We performed a comparison between IBM Spectrum Computing and IBM Turbonomic based on real PeerSpot user reviews.
Find out in this report how the two Cloud Management solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"Spectrum Computing's best features are its speed, robustness, and data processing and analysis."
"The most valuable feature is the backup capability."
"The most valuable aspect of the product is the policy driving resource management, to optimize the computing across data centers."
"It has automated a lot of things. We have saved 30 to 35 percent in human resource time and cost, which is pretty substantial. We don't have a big workforce here, so we have to use all the automation we can get."
"We can manage multiple environments using a single pane of glass, which is something that I really like."
"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."
"We've saved hundreds of hours. Most of the time those hours would have to be after hours as well, which are more valuable to me as that's my personal time."
"Turbonomic has helped optimize cloud operations and reduced our cloud costs significantly. Overall, we are at about 40 percent savings, and we spend about three million a year just in Azure. It reduces the size of the VMs, putting them into the right template for usage. People don't realize that you don't have to future-proof a virtual machine in Azure. You just need to build it for today. As the business or service grows, you can scale up or out. About 90 percent of all the costs that we've reduced has been from sizing machines appropriately."
"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."
"The tool provides the ability to look at the consumption utilization over a period of time and determine if we need to change that resource allocation based on the actual workload consumption, as opposed to how IT has configured it. Therefore, we have come to realize that a lot of our workloads are overprovisioned, and we are spending more money in the public cloud than we need to."
"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."
"Spectrum Computing is lagging behind other products, most likely because it hasn't been shifted to the cloud."
"We have not been able to use deduplication."
"We'd like to see some AI model training for machine learning."
"Additional interfaces would be helpful."
"It would be good for Turbonomic, on their side, to integrate with other companies like AppDynamics or SolarWinds or other monitoring softwares. I feel that the actual monitoring of applications, mixed in with their abilities, would help. That would be the case wherever Turbonomic lacks the ability to monitor an application or in cases where applications are so customized that it's not going to be able to handle them. There is monitoring that you can do with scripting that you may not be able to do with Turbonomic."
"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."
"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."
"There is room for improvement [with] upgrades. We have deployed the newer version, version 8 of Turbonomic. The problem is that there is no way to upgrade between major Turbonomic versions. You can upgrade minor versions without a problem, but when you go from version 6 to version 7, or version 7 to version 8, you basically have to deploy it new and let it start gathering data again. That is a problem because all of the data, all of the savings calculations that had been done on the old version, are gone. There's no way to keep track of your lifetime savings across versions."
"The way it handles updates needs to be improved."
"It would be nice for them to have a way to do something with physical machines, but I know that is not their strength Thankfully, the majority of our environment is virtual, but it would be nice to see this type of technology across some other platforms. It would be nice to have capacity planning across physical machines."
"I would love to see Turbonomic analyze backup data. We have had people in the past put servers into daily full backups with seven-year retention and where the disk size is two terabytes. So, every single day, there is a two terabyte snapshot put into a Blob somewhere. I would love to see Turbonomic say, "Here are all your backups along with the age of them," to help us manage the savings by not having us spend so much on the storage in Azure. That would be huge."
IBM Spectrum Computing uses intelligent workload and policy-driven resource management to optimize resources across the data center, on premises and in the cloud. Now up to 150X faster and scalable to over 160,000 cores, IBM provides you with the latest advances in software-defined infrastructure to help you unleash the power of your distributed mission-critical high performance computing (HPC), analytics and big data applications as well as a new generation open source frameworks such as Hadoop and Spark.
IBM Turbonomic Application Resource Management (ARM) software is used by customers to assure application performance while eliminating inefficiencies by dynamically resourcing applications across hybrid and multicloud environments. Turbonomic customers report an average 33% reduction in cloud and infrastructure waste without impacting application performance, and return-on-investment of 471% over three years.
For further information, please visit www.ibm.com/cloud/turbonomic
IBM Spectrum Computing is ranked 24th in Cloud Management with 3 reviews while IBM Turbonomic is ranked 3rd in Cloud Management with 21 reviews. IBM Spectrum Computing is rated 8.0, while IBM Turbonomic is rated 9.0. The top reviewer of IBM Spectrum Computing writes "Responsive technical support with good reliability, and optimization of computing". On the other hand, the top reviewer of IBM Turbonomic writes "Helps us optimize cloud operations, reducing our cloud costs". IBM Spectrum Computing is most compared with Red Hat CloudForms, HPE Ezmeral Data Fabric, Cloudera Distribution for Hadoop and VMware Aria Operations, whereas IBM Turbonomic is most compared with VMware Aria Operations, Azure Cost Management, CloudHealth, Densify and Cisco Intersight. See our IBM Spectrum Computing vs. IBM Turbonomic report.
See our list of best Cloud Management vendors.
We monitor all Cloud Management reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.