IBM Turbonomic SaaS

What do you like best about the product?

I work as a Software Engineer dealing with hybrid cloud service and Kubernetes architectures, but the thing I frequently think about it how to keep our applications available without painfully spent budget in public clouds. Use Turbonomic mainly to combine application performance and infrastructure utilization across AWS, Azure being used with our on-prem VMware clusters sits on the same landscape now.

When it comes to the workflow, Turbonomic offers us a centralized view of our resource consumption that is very precise. Our engineering and DevOps teams poured massive amounts of time into manually debating capacity planning in advance of use, or tracing performance bottlenecks back to root host constraints. This way, the platform not only successfully maps our dependencies in a distributed system but also codes them automatically showing without any manual intervention how Kubernetes pods interact with which VMs or what storage volumes are attached.

The Ai-Optimisation recommendation is what really makes it sing. Rather than have to guess at how to improve resource allocation across our environments, Turbonomic automatically detects that we have resources sitting idle or facing demand. That means we can safely scale down CPU and memory limits on non-critical workloads in CI/CD pipelines and production deployments, releasing the resources for more high-demand services.

In terms of integrations, it integrates natively to our VMware environment, public cloud instances and K8s clusters. It integrates nicely with our existing observability stack, providing a single pane of glass view for both application and infrastructure health metrics. Visualizing complex resource relationships is a great use case for the UI; it allows us to clearly communicate during incident response between our platform engineers, SREs and product dev teams. In terms of performance the monitoring is stable and able to weather even our busiest traffic peak providing us with real-time data that we can actually believe in

What do you dislike about the product?

The most notable point of friction for my organization is that this solution is not plug-and-play. The first installation process is complex, and the platform's default settings have to be adjusted to fit an organization's specific architecture.

The UI is very powerful, but also very dense. For a new engineering employee or a junior developer, the first time they log in, they may become overwhelmed by the rich interface that contains many graphs and a lot of data. There is a large amount of learning required to understand how Turbonomic differentiates risk and efficiency.

The automation module (which performs the resizing functionality) lacks the required maturity. We still have to manually approve every suggestion of an automation module. At this point, we have no confidence that an automated resizing of the critical database or the Master node or Master database will be safe doing it unapproved, in an automated fashion. There is a similar friction point with the reporting modules. The reporting modules also lack the required flexibility to create, with minimal effort, the reports that meet our needs. The licensing costs of the module are also excessive. For organizations with a limited infrastructure, Turbonomic is a very difficult sell, as it has a very limited scope and large costs. The module is only a very large organization focused tool. The larger the infrastructure, the more the ROI.

What problems is the product solving and how is that benefiting you?

Overprovisioning is a habit that Turbonomic tries to help with. Our engineers used to provision huge CPU and server memory to production workloads just to be safe. As you might guess, it resulted in huge cloud waste. Turbonomic assists us in margins and provides us with empirical data to rightsize and make infrastructure more efficient, without jeopardizing the application’s integrity.

From the operational side, it helps us a lot with the infrastructure tuning manual effort. Now, when a performance degradation happens, and we need to check the infrastructure, we have an adequate dashboard to help us rule out or confirm infrastructure starvation, and this has helped us a lot with the performance degradation troubleshooting.

From the business side, Turbonomic provides a measurable ROI. We've been able to save money on monthly AWS and Azure bills thanks to it finding “zombie” instances, and uns used and oversized Virtual Machines. Turbonomic changed our capacity planning from a reactive approach to a proactive one. This saved time for our teams to monitor and manage the physical infrastructure and allowed us to concentrate on building more features.

What do you like best about the product?

As a Software Engineer who collaborates with both the Development and the SRE teams, I find Turbonomic to be a valuable tool as it grants visibility and strategic insight to our hybrid cloud infrastructure, which is composed of both AWS and on-prem VMware. Our architecture is complex, as we focus on building internal enterprise applications such as our Human Resource Management System (HRMS), as well as our fully automated billing system. The architecture also utilizes microservices along with Node.JS and Python.

Before Turbonomic, it was mostly guesswork when managing resources for the environments we provisioned. We had to provision more CPU and Memory than required to work around latency issues during peak times. Turbonomic’s solution offers a considerable amount of information pertaining to the resources and how they impact the applications. The solution has also aided managing a lot of things related to Kubernetes pods. It doesn’t show us a single hot node and call it a day. It shows us the various automations it can carry out to ‘remove’ some resource burden by allocating pods and nodes dynamically.

It also addresses the gaps between engineering and operations. If we have to deploy a resource-intensive billing system, the operations team can examine the dependency mapping to understand how the new system impacts the overall infrastructure and what trade-offs can be made in the systems to accommodate the new billing system.

What do you dislike about the product?

We can expect this to take considerable time to set up as a connector to all your tools, spanning APM, cloud service providers, Kubernetes, vCenter, etc.

In addition, gathering the many alerts and recommendations produced by the system will take considerable time. The system will become very pushy and nagging if the recommendations are not acted on. For example, one of the recommendations was to downscale a certain number of workers to optimize the cloud resources. One of the workers was used to process background tasks. The recommendation was based on the worker being underutilized on average, but it was actually downscaled the worker that processed the tasks. If time is not spent continually to manage the system, then the system will have to be configured to operate without any automation.

The user interface is probably the biggest hurdle for the non-technical members of the team, and probably the most overwhelming facet of the system, namely that it is not user friendly and gives little indication of your overall system performance versus your cloud costs.

What problems is the product solving and how is that benefiting you?

The principal problem that Turbonomic addresses for us is eliminating the waste of resources while consistently meeting performance thresholds. In an enterprise environment, the default engineering thinking when there is a problem is to just throw more hardware at the issue. With Turbonomic, we have objective data to more accurately determine the correct size for AWS instances and the container resources we need.

Our monthly cloud billing has gone down noticeably due to the more accurate sizing. The manual monitoring burden on our DevOps engineers has significantly decreased. In the past, our engineers would need to spend hours looking at the Grafana dashboards for resource bottlenecks. With Turbonomic, the DevOps engineers can be reallocated to more productive activities like application development. The burden of resource monitoring has been taken off of our engineers and placed on Turbonomic, which is a more accurate monitoring solution. Our engineers can focus on the business priorities and core applications instead of dealing with resource allocation and planning activities with much less engagement with the infrastructure planning teams.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

IBM Turbonomic automatically optimizes application performance and infrastructure costs, solving issues like over-provisioning and cloud cost waste by rightsizing resources across environments.

What do you like best about the product?

I like that Turbonomic goes beyond basic monitoring by providing automated resource optimization. It helps ensure applications get the right amount of CPU and memory, while also avoiding over-provisioning. Its Kubernetes and cloud integrations make it especially useful in modern DevOps environments.

What do you dislike about the product?

The learning curve can feel steep at first, and making sense of all the optimization actions takes some familiarity with infrastructure and Kubernetes. For beginners, the interface and underlying concepts may come across as complex and a bit overwhelming initially.

What problems is the product solving and how is that benefiting you?

IBM Turbonomic helps address over-provisioned and under-utilized infrastructure by continuously analyzing application demand and automatically optimizing how resources are allocated. Rather than manually tweaking CPU, memory, or scaling policies, the platform recommends or can execute actions that keep performance steady while cutting unnecessary cloud and infrastructure costs. For me, this means less time spent on manual tuning and more confidence that my applications are running efficiently without overspending.

What do you like best about the product?

I really appreciate how IBM Turbonomic reduces the bill for our clients without them needing to get involved in all the technical details. It's pretty neat how this tool has helped us build trust with our clients, enabling them to scale their companies with more tools from us. In a sort of indirect way, this has boosted growth for both our business and our clients' businesses.

What do you dislike about the product?

So I think the biggest hurdle with it is its steep learning curve and initial configuration complexity which can delay the time-to-value for clients when they always expect instant results. While the core of the platform, which is AI, is really awesome, but the reporting and dashboarding thing is really rigid, making it difficult for me to present some high-level business-centric KPIs that I have to mostly present and is asked for. So I need a lot of manual data work to do while implementing and generating KPIs. Similarly, the licensing cost is really high and tough for those small-scale Salesforce implementations that makes me unable to pitch this tool for them.

What problems is the product solving and how is that benefiting you?

IBM Turbonomic's AI-driven autopilot optimizes resource allocation in real-time, balancing performance and cost-efficiency. It eliminates over-provisioning guesswork, reduces client cloud bills, and maintains high application speed, fostering client trust and business growth while ending 'war room' culture.