'm a big fan of the automation aspect, particularly in scaling CPU and memory, as it helps me with cost management. Review collected by and hosted on G2.com.

I think there could be improvements in the reporting and dashboarding features. Review collected by and hosted on G2.com.

I appreciate how IBM Turbonomic has been very helpful in my career by resolving severe issues, proving invaluable to both my role and organization. The mesmerizing infrastructure stands out to me, along with the system's ability to seamlessly integrate with applications like AI, Kubernetes, OpenSearch, and other open-source applications, which are crucial for problem-solving within my organization. I find this platform reliable, with a richer set of libraries compared to previous technologies such as Hadoop. Additionally, even though setup took 2-3 days, the provided documentation was comprehensive enough to facilitate it successfully. Review collected by and hosted on G2.com.

the time taking environment Review collected by and hosted on G2.com.

It integrate with different cloud providers easily like AWS, Azure, GCP, VMWare. Reduces cost using ML models. Review collected by and hosted on G2.com.

Price is high to use it. It itself consume lot of resources. Review collected by and hosted on G2.com.