We leverage IBM Turbonomic to manage our 28 on-premises data centers, effectively optimizing resources and ensuring their operational efficiency. It helps us automate workload scaling to guarantee consistent application performance. Additionally, while we primarily use Turbonomic for on-premises management, we also utilize it within our AWS environment for cost optimization purposes. This allows us to identify and implement cost-saving measures within the cloud.

One of our key challenges was finding a tool that could provide a holistic view of our entire environment and report back key data points, such as CPU and memory usage. This would allow us to identify potential areas for cost savings. We were frequently receiving requests to increase resources like memory and CPU, so we needed a tool that not only gave us historical data but also empowered us to take action, whether that meant automating changes or manually making the changes and IBM Turbonomic does that.

We use IBM Turbonomic in a hybrid cloud environment. Although it supports multi-cloud capabilities, we currently operate in a single-cloud setting.

Turbonomic offers visibility into our environment's performance, spanning across applications, underlying infrastructure, and protection resources. 

The visibility and analytics help to bridge the data gap between disparate IT teams such as applications and infrastructure. This is important for awareness collaboration, cost saving, and helping to design and improve our application.

Enhanced visibility and data analytics have contributed to a significant reduction in our mean time to resolve. Tools like Turbonomic provide crucial visualization and insights, empowering us to make data-driven decisions instead of relying on assumptions as we did before. This newfound transparency translates to a massive improvement, going from complete darkness to having a clear 100 percent view of the situation.

Although our applications are not optimized for the cloud we have seen some improvement in response time.

IBM Turbonomic empowers us to achieve more with fewer people thanks to automation. Previously, customers frequently contacted us requesting resource increases to resolve issues. Now, we have a tool that allows us to objectively assess their needs, leading to a deeper understanding of our applications. This solution also generates significant cost savings in the cloud and optimizes hardware utilization within our data centers. Its AI algorithm intelligently allocates servers on hosts, maximizing efficiency without compromising performance. By fine-tuning resource allocation without causing performance bottlenecks, Turbonomic extends the lifespan of existing hardware, postponing the need for new purchases. This effectively stretches our capital expenditure budget. We started to see the benefits of IBM Turbonomic within the first 60 days.

IBM is a fantastic partner. Their tech support has been outstanding, and the product itself is excellent - a very solid offering.

By automating resource management with Turbonomic, our engineers are freed up to focus on more strategic initiatives like innovation and ongoing organizational projects. Previously, manually adding resources was a time-consuming process that interrupted workflows. Now, automation handles scaling efficiently, saving us thousands of man-hours and significant costs.

It has illuminated the need for SetOps. It has highlighted areas of overspending, and the actions we've taken have demonstrated significant cost savings.

IBM Turbonomic has positively impacted our overall application performance.

IBM Turbonomic has helped reduce both CAPEX and OPEX. It has also significantly reduced cloud build times.

On-premises, one advantage I find particularly appealing is the ability to create policies for automatic CPU and memory scaling based on demand. This improves elasticity and responsiveness to application needs. Additionally, IBM Turbonomic helps us track and quantify cloud cost savings, facilitating informed decision-making.

We're also implementing FinOps principles. By collaborating with other departments, we can demonstrate the cost implications of various resource allocations. For example, specifying a minimum processor requirement might incur higher cloud costs. This increased awareness through IBM Turbonomic allows us to predict impact and make cost-effective decisions.

Turbonomic can modernize the look and feel, making it more user-friendly to access and obtain information.

I have been using IBM Turbonomic for over two years.

IBM Turbonomic is one of the most stable solutions that I have worked with in the last 26 years.

IBM Turbonomic is highly scalable.

The technical support team is both responsive and knowledgeable. They quickly escalate complex issues to someone who can resolve them efficiently.

Positive

The initial deployment proved complex due to our extensive environment, encompassing over 23,000 VMs. This necessitated a year-long rollout, overseen by a dedicated team comprising a salesperson and a senior technical expert.

IBM Turbonomic helped us with the implementation. They were a great partner.

We have seen a return on investment with IBM Turbonomic.

IBM Turbonomic is an investment that we believe will deliver positive returns. We justified the purchase based on its potential to significantly reduce costs, which will allow us to recoup the investment and generate additional benefits. This ultimately makes refinancing a worthwhile option.

I would rate IBM Turbonomic ten out of ten.

We need to perform regular maintenance due to the frequent release of updates.

I recommend that users review their internal processes and partner with other departments to facilitate company-wide implementation. Collaboration will be crucial, so ensure they have a FinOps practice in place, or establish one if necessary.

I mostly provide it to my clients. There are multiple reasons why they would use it depending on the client's needs and their solution. 

Turbonomic provides visibility and analytics into an environment’s performance. The visibility and analytics help bridge the data gap between disparate IT teams, such as Applications and Infrastructure. Having this visibility, specifically for cloud optimization, is extremely important

This has helped reduce our mean time to resolution (MTTR). On average there is about a 10% to 20% reduction, but it can be up to 60%.

Turbonomic has shortened application response time. It has made them more agile.

It's very good for optimizing the monitoring of the public cloud, private cloud, hybrid cloud, and/or Kubernetes. There are some health tools. It is extremely good for that. It is good for our clients to have visibility. It helps to have a complete view of what is going on.

Their automation has helped engineers focus on innovation and ongoing modernization projects. It has saved us about 30% of our work time. Having visibility for particular solutions helped resolve issues, troubleshoot the management of clusters, and so on. It helped to reallocate resources to other parts of the business.

Our clients have seen about 10%-20% of savings from utilizing Turbonomic. 

It is a good holistic platform that is easy to use. It works pretty well.

It can be more agnostic in terms of the solutions that it provides. It can include some other cost-saving methods for the public cloud and SaaS applications.

I have been in the IT industry for about 25 years, and I have been working with this solution for about six years.

It is extremely stable. It works perfectly.

The scalability is okay. It sometimes has problems with the hybrid nature of things, but it is fairly scalable.

We do not use their support. Everything is done in-house.

We have used different ones provided by VMware. The solution is specific to the client. Turbonomic is one of the solutions we provide. It is extremely good and very easy to use. 

I am not involved in its deployment. In terms of maintenance, there are general updates, and making sure the platform works and you are getting what you need from it.

The deployment model depends on the requirements, but 90% of the time, it is in the cloud. In certain classes, it is deployed in the cloud, managing multiple hybrid infrastructures between the cloud and on-prem. In certain circumstances, it is separated between different sites across the globe.

I would definitely recommend a trial. It is a very good product, and it is worth its weight. It is something that is invaluable to most customers.

I would rate Turbonomic a nine out of ten.

We primarily used it for the rightsizing of VMs. I have used it in both Azure and AWS. We also used it for the right categorization of disks.

We also used it quite a bit for comparison. We wanted to see if we migrate from on-prem to the cloud, what the cost would look like. We used it for what-if analysis.

Rightsizing and right categorization are part of optimization exercises. Turbonomic provides a single platform to help optimize costs and resource efficiency.

It provides good visibility of performance at the resource level. This visibility and analytics have helped bridge the data gap between disparate IT teams such as Applications and Infrastructure.

The visibility and analytics from Turbonomic have not helped reduce our mean time to resolution. We only used it for cost savings and not optimization.

Turbonomic has not impacted our application performance. You can do it if you integrate it with a tool like Dynatrace but not in itself.

Turbonomic can optimize the monitoring of public cloud, private cloud, hybrid cloud, and/or Kubernetes. That is where it specializes. With respect to the cloud, their algorithm is pretty good, and their recommendations are relatively trustworthy as compared to other tools. For cloud optimization, it is pretty good. It is also pretty good for balancing on-prem resources.

On the on-prem side, we had some automation or scheduling in place. On the cloud side, we did not do any scheduling. On the on-prem side, it would automatically go and make the changes needed, but on the cloud side, we took the recommendations, and we made the changes ourselves. We did not schedule them in the cloud. It is hard to quantify the time saved, but the analysis part is pretty good. We must have saved time and money.

Turbonomic helped to optimize costs and automate the changes on-prem. There were savings, but I do not have an exact number because we did it in phases. The first time, there would be more savings, and from the second round, they would slow down because you already reaped the benefit from the first-time recommendations. We did not do all the changes at once, so I do not have the numbers, but typically, any organization would have 20% savings in VMs and disks. Turbonomic does a good job. It depends on how big an organization is, but on average, the tool can cut down the VM cost by 20%.

Rightsizing is valuable. Its recommendations are pretty good. It was useful for the rightsizing and the right categorization of virtual machines and disks.

I would like Turbonomic to add more services, especially in the cloud area. I have already told them this. They can add Azure NetApp Files. They can add Azure Blob storage. They have already added Azure App service, but they can do more.

I have been using this solution for three years. I have used it in my current company, and before that, I used it in another company. I brought it into that company. We did the RFP. There were quite a few suppliers who came and did the presentation. We then selected Turbonomic. That was before IBM took it over.

There were some issues, but they were not very frequent. 

It was good. I did not see any big issues, but we did not scale it a lot. We added a couple of accounts later, and it was okay.

Their support was pretty good. There were some very good engineers who helped us. Turbonomic's support is top-notch. When needed, they brought specialists. It was pretty good. I would rate them a nine out of ten.

Positive

I was just using cloud-native tools. Turbonomic saves time on the cumbersome tasks that we have to do with the native tools. Its recommendations are good.

Turbonomic is real-time. When you are using cloud-native tools, you are chasing the wheel, and by the time you are done with the analysis, the data is too old. Turbonomic's algorithm provides real-time analysis and recommendations, which are pretty useful.

In terms of application awareness, Turbonomic did not provide a lot of value. We could group it, but cloud-native tools provided the tagging capability. We did not do a lot with Turbonomic in terms of application visibility.

We did not go with the SaaS offering. We most probably implemented it on-prem. I was not involved in its deployment in this company, but I was involved in my previous company.

I would rate it a seven out of ten in terms of the setup. It is not as straightforward as they say during the sales calls, but most of the complications that we faced were from our side.

We did it ourselves with the help of Turbonomic engineers. It probably took us two months because of the processes that we had to follow.

It required people from all areas. We needed people from security, on-prem data center management, and storage teams, but they were not required for the entire two months. On average, three to four people were required on a need basis.

The optimization with Turbonomic reduced our organization's OPEX.

I have not seen Turbonomic's new pricing since IBM purchased it. When we were looking at it in my previous company before IBM's purchase, it was compatible with other tools.

We evaluated four or five tools, such as AppTio and Flexera. We went for Turbonomic because the algorithm was very good, conservative, and trustworthy.

Their support was also very good. There was a lot of hand-holding. There were regular meetings. For any questions we had, good support was always available.

I have not seen the new product after IBM acquired it, but based on my experience, I would advise building trust slowly. Whatever recommendations it is giving, first validate them. After the trust is established, you can do more things in terms of implementing recommendations.

My experience with Turbonomic has been good. I would rate it an eight out of ten.

We typically use it for optimizing the performance and resource allocation of virtual machines.

It offers visibility and analytics for monitoring performance across our environment, starting from the application layer and extending down the stack to the underlying infrastructure resources. Specifically, it concentrates on optimizing memory and CPU resources as part of our focus on hardware and environment optimization, without delving into additional aspects.

There was a single project where it helped us reduce the size of hundreds of VMs. This represents the only example with which I am familiar.

It's important to note that optimizing the monitoring of our private cloud is not the primary function of this tool. It is specifically utilized for optimization purposes. We employ it for tasks such as trending predictions and VM utilization performance. However, for monitoring, we rely on a completely different tool.

It has resulted in cost savings, specifically at the infrastructure layer.

The primary features we have focused on are reporting and optimization.

I have been working with it for more than five years.

It has proven to be highly stable.

I haven't directly interacted with tech support, but based on what I've heard, the overall experience was satisfactory.

Maintenance is necessary, and one person is sufficient for the task.

Overall, I would rate it eight out of ten.