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.

Initially, our use case was to reduce cloud spend. But Turbonomic is much more than just a reduction-in-cloud-spend tool. As we went on, it became more about optimizing applications and making sure that they function as expected, while reducing the cost of cloud resources. It became a question of how we make applications function properly, at speed, with the best cost possible, and without creating any risk for the application itself.

Turbonomic has shed light on processes, on how applications actually function for people. The folks in the IT organization still tend to build large, to oversize things, to make sure that their applications perform properly. Turbonomic sheds light on what could be a more efficient application and deployment.

We use it in a multi-cloud environment.

My favorite part of the solution is the automation scheduling. Being able to choose when actions happen, and how they happen, whether that be through an approval process during the workflow, or whether it be someone executing it on a weekend because they're working in their own environment.

We don't use Turbonomic for FinOps and part of the reason is its cost reporting. The reporting could be much more robust and, if that were the case, I could pitch it for FinOps. You might say that's a weakness, but it's not what it's supposed to do.

If it had the reporting, it would be a 10 out of 10.

I've been using IBM Turbonomic for four years.

Since we moved to the SaaS deployment, I haven't noticed any issues. About five years ago when I started evaluating it, there were some on-prem issues, but not with the SaaS solution.

Scalability is not a problem. If you need more, just buy more licenses and it expands. They monitor that and expand your instances. It's not something you need to worry about.

Their tech support is very responsive. They are part of IBM and not just Turbonomic anymore, so they've grown exponentially over time. But I found, in working with their engineers on the tickets we submitted, that they were very responsive, getting back to us as quickly as they could on the challenges we were having. They have been helpful.

Positive

We would go quarter-to-quarter and say, "Okay, go optimize our application environments." We could use Azure Monitor or a couple of other tools that aren't nearly as robust, and without knowing the impact, other than what Azure Monitor gives you. But Azure Monitor, which doesn't do memory metrics, would tell you, "You can reduce size by doing this," but maybe memory was the problem. Turbonomic is much more robust. Before using it, we were doing things in a very manual way. 

The way I got Turbonomic through the door was by saying, "You want to have your entire staff clean up the cloud every quarter?"

The initial deployment is very straightforward. The Kubernetes stuff was a little beyond me because I'm not a Kubernetes person. But once we got somebody who knew Kubernetes involved, it was pretty straightforward. It takes less than a few hours and that's for an enterprise. It can be done very quickly.

We started with the solution on-prem, but I quickly moved it to the SaaS model because with on-prem there's a lot to manage. It's a Kubernetes cluster and you need a Kubernetes administrator. You have to have rights to it. There are a lot of other moving parts when you manage it yourself. Once you move to a SaaS-based solution, the burden of keeping the product upgraded and up to date is on Turbonomic. I don't want to manage updates and patches.

With the SaaS solution, there is no maintenance on our side.

Our internal resources worked with the Turbonomic team. After that, I turned over the application to the team that is going to be supporting the applications, because I have no insight into applications. That's not my role. Turbonomic is meant to be in their hands, not mine.

There were three to four people involved initially. Once you get it installed, you start bringing in your DevOps engineers to have them understand it, and they'll work with the application support people. 

The team grows as large as it has to, depending on how many application teams and DevOps engineers you have. People can manage their applications or they can manage multiple applications. You can divide it up, so the teams vary in size. But it's always going to land as close to the application as it can, to get the right people to make the right decisions. If you're a very large organization, you don't centralize the product. It doesn't work well that way.

Everybody tells me the pricing is high. But the ROIs are great. Like any software, if it sits on a shelf and no one uses it, it's a waste of money. If you implement it and do the right things before you start using it, the ROI is very fast. And then you can justify the cost, because the ROI is very quick.

We had a couple of hiccups, but we planned for about a nine-month ROI, in the course of a three-year plan. If you put the resources into it and you dedicate the time to it, then ROI is very attainable. If you just let the product churn and tell you what's going on, and don't do anything, then you don't get ROI and don't actually reduce your cloud spend.

I looked at CloudHealth, Cloudability, and one other. We went with Turbonomic because of the intelligence engine. It uses AI to make determinations on data that's coming in at a faster pace than humans can comprehend. People can't monitor a thousand VMs and keep track of them on a daily, hourly, or minute-by-minute basis. With Cloudability, it's not done as efficiently and it's not done with AI. It has cloud-native optimization tools, and they're not as accurate. Turbonomic provides you with accurate, almost up-to-the-minute, information about your application performance, VMs, databases, and storage performance at a much faster pace than humans could ever do. That's why I liked it so much.

Turbonomic does give you visibility into your environment’s performance as well as analytics, from the application layer all the way down the stack. But it does not give you as much as others do. More specialized applications, like New Relic, go much deeper, but with those products, those features are an additional cost. How much is enough is what it really comes down to. How much monitoring and in-depth analytics do you need? Some applications need much more and some don't. If a website is running fine, don't worry about it. In that case, you just need to know the up/down status and that's it. If you're running database queries and things are running slow, you might need deeper analytics. Turbonomic doesn't do that.

Whenever we have a specific application that we need to go into deeper, we will use New Relic or SolarWinds or the like; a dedicated application performance monitoring tool. Turbonomic does have the ability to target apps, but we're not quite there yet.

Educate yourself on the product, as well as on the process. The process is even more important than the product because people need to understand that you're going to be making some changes to the environment. If they're resistant to that, then you're going to have challenges getting Turbonomic to be useful.

You not only need executive buy-in and senior leadership buy-in, you also need your engineers' buy-in. If your executives don't buy into it, your engineers certainly aren't going to. And even if your executives have bought into it, you still have to get the engineers on board because there are all kinds of ways not to do work.

And you have to understand your own company's processes around how to make changes to an environment. What is your change control process? Can you make changes in dev, test, and QA without a change ticket? How do you do production? Do you, in fact, do production?

I would recommend doing something like a workshop where you look at all the applications you're going to point Turbonomic at. Get each team together and explain to them how it's going to work and how it benefits them, as opposed to: "We bought a new product. You're going to use it. Deal with it." People like to know how it impacts their lives and why they're potentially doing more work. In the long run, it actually becomes less work. It's just hard to get past that point. In the movie "Cast Away" it was really hard for Tom Hanks to get past those waves. But once he got past them, he was fine. It's something like that, but not as dramatic; it's not that you're trying to save your life. But you have to explain to people why there's going to be some upfront work: to save them a lot of work on the back end.

In terms of the solution's visibility and analytics helping to bridge the data gap between disparate IT teams, we're working on that. Implementing Turbonomic is a journey. It's not "install it, and then it does what it does." You have to learn it and integrate it into your environment and your workflows. It does shed light on infrastructure and application teams having to work together, and that's a good thing. Application teams generally don't like infrastructure teams because they don't give them enough infrastructure. Infrastructure teams think the application teams complain too much. Turbonomic says, "Here is what you guys are doing. And here is how to get it done right. Work together," and everybody will be happy. That's more of a "people challenge" and less of a technology challenge.

But the visibility and analytics have not yet reduced our mean time to resolution. The solution hasn't had any impact on our application response time and it's not supposed to. Turbonics is supposed to change your resources based on your schedule, and you shouldn't notice it doing anything, except for the downtime that an application sometimes requires. It should be seamless.

Similarly, when it comes to helping our engineers focus on innovation and modernization, it's a work in progress. That's hard to quantify. It's our role, as architects, to help people do their jobs better and have more time to do innovation versus fixing. We are definitely spending less time worrying about application performance, because Turbonomic takes care of that. But in terms of innovation, I have no way to quantify that. We have people learning it and using it, but are we innovating better? I hope so.

We did some digging into Kubernetes and the solution does show you some good insights there, and it may have come a little farther in that regard since the last time I was hands-on with it. It gave us good insight into what our Kubernetes clusters were doing. Since then, we have moved on to doing more IaaS-based stuff.

Overall, it's the best product for APM that I've seen.