IBM Turbonomic Reviews

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.

I belong to the on-premises team. We're a telecom company with a private and public cloud, but we don't use Turbonomic for the cloud infrastructure. We use Turbonomic for capacity forecasting and analysis. We do not use the solution as much on the application layer. We scan only the infrastructure. Turbonomic isn't providing any useful reports on the application layer. We did some application groupings, but it didn't help us because we didn't receive any application information. 

There are more than 50 Turbonomic users at the company, including admins and developers. There is a 10-person admin team, and the rest are end-users with limited access to see the reports on their machines.

Turbonomic provides recommendations about ideal resource levels. It helps us identify where we lack capacity and require more resources. These forecasts save time because we can avoid a capacity crisis. It tells us where to place the machines, so resources are automatically balanced. Those recommendations are there from the tool. That has helped.

It is a standard tool that helps to analyze capacity metrics. Without Turbonomic, we would struggle to manage capacity planning. It is essential to have a tool like Turbonomic because we rely on it for VMware capacity planning.

Turbonomic helped to reduce performance degradation by forecasting utilization and notifying us when we need to increase hardware resources before it reaches a critical threshold. Our SLAs require us to maintain 24/7 availability. 

The most valuable features are the cluster utilization reports and the resource capacity planning. We can simulate how much capacity we can add to the current resources. The individual DM reports and VM-facing recommendations report are also helpful. 

It can tell us where performance is lagging on the hardware layer. It's not on the application layer. Turbonomic can tell us where our memory and disks are to the point where performance will suffer. 

Turbonomic will identify causes and suggest actions in one unique report. For example, if a memory center is underutilized, it might suggest increasing utilization from 16 to 20 percent.

The automation area could be improved, and the generic reports are poor. We want more details in the analysis report from the application layer. The reports from the infrastructure layer are satisfactory, but Turbonomic won't provide much information if we dig down further than the application layer. 

I would like them to add some apps for physical device load resourcing and physical-to-virtual calculation. It gives excellent recommendations for the virtual layer but doesn't have the capabilities for physical-to-virtual analysis.

Automated deployment is something else they could add. Some built-in automation features are helpful, but we aren't effectively using a few. We want a few more automated features, like autoscaling and automatic performance optimization testing would be useful. 

I have been using Turbonomic for nine years.

Turbonomic is 100% stable. I've never seen any downtime. 

Turbonomic is scalable. 

I rate IBM support a nine out of ten. They've been there when we needed support. We haven't had to escalate any tickets lately, but they provided decent support during the initial deployment.

Positive

Before Turbonomic, I used a solution called VMware ONE Service. I started to use Turbonomic when I switched roles. I don't know why the company adopted Turbonomic, but it was in use when I joined. 

The setup is straightforward. It involves bringing down all the services from vCenter. There's nothing complicated about it. Deployment takes half a day once you have all the prerequisites, like the IP hosts, record ports, firewall configurations, etc. The virtual operations team handles deployment and maintenance. It's about ten people. 

I rate IBM Turbonomic six out of ten. I would recommend it for capacity planning. Decision makers want to predict workloads and plan. We get excellent reports and recommendations for machine optimization and sizing. I wouldn't recommend it for monitoring. 

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.

We use it to optimize costs and resource efficiency across our environments and  present infrastructure change requests to the business.

We had a lack of knowledge about things at our product level, and Turbonomic helped us resolve that. From an integration perspective, it also helped us find connectivity between our gateway tool products.

When it comes to optimizing costs and resource efficiency, before Turbonomic, we would add big, expensive storage and scale up across the tenant. Now, we are able to allocate the resources we need. We can also justify to the business, based on usage, why we are going with those resources. We have all kinds of proof to explain to the business how we are scaling down.

The visibility and analytics into our environment’s performance, from the APM down to the infrastructure, help us illustrate and clarify for the business the types of infrastructure changes we are suggesting. And they give us approval. Although we can collect the same information from Azure, Turbonomic is very user-friendly, and we can also automate notifications.

Regarding policy implementation, that can be implemented by a skilled engineer in five to 10 minutes. But if that same task is assigned to a new engineer who is not familiar with Turbonic, he would have to reference the previous document and the previous policy. Implementing the same thing would take that new engineer 15 to 20 minutes.

I like the analytics that help us optimize compatibility. Whereas Azure Advisor tells us what we have to do, Turbonomic has automation that actually does those things. That means we don't have to be present to get them done and it simplifies our IT engineers' jobs.

The platform is continually updated with new features. If they would educate their customers to understand the latest updates, that would help customers be more satisfied with the updates and push them into their environments.

Also, there are a lot of features that are not available in Turbonomic. For example, PaaS component optimization and automation are still in the development phase. If they could provide those enhancements, that would be really great. For example, we are spending a lot of time on Azure Databricks, exploring it and trying to do cost optimization as well as setting up the policies. If Turbonomic could help us understand how much the Databricks CPU is using per instance or workspace, that would help us optimize it, scale it according to our business requirements, and decrease costs.

We have been using IBM Turbonomic in the cloud for the past year.

The stability depends on how you set up the policies and logic. In general, Turbonomic is stable.

It's scalable because we are hosted in the cloud. We can expand it vertically.

I'm an open-source guy, so I used a lot of open-source code to do the cost optimization through scripting. One reason Turbonomic was our first choice was that our on-prem team was already using it.

We have it set up in the cloud and on-premises. I wasn't involved in the initial deployment as it was already on-prem. We just expanded it to the cloud. That operation was very easy. We just opened the access to the cloud. That was it.

We have a test environment, so we apply things on the test environment and make sure our policies are having the proper effects and are working perfectly. We can work on an environment-by-environment basis.

There is not much maintenance involved, other than applying patches.

There are a lot of options available, but compared to other products, Turbonomic has many features. Other products are at a more beginning stage.

When you do your initial assessment, you have to understand your business needs and then choose the product.

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.

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.

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.