IBM Turbonomic Reviews

We use IBM Turbonomic to automate our cloud operations, including monitoring, consolidating dashboards, and reporting. This helps us get a consolidated view of all customer spending into a single dashboard, allowing us to identify opportunities to improve their current spending.

It can consolidate and amalgamate all the efforts. Before using it, we had multiple reports, tools, and sources of information that we needed to consolidate. With IBM Turbonomic, we can operate everything in a single console and view everything in a unified way. This allows us to address key performance issues and spending concerns that we identify, optimizing our operations to work better for our customers and us.

The overall price, the dashboards, and the FinOps capabilities are important features. The ability to manage all the budgets is also crucial.

The implementation could be enhanced.

I have been using IBM Turbonomic as an integrator for the past year.

50 users are using this solution. As an integrator, we're constantly looking for new logs and trying to make some of our customers for whom we do not provide cloud services part of that new ecosystem.

Initially, it can be tricky as you have to configure everything. The setup requires a significant level of effort. If there were a way to migrate or import some features or have some preconfigured settings, it would greatly help with the initial setup. It takes three to four months as per standard operation.

We have engineers who are certified in the tools. We have a couple of product managers, but the main source of disruption, or at least delays, is the integration and dependency on other areas. For example, if we want to integrate the CMDB with the monitoring tools we already have in place for each of our different customers, it requires time and dependencies not only on the availability of people but also on the ability to make changes to the environments.

The ROI is very good. Although it's expensive, you can fully implement the recommendations from the various tools and dashboards and easily recover the investment within the first year.

It offers different scenarios. It provides more capabilities than many other tools available. Typically, its price is set as a percentage of the consumption of some of our customers' services. The cost will vary depending on the specific scenario, but it is not cheap.

You can easily maintain it once you get into a stable mode with IBM Turbonomic. The operations team that adopted the tool is getting a lot of value from it, making it easier for them to manage and consolidate their work. It doesn't ramp up your AppDV or resource needs but helps improve and optimize them. We are using fewer people now.

It has a lot of capabilities. We haven't encountered any scalability issues. The way we have implemented it has helped us easily incorporate new customer sets.

There weren't many people with the skills to implement and manage IBM Turbonomic, so we had to develop the team's expertise. However, once we overcame that hurdle, managing it became easier.

Overall, I rate the solution a nine out of ten.

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

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

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

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

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

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

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

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

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

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

It is extremely stable. It works perfectly.

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

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

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

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

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

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

I would rate Turbonomic a nine out of ten.

We have four hosts and 250 VMs, so we automated Turbonomics to load balance across multiple hosts and achieve the most efficient usage of resources. The objective is to make the machines run as well as they can. The second use case is sizing recommendations, which we treat as gospel. The third use case is to help us replicate our VMs into Azure using a tool called Zerto. Turbonomic ensures we size the Azure instance correctly because we need to choose from a list of 10,000 sizes. We'll pay too much if we get the sizing wrong. 

Before implementing Turbonomic, we had difficulty reaching a consensus about VM placement and sizing. Everybody's opinion was wrong, including mine. The application developers, implementers, and infrastructure team could never decide the appropriate size of a virtual machine. I always made the machines small, and they always made them too big. We were both probably wrong. 

Turbonomic can determine the correct size of the box. The appropriate placement and sizing change every minute, so you'd only be correct for a minute if you ever got it right. Everyone is making incorrect assumptions about virtual machines based on physical workloads. They want to add more CPUs or 600 gigs of RAM. You're not going to get that in a virtualized or cloud environment. If you don't size it correctly, it will cause a performance issue or cost too much in the cloud. Turbonomic helps us avoid these mistakes. 

It helps us do everything we can to make a VM run optimally in an automated fashion. I have to understand why the VMs need to be redistributed. It just does it. If we have a problem after that, I know that placement and sizing aren't the problems. If I still have a problem, I need to use other tools to figure that out.

The solution helps us avoid performance degradation by placing the VM correctly and telling us if we're sized incorrectly. If someone complains about a performance issue and asks for more resources, I will consult Turbonomic about whether they need more CPU or RAM. If Turbonomic tells me that they do, I will give it to them. 

However, in the case of SQL Server, Turbonomic can't tell me if I have an index that's out of balance, so it doesn't fix the underlying problem. It just says that we need more resources to do this. When a situation like this happens, we go to the database admin and tell them we're using too much RAM, disk, or CPU. He will identify the problem and notify the other employees to stop doing whatever is causing it. 

We don't have applications that external users can access for a fee. Users in our company consume our applications to help them get business done. We don't have constant performance issues. By transitioning to virtualization, we got the benefits of fault tolerance and high availability because we used clustering. Now, instead of having available or unavailable applications, we have applications that perform better or worse. Turbonomic helped us avoid having applications that slow down because we virtualized. It's all shared resources, and we don't get trouble tickets about slowness unless there's an application problem.

Turbonomic provides some visibility into the application layer and underlying infrastructure. We also use ControlUp to drill down into the services running on each VM and what's hogging resources. Turbonomic manages Kubernetes and will size the Kubernetes container, but we don't use it to identify processes that consume the most resources.

Turbonomic handles workload placement and sizing exceptionally well. Granted, we don't make VMs with three CPUs and weird numbers of RAM. We try to come up with sizing that makes sense, but it tends to be close to what Turbonomic recommends. It does something that no other solution does. Microsoft and VMware will not suggest the correct size of a VM. 

It's essential to have an automated solution for handling placement and sizing. Things are changing so fast that a decision about the correct balance of your VMs across your cluster is incorrect by the time you make it. Turbonomics is constantly evaluating placement. 

Changes in the loads happen at various times of day, and some loads are unknown to us in different periods of the year, so we automate the decisions. It has a feature that enables you to start your VMs small and let them grow. Then, you can turn them off every month and try again. 

Turbonomic not only calculates the availability of resources for the task at hand but also forecasts what will happen to the system if I perform my recommendation. The problem with performance tools is that they sometimes provide recommendations that cause a problem, and they need to correct the problem they just fixed. Turbonomic avoids that by measuring what will happen if they do what they want. That's awesome.

Turbonomic doesn't do storage placement how I would prefer. We use multiple shared storage volumes on VMware, so I don't have one big disk. I have lots of disks that I can place VMs on, and that consumes IOPS from the disk subsystem. We were getting recommendations to provision a new volume. 

We use NetApp storage on the backend for the big one. I didn't want to re-provision a new volume. I wanted a placement. If it can place my workload in CPU and memory, why can't it tell me the placement of my disk volumes to spread my IOPS instead of telling me to make another volume? 

We have used Turbonomic for three or four years. I've gone through several upgrades.

Turbonomic is highly stable. We've never had issues. 

Turbonomic can handle any workload we throw at it, whether in the cloud or on-prem. I think that's why they went to Kubernetes. If your workload increases after your deployment, it will make recommendations on its own Kubernetes cluster that you need to size up or down. It doesn't automatically scale, but it understands that there are challenges to scale over time. In our case, we've scaled it down. It didn't need as many resources as it had.

I rate IBM support a ten out of ten. I've never had a problem. We can always reach support, and they know their product well. They can typically answer most questions or get back to me with a solution in a reasonable time. For example, when I asked them about the storage placement issue, they said, "We don't do things exactly the way you want. We understand it and will add that to our list of feature requests." If enough customers ask for it, they'll do it with the storage placement based on IOPS.

Positive

We use Turbonomic for infrastructure awareness, but we have other tools for application awareness like ControlUp. VMware has distributed resource scheduling, but we believe that Turbonomic is far superior to that.

Turbonomic has gone through several versions, and the latest is the most complicated because the first wasn't Kubernetes. It's a deployed OVF, so it's not that hard to do. Understanding the UI and configuration options isn't easy, but I'm an old guy who has been around since '87. I think all the new interfaces are unbelievably complicated and incomprehensible. It's like any product. Turbonomic is easy once you understand it. 

I didn't find the deployment to be complex or challenging. After you've deployed it, you need to configure it, do some placement, and tweak its recommendations. For example, I would never implement an odd number of CPUs, so I will specify that it only makes recommendations in units of two.

The deployment process is relatively lengthy because they've done all this Kubernetes stuff. You deploy it, and it spawns all these Kubernetes things. You need to wait until it finishes. It isn't instantaneous. 

Turbonomic requires no more maintenance than the average application. We keep it updated but don't immediately upgrade to the latest version. We stay one version behind. That's the sweet spot because we don't want to deal with problems in a brand-new release. We also review it annually. I can think of at least two occasions where they scheduled a technician to help us get it to the spot we agreed was the best. 

I haven't calculated the ROI. We do our internal ROI that looks at what it would cost not to implement Turbonomic. The cost would be poor performance based on infrastructure constraints. We believe it's worth what we pay for it. 

It has some features that help us control costs on the cloud. If we perform the recommendations on sizing, it shows you the difference in cost versus inaction. Turbonomic helps us size machines in the cloud and Kubernetes containers. We can run sizing reports that forecast whether a workload will be cost-effective if we move it to the cloud. 

When we create on-premise machines, the capital expenditure for on-prem equipment is fixed. It doesn't cost us more to be inefficient because we've already bought the hardware. It doesn't matter if I use it 70 percent or 90 percent. If you have an inefficient workload on the cloud, it may cost you a lot more than running it on-premises. You need to fix the application to avoid something stupid like storing data forever. Turbonomic will help us identify an inefficient application so we don't move it to the cloud and find out it costs a trillion dollars to run it.

When we first bought Turbonomic, we paid by ESXi host or something like that. We have several hosts with small workloads and a few with high workloads. We negotiated with Turbonomic, but the licensing model prevented us from covering a significant portion of our workload. Later, we got everything covered because they changed their pricing to a per VM model. 

I believe they modified it when IBM acquired Turbonomic or maybe right before. We could cover all the VMs that weren't included when it was charged by the number of hosts. We use virtualization for fault tolerance and high availability, but we might only have a handful of VMs. 

The licensing is now straightforward. You have a fee for a certain number of VMs plus maintenance. Everybody's switching to a subscription model these days. I'm an engineer, so I don't care how much anything costs. I only care that it works and doesn't keep me up at night. I'm not involved in purchasing. I don't think there are better, cheaper alternatives, but we review that annually.

I rate Turbonomic a ten out of ten. I always tell people about Turbonomic when talking to other infrastructure nerds. When we talk about infrastructure, the crucial part is not the implementation but measuring performance over time to see if your top is spinning out of control or falling over. 

We can typically get it right up front. The big question is: Does it continue to run, or is it slowly running out of steam? Turbonomic recommends, "Hey, if you're going to continue to build like this, you need to start provisioning the host." We don't use it for that, but if I get those recommendations, I need to check on the garbage collection, i.e., deleting unused resources that someone built and forgot about.

My biggest advice about Turbonomic is to use it to its fullest potential. To get the best benefit, you need to use it and measure the results. And if you don't use it, they're going to come up and go, "Well, what are you using this for?" "Oh, I don't know," and they won't renew it.

If you already have distributed resource scheduling or similar tools, Turbonomic does a better job and can do other functions that DRS can't. VMware won't recommend ways to size a VM in Azure so you can move it. Why would they want to do that? Turbonomic is middleware, so it doesn't have skin in the game regarding placement. It's making impartial recommendations irrespective of whose storage, hypervisor, or cloud platform you're using.

We have a hybrid cloud setup that includes some on-prem resources, and then we have AWS as our primary cloud provider. We have one or two resources on the Google Cloud Platform, but we don't target those with Turbonomic. Our company has a couple of different teams using Turbonomic. Our on-premise VMware virtualization and Windows group use Turbonomic to manage our on-prem resources. They use it to make sure that they're the correct size. 

I'm on the cloud engineering team, and I use it in a unique way. We use it for right-sizing VMs in AWS. We're using it to improve performance efficiency in our Kubernetes containers and make sure the requests are in line with what they should be. If an application has way more memory allocated than it needs, Turbonomic helps us decide to scale that back.

We have a platform that we use for our internal deployments. I use our API to get data and transform it for use in our platform. I've developed APIs that go in between our internal platform and Turbonomic. When our developers create and release code, these APIs allow them to take advantage of Turbonomic without using it directly. It's built into our platform so they can benefit from the performance improvements Turbonomic can recommend, but they don't need access to Turbonomic.

Cost savings is a significant benefit, especially with our AWS computing. It cuts down on human error. For example, sometimes someone will spin up some resources in AWS and then forget about it. We can go into Turbonomic's reporting and see that a virtual machine is idle, so you might want to scale it down. If it's not being used, you should delete it. Then you can save X amount of money. Turbonomic will automatically apply those things for you and tell you how much you're going to save. It's integrated with your AWS billing report and everything; it can give you real data. You click a button, and it'll apply all the functions for you, so you save a bunch of money. I would say that that's a huge part of it for us.

We have a couple of different use cases, and it was essential for us to meet all of them without the need to go to several different vendors. Turbonomic can manage on-premises and cloud-native resources all in the same place, providing direct cost benefits through our cloud providers and our on-prem hardware storage.

Turbonomic has also helped us improve our efficiency as an organization. We can better understand the actual cost of our applications and how to optimize, so we've become more efficient and cut down some of the extra expenses. It's also useful for capacity planning. We can understand how much resources we're using right now and how much we'll need when we bring on new clients for our software solution.

Turbonomic has helped us manage multiple facets of our business-critical functions. Our company provides a software platform for our clients. They log into a portal that's hosted either in the cloud or on-premises. Turbonomic can monitor those applications as well as the underlying storage and computing resources. It's monitoring the applications themselves, the production environments, development, and QA for future changes. We can understand how changes are going to impact our production.

It depends on the system that we're looking at. We have a change-management process for our business-critical things and our production resources. With that, we either schedule a change or manually execute the change during a planned maintenance window. Our change management board approved other functions, like development and QA-type resources that aren't in production that we're developing. We can automate those kinds of things all the time. I know that our storage team automates a ton of tasks, but I'm not exactly sure. I assume they wouldn't be automating production resources either.

We follow some pretty strict change management policies. Applying some of these resources will require restarting your process. We would do it either in a change management window that we schedule through Turbonomic or manually apply it. 

Turbonomic's application-driven prioritization helps us identify where risks are coming from while proactively preventing performance degradation. It's nice to be able to avoid problems before they happen. I don't have to wake up in the middle of the night and respond to some alert because one of our applications ran out of memory, and people couldn't use our product. It's helped me get some sleep. Our storage teams are super stoked about that, too, because they had all sorts of alarms going off all the time, and they set up a ton of automation with Turbonomic to handle that all for them. We've seen a significant reduction in open tickets for application issues.

Turbonomic can show us if we're not using some of our storage volumes efficiently in AWS. For example, if we've over-provisioned one of our virtual machines to have dedicated IOPs that it doesn't need, Turbonomic will detect that and tell us. You can save like a thousand bucks a month by switching the storage class. With a click of a button, it automatically makes the changes for you, and you can go in and save a ton of money on AWS with it. That's one of the primary ways I've used it. 

Kubernetes integration is excellent. Turbonomic helps us right-size deployments and replica sets. They've come a long way since I started here. I've been working with the team that uses Kubernetes or develops the Kubernetes integration, and it's been fantastic. Turbonomic helps prevent resource starvation too. Inside the console, there's a little graph that tells you what your application has been doing over the last week. You know that you need to take action right now before you run out of CPU or memory and your application starts to suffer. 

With Turbonomic, you have everything in one place. There aren't a bunch of different things to worry about or manage. It helps you manage full-stack applications as well. It's a challenge for many of our developers to understand what resources their application needs. We can automate that. Turbonomic processes all of the data, makes intelligent decisions, and automatically applies changes to the application. These are problems that are difficult for humans to solve because of the complexity of taking into account all these variables and determining how much memory to give an application. If you don't make the right decision, Turbonomic can discover that for you and fix it.

You can automate all of these functions. It tracks your application performance, and you can automate everything or have it wait for your input. It'll do it in real-time asynchronously in the background. Turbonomic can predict the impact of any given action, and that's one of the things I like about it. There's a little graph that pops up when you're about to do something. It shows you the history and predicts the future impact of what will happen when you click the button. For example, it can tell you that your utilization of the resource allocation will drop by this much, and you're going to be at about X percent utilization.

It's reasonably accurate. I haven't had a situation where it told me that everything would be okay, but it didn't work when I applied the change. So far, everything has been smooth sailing. Turbonomic can tell you how everything is currently performing, but we use other tools for that kind of monitoring. It can show you how your system is currently acting. If some things don't need action at the moment, it will tell you why. For example, it'll say you have this much memory allocated, and you're right on target, so you don't need to do anything. 

It's harder to use a monitoring tool to understand how your application performs over time. It depends on the monitoring tool, but often you have to set it up to ingest all this data and pick the right things to look at. Turbonomic does all that for you in the background. You can look at a suggestion, for example, if you need to up your memory allocation by a certain amount — and see all the data Turbonomic has gathered to make that decision. With a standard monitoring tool, you have to make that decision yourself. You're the one ingesting all the data. 

A monitoring tool is probably better if I want to see what my application is doing right this instant. As far as thresholds go, I think that's something I would probably use monitoring tools for. I would set it up to alert me when my resource allocation or memory usage exceeds 80 percent. I haven't used Turbonomic to do things like that. It's more forward-looking. When something is happening, like my application is running low on startup resources, I'll hop on a Turbonomic to see if there's a solution that I should apply. 

It's tough to say how they could improve. They've done a lot better with their Kubernetes integration. If you'd asked me a year and a half ago, I would say that I think their Kubernetes integration needs work. They started with more of a focus on on-prem VMware virtual machines. I think it was called VMTurbo at one point. Their main goal was to help you with these virtual machines. 

Now they've pivoted to also supporting containers, cloud-native tools, and cloud resources. At first, it was a little hard because they had this terminology that didn't translate to cloud-native applications for the way that Kubernetes deploy things versus a virtual machine. 

I was left wondering if this was a Kubernetes resource but now, it's come a long way. I think they've improved our UX as far as Kubernetes goes. I'm interested in seeing what they do in the future and how they progress with future Kubernetes integration. I would say that's something they've improved on a lot. 

I've been at my current company for a little over three years, and I believe we started looking into Turbonomic around that time. I would say I've been using it for two to three years.

I have never had Turbonomic go down or had a problem with it not being available when I need it, so I would say stability is great.

I've never had an issue where I would need to scale Turbonomic to handle more resources. Knowing what I know about how the solution is deployed, I would say it's scalable since it's built on Kubernetes. You can install the Kubernetes cluster and scale up instantly. Turbonomic has a micro-service architecture, so it appears to be scalable on the backend. I would say it's very scalable, but I haven't had any direct experience with scaling it myself. 

We're using it fairly extensively, but we don't have a ton of people working with it right now. Every relevant team uses it, including my team, cloud engineering, storage, and networking groups. In total, that's around 10 or 15 people using it. We are planning to increase usage. We're working on some new applications for Turbonomic, like integrating some of the data from Turbonomic into our platform as a service. 

I've also worked with some of their engineers on this. It's not necessarily things that I wouldn't figure out on my own, but they've helped to smooth the process along. Every once in a while, one of my contacts at Turbonomic lets us know a new feature is coming and ask us if we want to beta test it. We install it, update to the beta version, then go through and take a look. Some of those things would be cool, like a scaling solution with Istio, a Kubernetes load balancer service mesh tool.

I want to delve into scaling applications horizontally with Turbonomic based on response times and things like that. It would be nice to be able to automate more actions. Right now, I've integrated this into our platform, but in the future, we want to automate some of this more, especially for non-production resources. For example, if a developer decides to spin up a development application using way too many resources, we can automatically scale that down. That's the problem Turbonomic is trying to solve. It's tough to know how much you need. 

I rate Turbonomic support eight out of 10. Their support team has been good. We haven't had many problems, but when we do, they respond quickly. Whenever I've had to reach out for anything, they've been super-responsive, and they'll hop on Zoom call if we need them to troubleshoot something. 

Positive

The deployment process is a little tricky. It wasn't hard for me because I have pretty in-depth knowledge of Kubernetes, and their software runs on Kubernetes. To deploy it or upgrade it, you have to be able to follow steps and use the Kubernetes command line, or you'll need someone to come in and do it for you. We're deploying it to use with our OVA in our VMware environment on-premise, which is a little rough. It's not terrible, and I've had way worse software vendors, but I would say there's probably a little bit of room for improvement as far as upgrades go. We have to schedule a window and then make sure everything's working. With other on-prem services, you just run one command, and everything updates for you. Turbonomic upgrades are a little more involved.

When the guy on our side was going through the install process and setting all of this up, he had to get into the virtual machine environment and do a bunch of stuff, download some things, and then start running scripts. On top of that, he was trying to run these Kubernetes controls, and this other guy was helping install them. So it felt a little more clunky. I don't know how you would improve that unless it was a complete software solution service or a simple installer that you download and run.

The total deployment time depends on some different factors. We've deployed Turbonomic a couple of times. When they come out with a new version, we have to do a complete redeployment. I wasn't involved in the initial setup, so it's hard for me to say. But it took a couple of days to deploy the new version, plus a couple of hour-long sessions. It was around 15 hours total. I remember we tried to download a file, and it took two hours. I think that was because of the internet connection on our side. It's hard for me to quantify it. 

We've seen a great return on investment. Then again, I'm not sure how much we initially paid for it anyway, but we went through renegotiation. I don't have the numbers, but we bought some additional licenses, so we just expanded our use a little bit two or three weeks ago. I'd say that we got a good return on our investment, and we're excited about expanding our use in the future too. 

It has reduced our capital and operational expenditures. It's hard to estimate it, but the cloud savings have been significant. I can't give a percentage. However, there have been multiple times when I've applied something, and it has cut a considerable portion of our monthly spending on AWS — over 5 percent. Sometimes it's just a little, but all of those actions add up over time. If I apply a bunch of changes at once, it can add up. I can say we reduced 5 percent of our monthly spending just once, and that was pretty huge for us because we spent a ton on AWS resources. That was one time I can remember, but I'm sure it's been more than that, especially our other teams using it. We've also seen some savings in human resources costs, especially on the other team. They're not dealing with alarms going off all day anymore.

I rate Turbonomic 10 out of 10. For anyone thinking about implementing Turbonomic, I would suggest having someone familiar with Kubernetes — the more familiar, the better. You need someone who knows how to run a Kubernetes command to see what's happening with the state of the Turbonomic deployment if necessary. If you've got someone who knows how to use Kubernetes, include them in the deployment process.

We do vMotion through VMware. We let Turbonomic control our vMotion. We do server rightsizing and capacity management with it.

With Turbonomic, we were able to reduce our ESX cluster size and save money on our maintenance and license renewals. It saved us around $75,000 per year but it's a one-time reduction in VMware licensing. We don't renew the support. The ongoing savings is probably $50,000 to $75,000 a year, but there was a one-time of $200,000 plus.

It also saved human resource time and the cost involved in monitoring and optimizing our state by 25%.

Rightsizing is the most valuable feature because it helps with our capacity management and server density so that we are always optimized. 

Turbonomic provides specific actions that prevent resource starvation. It'll tell us if a server is overpowered or over-provisioned so that we can recover resources. And on the same note, it'll tell us if a server is under-provisioned and we need to add resources to it to help the performance.

It also provides us with a single platform that manages the full application stack. This was the secondary reason we went with Turbonomic. The primary reason was for the server optimization.

In my organization, optimizing application performance is a continuous process that is beyond the human scale. We're always looking to better optimize the environment.

We use Turbonomic's automation mode to continuously assure application performance by having the software manage resources in real-time for sizing-up. Sizing down is a manual process.

Turbonomic handles on-prem, virtualization, and storage. Turbonomic understands the resource relationships at each of these layers and the risks to performance for each. As far as we can tell, they are risk-averse. So they put controls in so that you don't cause outages. It makes the operations more secure.

We do a piece of automation in real-time, scheduling them for change windows, or manual execution for implementing Turbonomic actions. The vMotions are automation, the rightsize up is done automatically, and the rightsize down is during change windows.

We do the automation piece so that it is continuously rightsizing how many VMs are on a host for best performance, same with increasing resources on a VM to make sure application performance is where it should be. And then we do change control for the rightsize down because it requires a reboot.

The fact that Turbonomic shows application metrics and estimates the impact of taking a suggested action gives us a window into seeing what will happen if we do make the change. So it provides better visibility.

Turbonomic provides a proactive approach to avoiding performance degradation. That's what rightsizing does. It continuously optimizes so there are fewer application performance issues.

We have seen a 20% reduction in tickets opened for application issues.

Using Turbonomic does it all-in-one versus the approach of using monitoring and threshold to assure application performance. Sometimes your monitoring tool does not do the optimization as well.

The GUI and policy creation have room for improvement. There should be a better view of some of the numbers that are provided and easier to access. And policy creation should have it easier to identify groups.

I have been using Turbonomic for four years. 

We have not had any issues, it's pretty stable.

We've been able to add resources or things for it to look at without any issues so far. So there haven't been any scalability issues.

We are monitoring 3,000 workloads with Turbonomic.

There are two systems engineers who use it. In terms of maintenance, it only requires software updates. 

We may do some more integration with other applications like AppDynamics, but the platform itself, we've integrated it completely.

We've had great success with their support. They have good response times and willingness to engage.

The initial setup was straightforward. It's point and click. You install the VM and point it to your environment and it starts working.

The initial deployment took a couple of hours. 

We have seen ROI in cost reductions and savings. That directly applies to the cost we pay for Turbonomic licensing.

Our ROI is positive when it comes to the assurance of application performance in your company. It's a benefit for us.

Pricing is pretty straightforward. We haven't seen any major increases in it. It's a flexible model.

There aren't additional costs to the standard license. 

We also looked at VMware vRealize. The big difference between vRealize and Turbonomic is integration. It's easier to integrate Turbonomic than it is vRealize. There are more components to install.

It's a worthwhile investment, at least to get it, get some sort of trial installed to see because it'll give you recommendations as to what it can do and it'll allow you to determine if it will help your environment or not.

There were many things that could be optimized in our environment that we did not know about before.

I would rate Turbonomic an eight out of ten.

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

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

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

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

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

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

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

It has proven to be highly stable.

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

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

Overall, I would rate it eight out of ten.

We are in the healthcare industry and we use this solution for ensuring proper resource allocation for our virtual servers and our virtual desktops.

We use Turbonomic as a single platform to manage our full application stack, and having a single source of truth for application performance management is very important to us. The fewer places you have to go to make changes, the better. Having that available in a single pane of glass to make those changes makes it easier on our admins. Rather than having to go into multiple solutions to make changes, they do it all right there.

Turbonomic provides visibility and analytics in our environment from the application layer, all the way down the stack to underlying infrastructure resources. We don't use it as much on the application level as we do on the hardware and resources level.

This is a feature that's becoming more important to us. We're really starting to look at the analytics more nowadays and will in the future. It was not as important before but has become more so in the past year.

This visibility has definitely helped reduce our time to resolution. We have not quantified how much but it is due to having the visibility and the ability to monitor what's going on and make those changes in real-time. We just didn't have a baseline to compare and see how much it's improved.

With respect to any alerts that come up, it helps us to interpret those faster. This is something that is very important because it triages a lot of stuff that we don't have to then spend extra time doing, especially being a small team. It saves us at least a couple of hours per week.

Using this product helps us to reduce performance risk because it shows us where resources are needed but not yet allocated. Based on that, we're able to see whether those resources needed to be updated. In turn, that helps to limit application downtime or employees waiting for their jobs to get done.

It has definitely helped to reduce performance degradation. It helps us keep up with the changing environment and workloads that change over the course of days or weeks. Prior to this, it was all manual for us and we'd have to react. Now, we're able to be proactive.

This has also had a positive impactive on our applications' response time to SLAs. We're able to keep up and be proactive by fixing issues before the user even notices that there is a problem. This is important because it's great from a customer experience standpoint.

They never experience the problems they had in the past, where they would have to call us to say that their machine was running slowly, and then we'd have to figure out what was going on. Now, we know beforehand that they need additional resources, and many times, we're able to address that before they even realize it.

Generally speaking, using this solution has helped to eliminate resource constraints and it's helped us to understand what resources we need. In terms of that, we are able to modify our plans for the future concerning the acquisition of new hardware because we're able to satisfy the need with what we already have, rather than thinking we need to buy more. Turbonomic helps us to balance that out better, ensuring that we're not over-resourced in terms of hardware, and having resources sitting idle, which is very costly.

Turbonomic has helped our engineers focus on innovation because it has freed their time quite a bit. In the past, we had one person that would spend a lot of time trying to find where things were going wrong. This was precipitated by users saying that their machines were slow or not performing very well. Our staff would have to go in and figure out what was going on, then make the appropriate changes. Now, Turbonomic does that and our staff can focus on other tasks that need to be done.

The resource allocation features are the best for us. They have a lot of different features, but we had it at first in notification-only mode, or recommendation mode it may be called. In that mode, they would recommend what we should do, and then we would manually do it.

Once we realized that we could trust their recommendations, we set it into the automated mode, so it makes those changes on the fly for us. Especially during the pandemic, that really helped as we were scaling up our virtual desktops quite a bit. We almost tripled the number of desktops we had on there within the course of two and a half to three months.

In the automation engine, it is really quick to change things when it needs to scale up. However, scaling back is a little bit slower. Recovering resources when they're not needed is not as optimized as it could be.

I have been working with Turbonomic for approximately four years.

This is a very stable solution. Even when we have to perform upgrades, it's seamless. With other solutions, updates can sometimes be a problem, but with Turbonomic, it's been pretty easy.

Turbonomic is a scalable product.

We have about 280 servers and close to 550 virtual desktops being managed by Turbonomic. It is in a mode to increase resources as needed and then decrease them as the demand goes away.

At this point, we don't have any plans to increase usage. We have it covering all of the workloads that we need.

We only have two people that use it, and they are system analysts. They are in charge of deployment and maintenance.

The customer support has been very positive, although we've had very limited need for it. That said, whenever we've had to call in, they've been able to help us out very quickly.

One of the best parts about this solution is that there are so few issues that we simply don't need to use support very often.

Positive

We did not use a similar solution prior to this one. All of the work was done manually. We knew that our workload was increasing and the time spent on managing these types of workloads throughout our server stack was also increased. We implemented Turbonomic because we found it was a good way to free them up from a lot of that busy work.

Compared to what we were doing before, Turbonomic has given us full visibility. Prior to this, we had to look in multiple locations, and in some cases, we didn't have any visibility at all. 

The initial setup was very straightforward, and it was done within a couple of days.

Resources from Turbonomic assisted us with the deployment. They were very knowledgeable and were able to help our staff, who had never used the product before, understand it very quickly.

We have not measured ROI, although application performance has improved because we're not resource-constrained. We're not running into situations where our applications are failing due to a lack of resources, so it's helped us most with uptime and customer experience.

It has definitely helped in terms of CapEx because we've been able to avoid purchasing hardware that we originally thought we needed.

The pricing is in line with the other solutions that we have. It's not a bargain software, nor is it overly expensive.

I do not recall evaluating other solutions.

I believe that we use version 8.3, and we may be a couple of versions behind the latest.

Turbonomic has tools for optimizing and monitoring cloud-based environments, although, at this point, we use it mainly for our on-premises environment. We used it to help estimate what our cloud costs would be. Consequently, we realized that we were much better, at the time, not migrating to the cloud from a monetary standpoint.

Using the cost estimate to run our workloads in the cloud, we found 25% to 30% savings by staying on-premises versus going to the cloud. This is because our workloads are not optimized for the cloud. We'd have to retool a lot, which becomes very expensive.

The problem with moving is based on our application stack, rather than something that can be changed in Turbonomic. They saved us money in this regard because their estimates are very well thought out and very informative.

My advice for anybody who is looking into Turbonomic is that it's a great product. There are other options on the market but from what I've seen, this is one of the better ones. I'd suggest starting slowly when it comes to the recommendations. Make sure that you're verifying what their recommendations are and building that trust up before going into a more automated mode. Once it is automated, it can move pretty quickly and if you're not ready for it, it can cause some issues.

If somebody were looking into Turbonomic but already has a process automator and does monitoring, it would really come down to whether they are looking for better ease of use, or having an all-in-one platform if they currently use multiple tools. It's going to do a lot of the same tasks and they would have to do their own research to see what is better for them. I like that it gives that single pane of glass visibility, whereas they might have multiple vendors and multiple applications in their current use case.

In summary, it's a good product. There are things that they're working on and they keep adding new features, so we're happy to see that.

I would rate this solution a nine out of ten.

Our use case: Planning for sizing servers as we move them to the cloud. We use it as a substitute for VMware DRS. It does a much better job of leveling compute workload across an ESX cluster. We have a lot fewer issues with ready queue, etc. It is just a more sophisticated modeling tool for leveling VMs across an ESX infrastructure.

It is hosted on-prem, but we're looking at their SaaS offering for reporting. We do some reporting with Power BI on-premise, and it's deployed to servers that we have in Azure and on-prem.

The proactive monitoring of all our open enrollment applications has improved our organization. We have used it to size applications that we are moving to the cloud. Therefore, when we move them out there, we have them appropriately sized. We use it for reporting to current application owners, showing them where they are wasting money. There are easy things to find for an application, e.g., they decommissioned the server, but they never took care of the storage. Without a tool like this, that storage would just sit there forever, with us getting billed for it.

The solution handles applications, virtualization, cloud, on-prem compute, storage, and network in our environment, everything except containers because they are in an initial experimentation phase for us. The only production apps we have which use containers are a couple of vendor apps. Nothing we have developed, that's in use, is containerized yet. We are headed in that direction. We are just a little behind the curve.

Turbonomic understands the resource relationships at each of these layers (applications, virtualization, cloud, on-prem compute, storage, and network in our environment) and the risks to performance for each. It gives you a picture across the board of how those resources interact with each other and which ones are important. It's not looking at one aspect of performance, instead it is looking at 20 to 30 different things to give recommendations.

It provides a proactive approach to avoiding performance degradation. It's looking at the trends and when is the server going to run out of capacity. Our monitoring tools tell us when CPU or memory has been at 90 percent for 10 minutes. However, at that point, depending on the situation, we may be out of time. This points out, "Hey, in three weeks, you're not going to be looking good here. You need to add this stuff in advance."

We are notifying people in advance that they will have a problem as opposed to them opening tickets for a problem.

We have response-time SLAs for our applications. They are all different. It just depends on the application. Turbonomic has affected our ability to meet those SLAs in the ability to catch any performance problems before they start to occur. We are getting proactive notifications. If we have a sizing problem and there's growth happening over a trended period of time that shows that we're going to run out of capacity, rather than let the application team open a ticket, we're saying, "Hey, we're seeing latency in the application. Let's get 30 people on a bridge to research the latency." Well, the bridge never happens and the 30 people never get on it, this is because we proactively added capacity before it ever got to that point.

Turbonomic has saved human resource time and cost involved in monitoring and optimizing our estate. For our bridges, when we have a problem, we are willing to pay a little bit extra for infrastructure. We're willing to pull a lot more people than we're probably going to need onto our bridge to research the problem, rather than maybe getting the obvious team on, then having them call two more, and then the problem gets stretched out. We tend to ring the dinner bell and everybody comes running, then people go away as they prove that it's not their issue. So, you could easily end up with 30 to 40 people on every bridge for a brief period of time. Those man-hours rack up fast. Anything we can do to avoid that type of troubleshooting saves us a lot of money. Even more importantly, it keeps us productive on other projects we're working on, rather than at the end of the month going, "We're behind on these three projects. How could that have happened?" Well, "Remember there was that major problem with application ABC, and 50 people sat on a bridge for three days for 20 hours a day trying to resolve it."

In some cases you completely avoid the situation. A lot of our apps are really complex. A simple resource add in advance to a server might save us from having a ripple effect later. If we have a major application, as an example, and to get data for that application, it calls an API in another application, then pulls data from it. Well, the data it asks for: 80 percent of it's in that app, but 20 percent of it's in the next app. There is another API from that call to get that data to add it to the data from application B to send it back to application A. If you have sometimes a minor performance problem in application C that causes an outage in application A, which can be a nightmare to try and diagnose those types of problems, especially if those relationships aren't documented well. It is very difficult to quantify the savings, but If we can avoid problems like that, then the savings are big.

We are using monitoring and thresholds to assure application performance. It is great, but at the point where our monitoring tools are alerting, then we already have a problem in a lot of cases, though not always. The way we have things set up, we get warnings when resource utilization reaches 80 percent, because we try to keep it at 70 percent. We get alerts, which is kind of like, "Oh no," but we can do something about it when the applications are at 90 percent. The problem is there are so many alerts and it's such a huge environment. Because there is too much work going on, they get ignored. So, they can work into the 90s, and you end up a lot more often in a critical state. That's why the proactive monitoring of all our open enrollment stuff is really beneficial to us.

You have different groups who probably use almost everything. We use it for sizing of servers, and if somebody feels like their server needs additional resources, we validate it with the solution. We have a key part of the year called "open enrollment", where we really can't afford anything to be down or have any problems. We monitor it on a daily basis, and contact server owners if Turbonomic adds a forward-looking recommendation that they are running low on space. So, it keeps us safe. It is easy to monitor the virtual infrastructure and make sure there is capacity. However, with the individual VMs, in production alone, there are 12,000 of them. How do you keep up with those on an individual basis? So, we use Turbonomic to point out the individual VMs that are a little low.

Turbonomic provides specific actions that prevent resource starvation. They make memory recommendations and are very specific about recommendations. It looks at the individual servers, then it puts them in a cluster. At the end of the day, it comes back, and goes, "I can't fit these on here. There's not enough I/O capacity." Or, "There's just not enough memory, so you need to add two hosts."

For implementing the solution’s actions, we use scheduling for change windows and manual execution. The issue for us with the automation is we are considering starting to do the hot adds, but there are some problems with Windows Server 2019 and hot adds. It is a little buggy. So, if we turn that on with a cluster that has a lot of Windows 2019 Servers, then we would see a blue screen along with a lot of applications as well. Depending on what you are adding, cores or memory, it doesn't necessarily even take advantage of that at that moment. A reboot may be required, and we can't do that until later. So, that decreases the benefit of the real-time. For us, there is a lot of risk with real-time.

You can't add resources to a server in the cloud. If you have an Azure VM, you can't go add two cores to it because it's not going to have enough processing power. You would have to actually rebuild that server on top of a new server image which is larger. They got certain sizes available, so instead of an M3, we can pick an M4, then I need to reboot the server and have it come back up on that new image. As an industry, we need to come up with a way to handle that without an outage. Part of that is just having cloud applications built properly, but we don't. That's a problem, but I don't know if there is a solution for it. That would be the ultimate thing that would help us the most: If we could automatically resize servers in the cloud with no downtime.

The big thing is the integration with ServiceNow, so it's providing recommendations to configuration owners. So, if somebody owns a server, and it's doing a recommendation, I really don't want to see that recommendation. I want it to give that recommendation to the server owner, then have him either accept or decline that change control. Then, that change control takes place during the next maintenance window.

Three years.

Because of the size of our company, earlier versions were slow. However, they rearchitected the product about a year or 18 months ago and containerized parts of it, so we could expand and contract. Performance has been good since then.

I've a couple of guys who support it. We upgrade six or seven times a year. We are upgrading fairly often, so we are very close to current.

We have one guy spending maybe three weeks of the year doing upgrades. The upgrades are easy and fairly frequent, but there are almost always enhancements with these releases.

There are probably 50 people using it now. There are a handful who use it almost every day for sizing and infrastructure. We have a capacity management team who uses it all day long, every day. There are also multiple cloud teams and application teams who have been given access, so they can use it to appropriately size and work on their own applications. We are in the process of automating that to get that data out to everybody. There are a lot of other key teams who have found out what we were doing, and are like, "Can we have access to it now? So, we don't have to wait?" We are like, "Sure."

The scalability is good. I don't see any issues at all.

We were initially on the high-end of their customers. We ran two instances of it for a while, just because there was a limit of like 10,000 devices per system, and we were significantly past that.

Just from a server perspective, we are running about 26,000 servers right now, where 97 to 98 percent are virtualized. One person can't get a handle on that. Even figuring out what direction to look, you need to have tools to help you.

The technical support is good. We actually rarely call them. We have done quite a bit of work with them. Because of the number of purchases, they provided a TAM to work with us. So, we have kept that TAM around on an ongoing basis. We pretty much just call them, and they handle any support issues. From a support perspective, it has been one of the better experiences.

If it stops doing its thing and moving VMs around, it will be many days before it is going to have any impact on the environment, because everything is configured so well. From that perspective, it is an easier application to score than if you have a VMware host crash and trap a bunch of VMs on it.

We started using Turbonomic as a replacement for VMware DRS, which handled the VM placement.

We knew we were having some performance issues and ready queue problems that we felt could be improved. We worked with VMware for a while to tweak settings without a lot of success. So, we saw what Turbonomic said that they could do. We tried it, and it could do those things, so we bought it.

From a compute standpoint, Turbonomic provides us with a single platform that manages the full application stack. When we originally started, we were primarily looking for something that would make better use of our existing infrastructure. Because it does a much better job of putting VMs together on hosts, we were able to save money immediately just by implementing it. At the time, we were non-cloud. There was a period of time where we just couldn't put anything into the cloud for security reasons. We have moved past that now and are moving to the cloud. This solution has a lot more use cases for that, e.g., sizing workloads for the cloud and monitoring workloads in the cloud.

It's incredibly easy to set up. It took a couple of days. You spend more time building servers and getting ready for it.

It gathers its own data from vCenter. It doesn't touch the actual servers at all. Same thing with the different cloud vendors. It looks at your account information. It doesn't actually have to touch the servers themselves.

As far as the product goes, it's not an agent based. It can gather information, and start making recommendations within two or three days, then better recommendations within a week. After that, you're good. It doesn't get much easier.

We did the implementation ourselves. It took one guy to deploy it.

My group built a couple of the VMs that we needed and installed it. It took a couple of days. As far as gathering information, you don't have to put agents on any servers or anything like that. You give a user an ID for vCenter, and we have multiple vCenters.

The open enrollment applications are all mission-critical apps. If they go down, then the clock starts ticking on its way to seven-digit sales losses. It helps us avert situations like this multiple times a week. We are constantly using it to watch and notify application owners. If we don't use Turbonomic for this, then what would typically happen is the node recommendations that they would get from Dynatrace would start showing them that there is latency in their app. If they started digging into Dynatrace, then it would come up, going, "I'm running at 90 percent CPU all the time. I better get some more CPU." Well, Turbonomic tells us two weeks before that happens, that, "We need to be adding CPUs." So, it has a proactive nature. There are a lot of other tools in play that are monitoring what is happening. For our managers, Turbonomic helps us figure out what is going to happen.

We use Turbonomic to help optimize cloud operations, and that has reduced our cloud costs. We have a lot of applications that we run which are very cyclical. Fourth quarter of the year, they get the crap beat out of them. The other three quarters of the year, they are not used a whole lot. Without Turbonomic, would it be appropriate for the application to get resized nine months out of the year. Probably not.

It has helped save cloud costs by seven figures.

The tool itself is not free, but it's easily a positive ROI. It's hard to measure the benefit of just doing the DRS and optimizing our virtual infrastructure. I just can't stress enough how much it does such a better job of stacking VMs onto a set of ESX infrastructure. If you're using Turbonomic and looking at a cluster, you will see pretty much even utilization across a set of hosts. If you let VMware manage it, you will see one host at 95 percent, then another at five percent. Everything is running fine, and that's all they care about. However, if something starts going wrong on the host that is running at 95 percent, then you may see some degradation, just like rats leave the sinking ship trying to get out through that 5 percent host. Because it does a better job of balancing things, it utilizes infrastructure better, so you have fewer servers to host the same amount of VMs.

We have probably reduced our server purchase by a million dollars, just having Turbonomic manage the VDI infrastructure. Before they were static, so they just put an X number of VMs on each host, e.g., there are 70 VMs on that one, then it goes onto the next one. If we saw hotspots, then we would manually try and move a VM or two around.

We are using Turbonomic now to manage that and the supercluster feature that lets us migrate across clusters, which is really key for the VDIs, because we had infrastructure that wasn't well utilized 24 hours a day. So, we were buying lots of extras. The reason for that was we have developers in India, tons of people offshore, and people in the Philippines. As those people come and go, the utilization of different clusters shifts radically. So, if you're trying to have enough infrastructure to manage each cluster individually, then it takes a lot more than if you're managing it as a whole. That is one of the things that we use it for.

When we have expanded our licensing, it has always been easy to make an ROI-based decision. So, it's reasonably priced. We would like to have it cheaper, but we get more benefit from it than we pay for it. At the end of the day, that's all you can hope for.

We paid for our TAM, but I'm sure it's embedded in the cost. However, that's optional. Obviously, you can do it all yourself: Open all your own support tickets and just send in an email to your TAM. Our TAM has access to log in, because she's set up as a contractor for us. So, she can actually get in and work with us.

There weren't a lot of other options available at the time, but we did look at three others. I know there are other companies on the market. I don't remember which ones were competing with it at the time. There was only really one other in that space at the time, and there's a bunch now. Then, VMware was there competing as well, saying, "You just don't have it configured right. We can do better," but they really couldn't.

The model behind the scene that Turbonomic uses to make decisions just has a better way of balancing resources. It considers a lot more factors.

We use other tools to provide application-driven prioritization, to show us how top business applications and transactions are performing.

Unfortunately, a lot of our infrastructure in the cloud is still legacy. So, we can't make full use of it to go out and resize a server, because it will bring the application down. However, what we are doing is setting up integration servers now. This puts a change control out to make the recommended change and the owner of the server can approve that change, then it will take place within a maintenance window.

We don't manage resources in real-time. Most of our applications just don't support that. We don't have enough changes required that it would be mutually beneficial to us, so we aren't doing that yet, but we're headed in that direction.

It would be a big stretch for us to actually use Turbonomic to take resources away from servers. Our company has a philosophy, which was decided four or five years ago that the most important thing for us is for our applications to be up. So, if we waste a little money on the infrastructure to bolster applications when there is a problem, that is okay. We even have our own acronym, it's called margin of error (MOE). Typically, we are looking to have at least 30 percent free capacity on any server or cluster at any given time, which is certainly not running in the most efficient way possible, but we're okay with that. While we may spend three million dollars more a year on infrastructure, an hour long outage might cost us a million dollars. So, if there is a major problem with it with big performance degradation, then we want to have the capacity to step up and keep that application afloat while they figure out the issue.

It projects the outcome of if you are going to move from one set of infrastructure to another, then it will make a recommendation. For example, if I'm moving from one type of server to another type of server where there are different core counts, faster cores, and faster memory, then it will tell me in advance, "You need fewer resources to make that happen because you are moving to better equipment."

Biggest lesson learnt: What you should do is the obvious, it is just difficult to get people to do it. You need to have servers grouped and reported up to an executive level that can show the waste. Otherwise, you are working with server owners who have multiple priorities. They have a release that's due in two weeks which will impact their bonus at the end of the year, etc. If you hit them up, and go, "Hey, you're wasting about a thousand dollars a week on this server, and more on the others, so we need to resize them." They don't care. On an individual application or server basis, it's not a big deal. However, across a 26,000 server environment, $10,000 here or there pretty becomes real money. That is the biggest challenge: competing priorities. You have one group trying to manage infrastructure for the least possible amount while getting the best performance, and you have other people who have to deliver functionality to a business unit. If they don't, the business unit will lose a million dollars a day until they get it. Those are tough priorities to compete with.

Build that reporting infrastructure right from the beginning. Make sure you have your applications divided up by business unit, so you can take that overall feedback and write it up when you are showing it to a senior executive, "Hey look, you are paying for infrastructure. You are spending a million dollars more a month than you should be."

I would rate this solution as an eight (out of 10). It is a great app. The only reason I wouldn't give them a higher rating is from a reporting standpoint. That's just not their focus, but better reporting would help. We use an app called Cloud Temple with them, who is actually a partner of theirs. Turbonomic will tell you reporting is not what they see as their core competency, and they are going to take actions to optimize your environment. However, at the same time, they have done these partnerships with another company who does better reporting.