I love IBM Turbonomic's Actionable Insights; it tells me exactly what to move, scale, or resize to keep applications performing well. The Automation Engine is a big plus because it moves us away from reactive 'firefighting.' I appreciate the Full-Stack Visualization because it maps everything from the application to the storage layer in one view. The 'Placement' and 'Sizing' features are life-savers, taking the guesswork out of resource allocation. I really appreciate the value of the automation in saving a massive amount of time that used to be spent on manual troubleshooting and over-provisioning, and the integration with key tools like IBM Instana, Datadog, ServiceNow, AWS, Azure, and Red Hat OpenShift. The system basically gives us a map of our entire environment, making sense of our infrastructure relationships. Review collected by and hosted on G2.com.

While the tool is powerful, the initial configuration and policy tuning could definitely be more user-friendly. It takes a fair amount of time to set up the 'Targets' and make sure the automation doesn't trigger too many alerts at first. I’d also suggest improving the UI for the reporting dashboard. It's great for deep technical dives, but when I need to pull a quick high-level summary for my manager, it can feel a bit cluttered. Adding some more 'out-of-the-box' executive reporting templates would save us a lot of manual work in data exporting. Review collected by and hosted on G2.com.

I like that IBM Turbonomic automatically takes the right actions to balance application performance and infrastructure cost without manual intervention. Unlike many tools that only provide alerts or dashboards, Turbonomic actually executes actions like rightsizing CPU and memory, scaling workloads, or reallocating resources based on real-time application demand. This reduces manual effort and reactive firefighting significantly. I also appreciate that Turbonomic is application-centric in its decision-making, meaning it focuses on application performance rather than just infrastructure metrics. This ensures that cost optimization never comes at the expense of user experience or SLAs. The fact that it delivers real, measurable value by automatically optimizing resources and has a strong focus on application performance builds trust with engineering teams and has a proven impact on cost reduction, stability, and operational efficiency. Additionally, I appreciate how it reduces manual effort and ongoing firefighting for infrastructure teams. Review collected by and hosted on G2.com.

While IBM Turbonomic is a powerful optimization platform, there are a few areas where it could be better or has room for improvement: 1. Initial Setup and Onboarding Can Be Complex: Getting Turbonomic up and running with the right policies, access permissions, and automation levels takes time and effort—especially in large or highly regulated environments. Teams often need structured guidance and training to avoid misconfigurations early on. 2. Steep Learning Curve for Advanced Features: Some of the advanced capabilities (such as custom policies, complex what-if scenarios, and governance controls) aren’t immediately intuitive. There’s a learning curve that requires time investment from infrastructure or cloud engineers. Review collected by and hosted on G2.com.

What I like most about IBM Turbonomic is its real-time application resource management and automation. It provides clear, actionable insights that help optimize performance while controlling cloud and infrastructure costs. The platform’s ability to continuously analyze workloads and recommend (or automate) scaling decisions reduces manual effort and improves overall system reliability. I also appreciate the visibility it gives across environments, which makes capacity planning and troubleshooting much easier. Review collected by and hosted on G2.com.

One downside of IBM Turbonomic is that the initial setup and integration can be complex, especially in large or hybrid environments. The interface can feel overwhelming for new users due to the amount of data and recommendations presented. Additionally, fine-tuning policies and understanding all automation actions may require a learning curve, and licensing costs can be a concern for smaller teams. Review collected by and hosted on G2.com.

I really like IBM Turbonomic's automated right-sizing recommendations because it clearly shows what resources and applications actually need, optimizing CPU, memory, and storage without constant manual monitoring. Its continuous analysis and clear action recommendations make day-to-day infrastructure optimization much easier for me. Review collected by and hosted on G2.com.

Deployment and onboarding are not very lightweight. It works well once configured but the learning curve and configuration effort at the start could be simpler. A more guided installation flow, clearer documentation for integrations, and ready-made policy templates would make onboarding faster for new teams. Setup was manageable but not exactly plug-and-play. Basic deployment was straightforward, but proper configuration, integration, and policy tuning took some time to get right. Review collected by and hosted on G2.com.

I used IBM Turbonomic to monitor resource usage, ensuring I wasn't billed too much. I appreciate the detailed insights it provides, allowing me to manage my budgets effectively without overextending. I like the real-time workload optimization and automated actions driven by policy, which reduce manual workload and overhead, making things very easy. The resource monitoring feature stands out, and I find the user-friendly insights and detailed data valuable. Review collected by and hosted on G2.com.

The learning curve is a bit difficult for beginners. It's challenging to learn initially without more user-friendly interfaces and detailed guidelines. Also, the initial setup is a bit hard, requiring in-depth documentation review. Review collected by and hosted on G2.com.

I think the UI of IBM Turbonomic is very clear and even beginners can easily navigate and work with it. It's really helpful for new users to configure and understand all the features available on the UI. The product provides very clear recommendations for resource optimization, especially helping in optimizing CPU and memory. It improves resource efficiency, even in cloud environments. Review collected by and hosted on G2.com.

The initial setup was moderate and not very simple, especially for first-time users. It required a proper understanding of the infrastructure and integrations. During the implementation, we faced challenges in configuring the integrations with our existing environments. The UI can feel a bit volumetric initially, but proper planning and documentation review help to sort out these issues. Review collected by and hosted on G2.com.

I like IBM Turbonomic for its intelligent automation that automatically rightsizes resources to maintain performance while reducing cloud costs. It continuously analyzes application demand and adjusts CPU, memory, and cloud instance sizes in real time. This helps prevent performance bottlenecks, eliminates over-resourcing, and maintains SLAs. Review collected by and hosted on G2.com.

It can be complex to set up, has a learning curve, and licensing can be expensive for larger environments. Setup has been complex due to integration with multiple cloud platforms, hypervisors, and permission configurations. Initial setup is quite challenging because it required configuring integrations, permissions, and policies across environments which took some time to get right. Review collected by and hosted on G2.com.

I like how IBM Turbonomic helps identify redundant instances or servers that can be deleted or are underutilized in the cloud. The setup was pretty straightforward, and I appreciate using it across all multi-cloud environments. I find that the AI is good and is genuinely valuable for cloud cost optimization and analyzing multi-cloud platforms to optimize cost and vulnerabilities. Review collected by and hosted on G2.com.

I think IBM Turbonomic could improve by adding a vulnerability detection system. Also, while the AI is good, it needs improvement with handling security threats. Review collected by and hosted on G2.com.

some things I like about turbonomic is :

1. It works based on demand. This service not only monitors the infrastructure but also helps in auto scaling. It resizes, scales resources to maintain its performance.

2. A single place from where public clouds like AWS, Azure and Google Cloud can be managed.

3. Instead of just giving us alerts and us handling the job, it executes the actions automatically. Hence reduces the manual effort and speeds the process.

4. The planning features also helps very greatly for migrations, workload growth etc. Very useful for capacity planning and budgeting. Review collected by and hosted on G2.com.

1. Initial configuration is bit confusing as we need to configure all the details like apps, VMs, storage, cloud costs etc

2. The dashboards etc have lot of features so the learning requires ample amount of time.

3. The cost of environment and models selected the pricing seems pretty high Review collected by and hosted on G2.com.

I like IBM Turbonomic for its clear actionable recommendations, not just metrics. I also appreciate how accurate the recommendations are and how confidently we can automate the actions. It helps us with application resource optimization and cost control, right-sizing VMs and containers to prevent performance issues before users notice them and reducing costs by identifying over-provisioned resources. We also use it for capacity planning and automatic resource scaling. Review collected by and hosted on G2.com.

The UI feels complex at first and the initial setup time takes more time. Review collected by and hosted on G2.com.