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Head to AIOps and enjoy!
Updated February 12, 2022

Head to AIOps and enjoy!

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Software Version

Enterprise Edition

Overall Satisfaction with Turbonomic, an IBM Company

Turbonomic helps with supporting application resource management and network performance data incorporating machine learning to help create a microservices architecture where AIOps is addressing the need to remove legacy applications. It's been able to allow to monitor across multiple service lines as storage and database capabilities while automating many tasks. It also supports virtual machine setup.
  • Automation.
  • Scaling VM's.
  • Data analytics.
  • Turning off some features at times may take some work, but feasible.
  • More ways for targeting resources to make it more GUI-friendly.
  • Reporting to be customized views (for some).
  • Automation/saving time.
  • Storage level forecasting option.
  • Reduce MTTR.

Do you think IBM Turbonomic delivers good value for the price?

Yes

Are you happy with IBM Turbonomic's feature set?

Yes

Did IBM Turbonomic live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of IBM Turbonomic go as expected?

Yes

Would you buy IBM Turbonomic again?

Yes

Turbonomic has provided the ability to query out on business-critical applications and made it possible with AIOps. It's allowed us to monitor quickly, improve on the MTTR and extend this to the VM functionality. It has also allowed us to monitor data across multiple applications without any misconfiguring for the data to flow.
Yes - we could perform more data analysis and bring that towards customizable scripts which can help with automation, monitoring, and deployment. It has brought great unity between multiple teams in allowing for further collaboration. Also, Turbonomic has done this since application metrics can be compared much easier than before.
It has reduced our cloud spending and made our dependency less on the data center and more on the investment in AWS and Azure. The amount of savings is reflected based on the applications our team has been able to work through on new assignments through this capability from Turbonomic.
Turbonomic helps to bring AIOps in the mix where a lot of applications may not have that embedded with Network Monitoring as well. In addition, Turobonomic helps with fast deployment and is beneficial in supporting AWS and Azure applications especially. Less appropriate if the goal is just to specialize only in reporting. The platform has that but it is not a visualization platform.

Value of Using Turbonomic

  • Azure
  • Microsoft
There is a target type and then an application-driven architecture in Azure for insights on the data almost for monitoring and utilization metrics. The integration wasn't difficult in that an Azure SQL Database Managed Instance could be created and then brought together with an SSO option in Azure and searching for Turbonomic.
A lot of the integrations were being able to focus very much on pulling out resources on an APM front with computing, storage and other metrics where the ability to work with the configuration environment (global environment) to focus on costs by account, with budget, facilitate migration planning, and set up details on automation and Virtual Data Center also.
It allocates resources among applications by showing more on the cost breakdown by cloud service, with metrics on cloud provider information like Azure Management, Identity, Networking, Storage with costs per day, and total services costs. This then could facilitate and show the corresponding actions thereafter upon scaling.
It's helped for these cost savings since with the cost breakdown it is real-time costs and can be then used for scaling cloud instances accordingly in meeting the application demand. And migration planning is also part of the plan on determining which to execute and why.
It has been able to help with improving on migration to the public cloud, workload migration, and ability to add workloads easily. All of these core functions have helped to see how we can check which corresponding data regions and which VM's to use. And a place to compare with and without Turbonomic the differences.