IBM Turbonomic Reviews

We use Turbonomic to gather information that we can archive and review when there are performance issues or other problems. We look at the statistical data and see what was going on at that particular time across the cluster and if there was an issue. I generally look at the underlying resources, IOP utilization, CPU, CPU-ready ballooning, and anything that might cause performance issues.
Turbonomic is better than using the native vCenter to look at that and we didn't have vROps or anything.

Turbonomic helps us right-size virtual machines to utilize the available infrastructure components available and suggest where resources should exist. We also use the predictive tool to forecast what will happen when we add additional compute-demanding virtual machines or something to the environment. It shows us how that would impact existing resources. All of that frees up time that would otherwise be spent on manual calculation.

The solution's analytics are less important today because of changes in our environment. When we started using it, it was essential because we had more performance issues with the technology we had at the time. Turbonomic helps us interpret data alerts and speed sheets, which also isn't as important as it used to be. The solution helped to reduce performance degradation in the past, but it's less of an issue these days because we have optimized our environmental design.

Turbonomic reduced our mean time-to-resolution by about 50-60 percent when I used it for that. I can't say that it has improved our information sharing because our IT team is super small. We've got three people that are on the infrastructure. However, I have some experience with much larger environments, and the information that's in Turbonomic is easier to consume for some IT teams that maybe aren't as familiar with the virtualization environment.

It improved our application response time when we used it a lot more for performance analytics. We could see what consumed more IOPS and put it on the appropriate lens, where memory was not assigned properly. We could increase memory utilization or CPU. 

We can identify what is over-provisioned or if there are too many IOPS going through a particular data endpoint. CPU processor utilization, memory ballooning, etc., impact performance. 

Turbonomic provides many recommendations for right-sizing VMs for the tasks they're doing. That saves us significant time because we don't need to look at all that information and use calculators to figure it out. Turbonomic can tell you. Turbonomic reduced the time spent managing the performance of existing assets, freeing up time to do extra development work. 

I saw improvements in application response times when I used it regularly to look at performance gains. We achieved an improvement of around 20 percent in heavy application performance by right-sizing VMs and ensuring resources were appropriately assigned. 

The sizing information is the most useful aspect of Turbonomic. It helps us know which machines are over-provisioned or under-provisioned. I also like the historical information from the environment about performance metrics, utilization, and the like. It's nice to go back and look at what was happening in it at any particular time.

I do not like Turbonomic's new licensing model. The previous model was pretty straightforward, whereas the new model incorporates what most of the vendors are doing now with cores and utilization. Our pricing under the new model will go up quite a bit. Before, it was pretty straightforward, easy to understand, and reasonable.

I have used Turbonomic for nearly nine years.

Turbonomic is highly stable. We haven't ever had any issues with the product.

Our environment is smaller, and we don't add many resources to what we have. However, it seems to scale pretty well based on what I know about the product.

I rate Turbonomic support a ten out of ten. 

Positive

Setting up Turbonomic was pretty straightforward. You deploy the OVA, answer some questions, and point it at your environment. It's one of the easier infrastructure products to implement. I deployed Turbonomic in one afternoon, so it was a couple of hours max. After deployment, we had to install regular updates, but it was easy to do. Two people are involved in maintaining the product. 

In the past, we've seen a return, but it's currently hard to justify the recurring cost.

We evaluated vROps before selecting Turbonomic. Setting up vROps was much more complex. It required more overhead and maintenance. Getting the information we needed was more complicated. 

I rate Turbonomic an eight out of ten overall. I recommend evaluating it. Turbonomic might be easier than the product you currently use. You might be able to use the DRS mechanism in Turbonomic to get recommendations, and auto-sizing could make your life a lot easier.

We use the Reserved Instances and the recommendations of sizing of our family types in Azure. We use it for cost optimization for our workloads there.

We started with the on-prem solution, but then we went with the SaaS model. Now, Turbonomic handles the installation and the support of the appliances.

The volumes feature lets us know which volumes or disks are not attached or that are not being used anymore and that we can go ahead and delete them. It tells us how much money we'll be saving if we delete them. It's the same thing with Reserved Instances. It has that ability, that visibility, with those recommendations. 

There is also the family type that tells you which family the VM is going to and how much you're going to be saving. Disk tiering is one of the latest features. If you go from premium to standard, it shows you just how much you're going to be saving. It makes those decisions based on metrics.

When it comes to cloud costs, to VMs, the solution is saving us about $30,000 a month. It has also definitely reduced our IT-related expenditures by about $40,000 per month. And when it comes to the human resource time involved in monitoring and optimizing our estate, it saves us about 20 hours a week.

The recommendation of the family types is a huge help because it has saved us a lot of money. We use it primarily for that. Another thing that Turbonomic provides us with is a single platform that manages the full application stack and that's something I really like. 

One other useful feature in Turbonomic is the support for Kubernetes. That's one of the things that I have worked on with Kevin, our account rep, from Turbonomic. We're going to work on setting that up because our developers are pushing hard for Kubernetes for containers this year. Knowing that it's supporting that is awesome.

Something that Turbonomic started doing, just a couple of months ago with one of their latest releases, is the potential savings when it comes to disks. It is very promising. They make recommendations based on the type of disks. For example, if you're using a premium SSD, it makes recommendations, based on I/O metrics, to go to a standard SSD. Those types of recommendations are very valuable and that's another area where we see cost savings, which is awesome.

One ask that I'm waiting for, now that they have the ability to make recommendations for disks, for volumes, and disk tiering, is all about consumption. For example, we have a lot of VMs now, and these VMs use a lot of disks. Some of these servers have 8 TB disks, but they're only being used for 200 GBs. That's a lot of money that we're wasting. In Azure, it's not what you're using. You purchase the whole 8 TB disk and you pay for it. It doesn't matter how much you're using. So something that I've asked for from Turbonomic is recommendations based on disk utilization. In the example of the 8 TB disk where only 200 GBs are being used, based on the history, there should be a recommendation like, "You can safely use a 500 GB disk." That would create a lot of savings. And we would have more of a success rate than with the disk tiering, at least in our case.

Also, unfortunately, there is no support for cost optimization for networking.

I've been using Turbonomic for about three years.

It was definitely more stable on-prem. The reason I say that is because we've had several times where we have run into licensing issues. I don't know why that has been the case, although they have been few and far between. 

But when it has no issues, it runs just as if it were on-prem. The performance and the stability are not a problem.

It's a mature product. It very quickly detects when new VMs, new workloads, are added. You don't have to wait long. The tool picks things up very quickly in our environment.

Their technical support is excellent. I would rate them a nine out of 10. Whenever I send an email, they respond back. The only reason I don't give them a 10 is that I have been waiting for some time now on the Reserve Instances to work again. That's the only thing that has been a downer because we rely on them heavily. We are now having to use the Azure tool for that, and before the issue with Reserve Instances, we didn't have to. There's a lot of overlap between Azure on Turbonomic, but Turbonomic works better for us.

An aspect of the Turbonomic team that I have found, working with them over the years, is that whenever we've had an issue, they have always been willing to listen and to address it and to add the features we need. For example, when we started, Reserved Instances was really not up to par. But they listened to their customers and they started making changes. As time has gone on, the product has matured. They've incorporated a lot of the features that we've asked for into their appliance.

We tried it first on-prem, years ago. We used to host it. I installed it and updated it, working with the Turbonomic team. When it was hosted in our environment, I was responsible for everything.

The initial setup was straightforward. Because it was an appliance, the deployment took about an hour to stand it up. We use VMware on-prem so it was done with an OVA file, and it was pretty much a "next-next" process because the OVA is already packaged with how the tool should be deployed. There are certain custom inputs needed, like the name of the appliance, and how much storage. But everything else was already pre-packaged. The configuration definitely took a little bit longer.

The only downside was that Turbonomic came out with many releases. The latest releases had the latest features, but it required continuous upgrades. If we wanted to take advantage of one feature we continued to have to upgrade the appliance on-prem. That is why, when we found out that they do have a SaaS model, we went with that instead. We wanted Turbonomic to worry about things like the licensing, the updates, et cetera. We don't have to worry about that at all now, and that has been a huge relief. That has saved us a lot of time, for sure.

We didn't have to do any type of migration to their SaaS offering. They took care of everything in the back end.

We have five engineers who use the product, including a networking engineer, a storage engineer, and our DevOps team.

There are competitors out there. Since we're in Azure, which is the only cloud vendor that we use today, it has something called cost Azure Advisor, to help you with costs. I've tried it because it comes with it and we're paying for it, but Turbonomic is a better tool for us. We always seem to gravitate more toward it because everything is right there in that single pane of glass. It gives you recommendations based on Reserve Instances, even though right now, unfortunately, that's not working 100 percent. It does a lot of things, like the family types and the deleted volumes, and that type of automation for us, which is awesome. Azure Advisor does give you that as well, but it doesn't have everything. We have to drill down in it and it's not easy to navigate.

At one point the most valuable feature for us was Reserved Instances. The only problem with that today is that last year we changed from the EA licensing model to an MCA. At this moment, unfortunately, the Reserved Instances is not working. They're still working on it. It's in the roadmap, but that definitely was a big selling point for us. It worked well for us because we purchase a lot of Reserved Instances for our VMs.

Turbonomic makes a lot of recommendations to help prevent resource starvation. We can't implement all of them because it depends on our workloads. Not all the recommendations work for us because workloads on some of our VMs are very seasonal. There may be three times throughout the year, for about two weeks, where those VMs' usage is very high. They have to work at a high level. The solution can only go back a maximum of three months, and it won't work for us in some of those workloads because it doesn't have full visibility into the past year. But for some of our other workloads, those recommendations work.

Optimization of application performance is an ongoing process for us, especially as we move VMs from on-prem to Azure, or even build new VMs in Azure. More apps are being created and more services are being created, and we're taking advantage of that within Azure. However, we don't use Turbonomic's automation mode to continuously assure application performance by having the software manage resources in real-time. Our DevOps team is using Azure to control that automation.

For us, Turbonomic is an infrastructure service, VMs. As for applications, not yet, because now that we're introducing Kubernetes into our Azure environment, while it does have support for that, I don't know what it looks like yet. I have a meeting scheduled with them in order to configure that. It doesn't create it for you automatically in the back end. But it's more for our IaaS, infrastructure as a service. For storage, the closest thing now is the disk tiering with recommendations for going from and to different types of standard and premium SSD and HDD disks. Before, there wasn't that level of support. It was just VMs and family types, in our case.

We use manual execution for implementing the solution’s actions. We use manual because it depends on the business. We run a 24/7 shop. That's how it has always been on-prem, and that's how it is now in Azure, for our production VMs. We need to schedule maintenance windows because some of the recommendations from Turbonomic require a reboot. We need to schedule downtime with the application owners within the business.

The primary use case is to optimize our environment. We will take our OpenShift environment and use Turbonomic to monitor the size of the pods, then determine where to place the pods as well. We will make recommendations from that perspective. Turbonomic is an excellent product as far as we are concerned for managing the pod sizes and determining the best sizing for those pods. Right now, our development staff prefer to maximize the size of their pods and requests in terms of memory and CPU, and that causes us to potentially run out of resources.

We are managing the pods, their performance, and the utilization. It is more of a pod deployment model. Right now, we are monitoring the whole application as well as its allocation of resources, CPU, memory, etc. So, the application will be optimized and Turbonomic will help us optimize that sizing, because that is a problem right now.

We will be deploying this solution across all our OpenShift platforms to manage our existing environment.

The most valuable features have been the resizing, then the allocation of resources and where to run the pods. Those have been a huge success for us. It is a self-funding initiative in that regard.

Turbonomic provides specific actions that prevent resource starvation. Potentially running out of resources is a possibility. Now, we have an overallocation of resources. However, each time we use the resources, we incur additional costs from a licensing perspective. Turbonomic allows us to maximize our resources before we have to utilize additional resources. 

We like that Turbonomic shows application metrics and estimates the impact of taking a suggested action. It provides us a map of resource utilization as part of its recommendation. We evaluate and compare that to what we think would be appropriate from a human perspective to what Turbonomic is doing, then take the best action going forward. So far, we like exactly what we see from the product. It is very powerful.

After running this solution in production for a year, we may want a more granular approach to how we utilize the product because we are planning to use some of its metrics to feed into our financial system.

We have been currently using the product in our OpenShift environment for about six months. We did a PoC starting last Fall. We are now in the process of implementing it in production.

So far, the stability has been good. There is always room for improvement, but so far we have had no complaints from our team in regards to the product and how it operates in the OpenShift environment.

The technical support is outstanding. We have had a great relationship with the Turbonomic folks from the beginning. They have given us some excellent resources. Their support is five-stars.

We did not use another product. We are not replacing anything. We were familiar with the Turbonomic product in the context of our VMware environment. We thought it was the ideal product. Because of the way it calculates things, we also thought it was the right approach going forward. So, we went directly to choosing Turbonomic.

Our admins who deployed the technology said it was fairly straightforward. Because Turbonomic in OpenShift runs as a pod, it is fairly straightforward from a deployment model.

It is a relatively easy product to implement. If you're familiar with OpenShift, my OpenShift admins had no problems deploying it and working with the Turbonomic team. Their support has been great.

Phase one in deployment is to understand what actions would be from Turbonomic regarding resizing, then take actions based on those recommendations. After we are satisfied with what Turbonomic is doing, we will let Turbonomic take automated actions, which is phase two. We will be building a better trust relationship between our app and operations teams, when we allow Turbonomic to automatically deploy and take actions.

We like that Turbonomic provides a proactive approach to avoiding performance degradation. In phase one, we will do a manual evaluation of the recommendations. Then, in phase two, we plan to have Turbo fully automated and take actions based on what it thinks the best resource allocation model is.

We have two separate OpenShift clusters. We will be deploying one environment with more than 100 nodes and the other one with more than 50 nodes.

Working with Turbonomic consultants, the deployment was probably a couple of days. That was more to familiarize ourselves with the environment, what the commands were, etc. It was not a function of the complexity of the tool.

We don't have many people working on the product. We have about three people going through the testing and PoC environment right now and setting it up for deployment in our dev stage, and then finally in our production environment. There are about three individuals working on that. 

Because the product is self-managing in many ways and runs the way that it does, i.e., it runs as an application inside of OpenShift, I would probably dedicate half a resource from the OpenShift side to managing Turbonomic over the long-term.

While we are in the process of deploying now, we did a calculation and think that we definitely will be showing value and savings. Our expected ROI is 2:1. 

The product is fairly priced right now. Given its capabilities, it is excellently priced. We think that the product will become self-funding because we will be able to maximize our resources, which will help us from a capacity perspective. That should save us money in the long run.

I did some research on other products out there, but nothing met what I required from. Some of the products were cloud-only solutions, and that wasn't going to work for us because we are an all on-prem environment. However, we still think that the model that Turbonomic uses to make us determinations (its secret sauce) is actually the best thing out there.

You need to know OpenShift well to utilize the product. That is probably my biggest piece of advice. The more you know OpenShift, the better off you are when it comes to the product. The product can be self-driving in many ways.

We came in with a very specific set of goals, and Turbonomic has been able to meet those goals. We have had no real roadblocks so far

Our only context for productionizing is Turbonomic for containerized environments in OpenShift. We have taken a look at using Turbonomic for VM management, but that is not part of our initial work.

We are not running any cloud activities right now.

I would rate them as a nine out of 10. There is always room for improvement. For example, if they lower the cost, I could get a 3:1 ROI.

The primary reason we initially got it was to help us to right-size all of our VMs, to make sure that they were the appropriate size for the amount that they were being used. That was the biggest push to get this, and we implemented it. 

We have also discovered that Turbonomic can automatically suspend virtual machines that were on a schedule. For example, in the afternoons and the evenings when a VM wasn't going to be used, it could just be shut down, so that we wouldn't be charged eight to 10 hours of compute time, per machine, that wasn't going to be used at all during that time. That's been pretty useful. 

We're also using it to help us determine the reserved instances that we need. We haven't purchased the reserved instances yet but we're using Turbonomic's suggested reserved instance purchasing algorithms to assist us in finding the right balance for the number of RIs that we want to purchase.

It has provided us with a pretty good deal of savings on the front-end, helping us to correctly size VMs that were over-provisioned. We were paying a lot for VMs that really didn't need to be as big as they were. There has been a pretty drastic decrease in compute and cloud spends, due to either making the changes suggested by Turbonomic, or letting Turbonomic make the changes. That, coupled with using the suspend functionality to suspend machines that are not being used, pulled down our cloud spend a good bit.

The solution also provides a proactive approach to avoiding performance degradation. When you check in daily, it does evaluations on where your VMs and your infrastructure stand on that day. As a VM starts becoming more utilized, it will let you know to start planning on upgrading the machine because it's starting to show some extra usage that may grow beyond its capacity.

The right-sizing is the most valuable feature. It constantly lets you know if a machine is being over-utilized or under-utilized, so that you can make it the appropriate size for what you need it for.

It also brings up a list of machines and if something is under-provisioned and needs more compute power it will tell you, "This server needs more compute power, and we suggest you raise it up to this level." It will even automatically do it for you. In Azure, you don't have to actually go into the cloud provider to resize. You can just say, "Apply these resizes," and Turbonomic uses some back-end APIs to make the changes for you.

The way they evaluate reserved instances could use some polishing. The people that make decisions on what to buy are a bit confused by how it's laid out. I don't know if that's the fault of Turbonomic, or if that's just the complexity of reserved instances that Microsoft has created. It's not really that confusing for me, but for some people it's a little bit confusing. Trying to explain it to them is a bit tricky as well. We get to a point of impasse where we just accept that they don't really fully understand it, and that I can't fully explain it either. It would help if Turbonomic could simplify it or clarify it, and help non-technical people to understand what's going on and how the reserve instances are being calculated and what they apply to.

We've been using Turbonomic for about two-and-a-half years.

The stability is good. We've never had any issues with the server running and doing what it's supposed to do. It's never crashed. I've never had an issue bringing up the user interface. Nothing has ever caused any issues.

I think it would scale very well. Our size is in the medium range, but based on the way I see it working, I don't think it matters what size your cloud infrastructure would be. I think it would handle it very well.

In our company, Turbonomic is monitoring pretty much all of our machines in the Azure cloud. If they're in AWS, those are not managed. That's a separate side of the house and they don't want to have their stuff managed by Turbonomic. But we use it to manage everything from size to suspending, for all of our Azure-based machines.

As we move forward, we'll be using it more. We're going to look into using the suspension feature to suspend more VM. As we start getting comfortable with reserved instances, we'll probably use it to help us gauge how many reserved instances we need to buy.

I had to use their technical support when I set up the suspensions process. They were good. They did a good job. They got back with me fairly quickly and they were able to walk me through the process of how to configure it. They took the time to explain it very well. They even walked me through the process to make sure I understood how it works. As simple as a suspend of a VM might seem to be, initially, when you look at how it works in Turbonomic, it's not super-simple.

We didn't have a solution in place at all.

I was working for the company when they implemented Turbonomic but I wasn't part of the project that implemented it. I have since taken over one of the primary roles in utilizing it and we've deployed an updated version of it. I have a co-manager who uses it as well, on the same level as me. We back each other up on it.

That's something where there is room for improvement: upgrades. We have deployed the newer version, version 8 of Turbonomic. The problem is that there is no way to upgrade between major Turbonomic versions. You can upgrade minor versions without a problem, but when you go from version 6 to version 7, or version 7 to version 8, you basically have to deploy it new and let it start gathering data again. That is a problem because all of the data, all of the savings calculations that had been done on the old version, are gone. There's no way to keep track of your lifetime savings across versions.

Maybe there's a way that they can go and retrieve those specific bits of data, but so far I haven't seen how that happens. I would like to see them make major version upgrades work, first of all, because you can't currently upgrade to a major version. You have to deploy it to a new server. But at the very least, if we have to deploy it to a new server, give us a way to pull in that lifetime savings information, and track the information they've built up. We've had the same one for almost two years and it would be nice to keep all that tracking information for future reference.

I'm not involved in any of the billing, but my understanding is that is fairly expensive. 

It would be great if the price of the solution would scale with the amount of money that you are saving in the cloud. If the solution itself cost, say, $300,000 over the course of three years, it should be saving you $750,000 in cloud spend. They should make it worth it. At this point, I don't think there's any built-in tool to show you if the price that you're paying for Turbonomic is worth the cost savings that you're getting from it.

Or maybe the licensing and pricing could be done in tiers. If you had 100 virtual machines in the cloud, they would sell you licensing for 100 machines, and then 500, and then 1000. It would help if they did it in tiers so that you're not paying a massive amount of money for Turbonomic as a whole, and not saving as much as you were hoping.

I don't think Turbonomic provides you with a single platform that manages the full application stack. It manages a lot of the infrastructure stuff, Layer 1 through Layer 3 of the OSI model. It's mostly focused on infrastructure and making sure your infrastructure isn't over-provisioned. I wouldn't say it could all the way through the application.

Optimizing application performance on a continuous process is beyond the scope of a human to be able to do on a consistent basis. In other words, if you have 20 virtual machines, it's reasonable that a human could watch the utilization and determine size changes as needed, but if you're getting into hundreds of virtual machines, it becomes a task that's beyond the ability of a person to do by himself. It's a question of scale. As you get into hundreds of VMs, it becomes too tedious to keep track of and it becomes very time-consuming as well. Having said that, we don't use Turbonomic for that. We don't use it to manage any applications. We only use it to manage virtual machines.

We have only just started using containers. We haven't gotten into letting Turbonomic manage those containers. That's the only other thing that we would probably use it for at this point: managing the containerization. We use it right now for just cloud. We're pretty solid on on-prem because we've been doing a lot of migration of our on-prem stuff to the cloud. So we actually have a lot of compute resources available on-prem and we're not really worried about running into any resource issues.

Before Turbonomic, there was no person or group of people managing those aspects of our environment that it manages for us, but if there had been then, obviously, it would have reflected time savings at this point.

We're using it for placement automation. Turbonomic will look at the virtual machines that are on different hosts and it will say, "Hey, three are too many machines on this host. And this host doesn't have a lot of machines on it." It will place the virtual machines in a balanced way on different hosts and try to balance the hosts out as best it can.

We're also using it for CPU and RAM addition and automation: Do we need to add more memory or take away memory in the environment or look at a machine to see if it is being used to the best capacity?

We also use Turbonomic for planning. It takes a look at our environment and we can make plans, like if we want to put some of our environment into a cloud-based system, like Azure, it will tell us our costing.

We use it for about 4,000 machines.

Performance is something that should constantly be monitored and constantly be worked on. Turbonomic is doing that in the background, without me having to manually do it. It's a great help. It's monitoring something that I just physically couldn't do all by myself because the environment is too large. It's looking at the resources in any environment and taking snapshots of where things are. At the right time, it's adding or taking away resources, and that's helping make the environment more efficient.

Planning is another way it really helps because it gives us an idea of the cost to move. We wanted to move everything to the cloud. It helps us to figure out what it would look like and how Turbonomic could even help, if we want to do that. But more importantly, it gives us an idea of how much it would cost. We get a round figure of the cost involved.

It handles virtualization, cloud, on-premise, and storage for us. We're moving towards it handling applications. The solution understands the resource relationships at each of these layers and the risks to performance for each. It understands them and makes adjustments based on what's needed. It also lets us know, if we need to make some adjustments, where they are. It definitely does a good job with that. It's not only knowing where to put things, but it's doing it for us.

We use it for implementing scheduling of actions for change windows. We do that based on our service-level agreements. We have to have a certain level of uptime. We do things because of our change process and the way that we have systems going. It's best for us to do it at a scheduled time, when everybody knows that the system will be going through some type of change.

It helps us meet SLAs by making sure that the machine has the resources it needs to function properly. Also, when it does make changes, it does it during the SLA windows.

We also use Turbonomic to show application metrics and estimate the impact of taking a suggested action. We have it doing that based on things in clusters. We'll set something up in the cluster and then have it see if that cluster is being utilized to its fullest or if it's being over-utilized. It gives us visibility.

In addition, it helps optimize cloud operations and reduce our cloud costs. We're putting things on a certain type of storage and moving things to best fit our environment and give us the best cost for our environment. It will also give us an idea, before we put it there, of what the cost will be before we move it to Azure.

It saves me a lot of time because I don't have to do things to monitor the environment to see what machines need RAM or memory or need CPU taken away or added. It can do that for me. I would estimate it saves me 20 hours per month.

The automated memory balancing, where it looks at whether it's being used in the most efficient way and adds or takes away memory, is the best part. If it didn't do that, it would be something that I would have to do. We have too many machines for one person to do that. The automation helps me in that it is done in a really efficient way and a balanced way because of the policies. It really helps.

It provides specific actions that prevent resource starvation. If a machine needs more resources, it will use the automation policies to add resources so that machines get the resources that they need. And if they don't need them anymore, it will take them away.

It provides everything in one screen, one area. I'm able to see not only the planning, and not just the stuff that's on-premise, but things that are in the cloud. I can even narrow down to different applications. It does a lot in one pane. That was really important because we didn't have to go to three or four different products to find what Turbonomic had in one product. It really helps that Turbonomic is showing you efficiencies and the best way to keep things in the environment working properly, instead of me, or even me and co-worker, trying to do it. We just know that it's handling it better.

You can monitor an application and all the hosts and virtual machines that are connected to the application and it will give you an idea of how many resources it is using and what it is using. It also lets you know if it's over-utilized or under-utilized. It helps us to know if we're in an area where a machine might not perform the way we are expecting it to, because of the resources that it has or the resources it needs to have.

The planning and costing areas could be a little bit more detailed. When you have more than 2,000 machines, the reports don't work properly. They need to fix it so that the reports work when you use that many virtual machines.

I've been using Turbonomic for about one year.

It's very stable and works the way that it should.

It seems scalable, although I haven't scaled it up or taken anything away yet. We plan to use more storage. We're not using as much storage as we would like.

The support has been really good. The people that I've been working with have been really helpful. It's been working.

We used vRA from VMware. 

The initial setup of Turbonomic was straightforward. I worked with somebody from Turbonomic and we got an understanding of the environment and we worked together to get it installed and to get the placement working first. After the placement, we worked on changing and automating the addition of CPUs and RAM.

Something we need to do is make the solution aware of business-critical applications by understanding the underlying supply chain of resources. We need to make it aware of what's critical. We do that by setting up clusters and then setting certain policies on what is in each cluster. We separate critical things through clusters.

We implemented this solution in our company and we also implement it for our clients. We use it because it connects to monitoring tools over APIs and if you have a monitoring solution, it removes data intelligently. Our aim is to optimize our customers' systems. The solution also offers insight into the financial impact of an event. We are partners with IBM which purchased Turbonomic earlier this year, and we are resellers of Turbonomic. I'm the company CEO/CIO. 

The help desk analyzes metrics events and Turbonomic tells you exactly what is wrong and how to resolve it. The solution improves the function of the help desk and saves time by making repairs quickly.

Optimization is a valuable feature. For example, if I have eASICs running and several VMwares running, it shows me how to optimize the system and get the best out of it. It reduces the cost of the two systems. I like that the solution integrates almost automatically with other products.

The solution could be improved by having all interfaces together and I'd really like to see more interfaces. 

I've been using this solution for two months. 

We haven't had any stability issues, the solution is running well.

Scalability is fine. We're using the product extensively with deployment of almost 54,000 systems. We receive a lot of alerts from those systems and do a lot of monitoring, so it's used constantly. 

The technical support is now done by IBM.

The initial setup is very straightforward. You execute an application or a script, depending on whether you're running it in Linux or Windows, and then you deploy the containers. It's pretty much one command and then everything is automatic. It took us about 20 minutes. 

We already had a good return in the first couple of weeks. 

Licensing costs are middle of the road; you need to take into account what the solution delivers to the customer. If it's running automatic remediation then it saves a lot of money for an organization. 

This tool is excellent for a CFO, while other tools are geared more towards a CIO. This tool has a different market. We're currently working in Europe, carrying out implementations in both Italy and Portugal, and we're starting on a new deployment project in Dubai next week. 

I rate this solution nine out of 10. 

We looked to this product to help us to right-size our virtual machines and containers. Most people who request virtual machines tend to request more resources than they need, and most of the time it's hard to prove that they don't.

Our environment is entirely on-premises. We have virtual machines and Kubernetes running on some of those VMs. We also have plans to go to the cloud with some workloads in the future, so it's good to have this tool to help get the sizing just right when the time comes to pull the plug.

Turbonomic has been helping us use our resources more efficiently, the challenge we currently face internally is getting people to allow the servers to be turned off to make the changes recommended.

I also like the planning tool, which is helpful to make sure we have enough capacity for failing over one datacenter to the other in case of disaster, while still maximizing what we already have and helping to keep us from making host purchases we don't need yet. It's nice not having to make all those calculations on my own.

At the moment, I have only really had the opportunity to manage virtual machines with it.

We're starting to let it look at our Kubernetes workloads as well.

The automation and orchestration components are definitely the best part, as you can tell it what it can do and when, and just let it be. However, I haven't reached that point yet. That is the plan for the future though.

I really like the planning tool so I can make sure we have enough resources for upcoming projects that require servers, while still having enough resources for our disaster recovery failover needs.

The reporting feature could be made a little more seamless with the rest of the tool. It would be nice to be able to share and print the reports a little more easily.

Some features are only available via changes to the deployment YAML, and it would be better to have them in the UI.

The upgrade process is a bit tedious, but I know the team is working on that.

The ability to set the scope at the dashboard level and let it flow down to all the widgets you add would be amazing. Having to select the scope per widget is a bit tedious.

We have been using Turbonomic for just under a year.

It is pretty stable, we have not had issues with that.

Turbonomic seems scalable to me.

Technical support is where Turbonomic shines.

Lots of features are still in the works but the company really makes sure you have a lot of attention and support after the sale, which is sometimes hard to get. Most of the time, once a vendor has your money, you never hear from them again. However, I have had a great experience with post-sales support and a lot of hand-holding.

It is really well done on their part to make their customer happy.

We did not use a similar solution prior to this one.

The initial setup is pretty straightforward.

Deployed was completed in-house.

We have no ROI so far.

It's worth the time and money investment if you can afford it.

We evaluated VMware vROps before selecting Turbonomic.

It has a feature called "right-sizing". This makes sure that our virtual machines are sized properly so we don't have a lot of wasted resources, either too large or too small. This way, our machines function much better than they should.

We have a system where our developers automate machine builds, and that is constantly running out of resources. Turbonomic helps us with that, so I don't have to keep buying hardware. The developers always say, "They don't have enough. They don't have enough. They don't have enough," when they just configured it improperly. Therefore, Turbonomic helps us identify configuration issues on their side so it doesn't cost me money on the other end to buy resources that I don't really need.

The solution handles some of our applications and cloud as well as giving us some insight into the storage aspect. While we have other tools, Turbonomic does give me insight onto my storage utilization as well. That part helps us with the virtualization stack. Turbonomic understands the resource relationships at each of these layers and the risks to performance for each of them, which helps us be a little more at the front of the game, giving us a little more insights to be more ahead of what we need to do.

To an extent, the solution helps manage our business-critical applications by understanding the underlying supply chain of resources. Some of our business-critical apps are our large virtualization stacks, and that is what our developers develop on. These keep our development environment running optimally so they can continue to develop without having to wait for resources.

It does suggest actions. It let us know ahead of time: 

• If we need to increase hardware.
• If we're okay.
• If we need to decrease hardware.
• What to expect during peak season.

The most valuable feature is the right-sizing. For my maintenance weekend, I can schedule it to right-size a subset of VMs every month. That works perfectly for me. It goes out on its own and tells me which machines need to be changed, then it will perform that function. I don't have to do any manual intervention. It runs its own report in the background. 

Turbonomic tells me ahead of time to prevent resource starvation. Going into the console, it tells me whether actions can be taken, saying, "Do you want to do these now?" Then, I can push a button and Turbonomic will do them. Or, I could simulate a load by saying, "Well, I'm going to add this, what will happen?" It will then tell me what will happen, so I can know ahead of time.

It optimizes application performance as a continuous process that is beyond human scale.

The solution helps us with troubleshooting issues in certain virtual environments. It gives you good information so you can drill down to different levels. You can get information to help you troubleshoot issues that you are seeing, whether it be storage-related or virtual machine-related.

They could add a few more reports. They could also be a bit more granular. While they have reports, sometimes it is hard to figure out what you are looking for just by looking at the date.

They could update the look of the console.

There are some manual issues. When it comes to forecasting dollar amounts, you have to put in all these inputs. Some of the questions they ask are a little outside of the realm that any engineer should be putting in. If they could streamline that, the solution would be much better.

I have been using it for about two and a half years.

It is very stable. I don't think it has ever gone down unless it was something that we did. If you have a good system that is sized properly, then it works well.

The solution doesn't require any maintenance on our side.

We schedule for change windows because a lot of the times the recommended changes will require a restart. So, we have to do them when they don't affect anybody. For a lot of what we are using Turbonomic, it will suggest actions that require restarts, shutdowns, etc. Those need to be done off-hours.

It scales well. If you need to scale up, you just purchase more licenses.

We have 6,500 endpoints, which are 95 percent virtual and five percent cloud.

We have our operations team who use it sometimes. Operations gets the first call about performance improvements. If someone calls or opens a ticket with them, and says, "Oh, I need more resources," then they will log onto Turbonomic and see if a machine is underperforming for whatever reason. 

My team is managing the resources for right-sizing and forecasting,

We learned that all we have to do is engage the Turbonomic team, and they will help us get anything that we need done. They will make feature improvements if we request them. They are proactive when working with us.

Previously, we used something out-of-the-box or homegrown, in-house scripts.

Upgrading the solution is straightforward. It is pretty easy.

When we are notified of an upgrade, feature pack, or patch, it is like a two or three step function to get to the next level. It is very non-intrusive and easy. It takes 35 to 40 minutes to upgrade or apply a patch, and the solution doesn't go down.

We keep it because we see ROI. For example, I had an issue recently, which was big, and Turbo helped me solve that problem. If we did not have this solution, it would have been a much bigger issue.

It has reduced our IT-related CapEx and OpEx, because you have a lot of people who complain that they need this and that. Those would be capital expenditures if we're buying more machines, and we really don't need them. So, we can just right-size a VM or an environment, and that pays for the capital and operational expenditures. The automation helps a lot with this as well. This has reduced our expenditures around 10 percent because I haven't bought anything in a while. I haven't needed to buy anything, except for replacements.

It has saved time. Without this application, I would have to do everything myself. That would take at least eight hours a month, because the right-sizing takes a couple hours, then I would have to prepare it.

It is an endpoint type license, which is fine. It is not overly expensive.

We tested Turbonomic against VMware vROps. Both solutions will tell you, "Oh, you should do this or that. This is an issue. You can do this." However, at the time that we tested vROps, it was an older version. They weren't offering the ability to schedule right-sizing and automating it, where Turbonomic does. vROps would just alert you, so you would have to do the right-sizing yourself. Therefore, vROps would have been a bit more manual with more operational needs.

I target it at the cloud to get a baseline against other tools, e.g., which ones we are going to go with long-term. Turbonomic, in our cloud, points towards development environments, not production environments.

We are not really application-specific. However, it does work well with the monitoring and ensuring performance. I can identify a performance issue just by opening the dashboard, even if I am not necessarily looking for one.

I would give it an eight (out of 10). It is a really good product.