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IBM to Acquire Turbonomic

IBM announced a definitive agreement to acquire Turbonomic, an Application Resource Management (ARM) and Network Performance Management (NPM) software provider based in Boston, MA.

The acquisition will provide businesses with full stack application observability and management to assure performance and minimize costs using AI to optimize resources – such as containers, VMs, servers, storage, networks, and databases. This will ensure they can dynamically and more efficiently assess and manage the performance of any application, anywhere. Financial details were not disclosed.

The acquisition complements IBM’s recent acquisition of Instana for application performance monitoring (APM) and observability, and the launch of IBM Cloud Pak for Watson AIOps to automate IT Operations using AI. By acquiring Turbonomic, IBM will be able to provide customers with AI-powered automation capabilities that span from AIOps (the use of AI to automate IT Operations) to application and infrastructure observability – all built on Red Hat OpenShift to run across any hybrid cloud environment.

“IBM continues to reshape its future as a hybrid cloud and AI company,” said Rob Thomas, Senior VP, IBM Cloud and Data Platform. “The Turbonomic acquisition is yet another example of our commitment to making the most impactful investments to advance this strategy and ensure customers find the most innovative ways to fuel their digital transformations.”

With the acquisition of Turbonomic, IBM will help companies overcome the high costs associated with managing performance and availability for multiple applications sharing an increasingly complex hybrid cloud environment. Given these challenges, organizations are seeking to adopt AIOps for full stack observability and visibility into their IT resources so they can deliver high availability and performance of applications at lower costs.

“We believe that AI-powered automation has become inevitable, helping to make all information-centric jobs more productive,” said Dinesh Nirmal, GM, IBM Automation. “That’s why IBM continues to invest in providing our customers with a one-stop shop of AI-powered automation capabilities that spans business processes and IT. The addition of Turbonomic now takes our portfolio another major step forward by ensuring customers will have full visibility into what is going on throughout their hybrid cloud infrastructure, and across their entire enterprise.”

“Businesses are looking for AI-driven software to help them manage the scale and complexity challenges of running applications cross-cloud,” said Ben Nye, CEO, Turbonomic. “Turbonomic not only prescribes actions, but allows customers to take them. The combination of IBM and Turbonomic will continuously assure target application response times even during peak demand.”

Turbonomic provides businesses with its ARM software that simultaneously optimizes the performance, compliance, and cost of applications in real-time. Upon close of the acquisition, IBM plans to integrate Turbonomic’s ARM software with the APM and real-time observability capabilities of Instana and the ITOps capabilities of IBM Cloud Pak for Watson AIOps to help customers assure application performance and minimize costs by driving optimization across development, test and production environments.

By integrating Turbonomic ARM with Instana’s APM capabilities, a user will now be able to automate actions to optimize their underlying IT infrastructure and assure performance across applications. The Turbonomic ARM integration with IBM Cloud Pak for Watson AIOps will enrich the ITOps experience in cross-cloud management by bridging an application’s topology to the resources on which it runs. This ensures customers can deliver quicker resolution of incidents or, if resourcing actions are automated, automatically absorb demand spikes with no degradation to end user response time.

Another major benefit for customers is the potential for sustainability improvements related to lower server, facilities and carbon usage afforded by Turbonomic’s ability to continually right size resources, without compromising application performance.

As 5G adoption continues to grow, enterprises are also looking to move workloads to the edge. This is driving networking to be an integral component of the application deployment strategy. With this acquisition, IBM plans to leverage Turbonomic’s NPM products and strong presence in the telecommunications industry to complement its own offerings and expertise in this area, helping customers intelligently optimize applications running in 5G environments.

Turbonomic has built and maintains an OEM relationship with Cisco through Cisco Intersight Workload Optimizer. The acquisition also builds on IBM’s growing investment in its ecosystem of business partners such as Cisco to help customers accelerate their journey to hybrid cloud and AI.

IBM continues its focus on providing organizations with a one-stop shop of AI-powered automation capabilities for business and IT all built on Red Hat OpenShift, helping to automate their entire enterprise, from robotic process automation (RPA), to AIOps, ARM and process mining. This is the latest move in a series of recent IBM acquisitions – including myInvenio, Instana and WDG Automation; ecosystem partnerships; organic R&D – including the launch of Cloud Pak for Watson AIOps; and customer adoption by leading brands -including CaixaBank, PNC Financial Services and Banco Popular.

The transaction is subject to customary closing conditions. It is anticipated the transaction will close in the second quarter of 2021.

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IBM to Acquire Turbonomic

IBM announced a definitive agreement to acquire Turbonomic, an Application Resource Management (ARM) and Network Performance Management (NPM) software provider based in Boston, MA.

The acquisition will provide businesses with full stack application observability and management to assure performance and minimize costs using AI to optimize resources – such as containers, VMs, servers, storage, networks, and databases. This will ensure they can dynamically and more efficiently assess and manage the performance of any application, anywhere. Financial details were not disclosed.

The acquisition complements IBM’s recent acquisition of Instana for application performance monitoring (APM) and observability, and the launch of IBM Cloud Pak for Watson AIOps to automate IT Operations using AI. By acquiring Turbonomic, IBM will be able to provide customers with AI-powered automation capabilities that span from AIOps (the use of AI to automate IT Operations) to application and infrastructure observability – all built on Red Hat OpenShift to run across any hybrid cloud environment.

“IBM continues to reshape its future as a hybrid cloud and AI company,” said Rob Thomas, Senior VP, IBM Cloud and Data Platform. “The Turbonomic acquisition is yet another example of our commitment to making the most impactful investments to advance this strategy and ensure customers find the most innovative ways to fuel their digital transformations.”

With the acquisition of Turbonomic, IBM will help companies overcome the high costs associated with managing performance and availability for multiple applications sharing an increasingly complex hybrid cloud environment. Given these challenges, organizations are seeking to adopt AIOps for full stack observability and visibility into their IT resources so they can deliver high availability and performance of applications at lower costs.

“We believe that AI-powered automation has become inevitable, helping to make all information-centric jobs more productive,” said Dinesh Nirmal, GM, IBM Automation. “That’s why IBM continues to invest in providing our customers with a one-stop shop of AI-powered automation capabilities that spans business processes and IT. The addition of Turbonomic now takes our portfolio another major step forward by ensuring customers will have full visibility into what is going on throughout their hybrid cloud infrastructure, and across their entire enterprise.”

“Businesses are looking for AI-driven software to help them manage the scale and complexity challenges of running applications cross-cloud,” said Ben Nye, CEO, Turbonomic. “Turbonomic not only prescribes actions, but allows customers to take them. The combination of IBM and Turbonomic will continuously assure target application response times even during peak demand.”

Turbonomic provides businesses with its ARM software that simultaneously optimizes the performance, compliance, and cost of applications in real-time. Upon close of the acquisition, IBM plans to integrate Turbonomic’s ARM software with the APM and real-time observability capabilities of Instana and the ITOps capabilities of IBM Cloud Pak for Watson AIOps to help customers assure application performance and minimize costs by driving optimization across development, test and production environments.

By integrating Turbonomic ARM with Instana’s APM capabilities, a user will now be able to automate actions to optimize their underlying IT infrastructure and assure performance across applications. The Turbonomic ARM integration with IBM Cloud Pak for Watson AIOps will enrich the ITOps experience in cross-cloud management by bridging an application’s topology to the resources on which it runs. This ensures customers can deliver quicker resolution of incidents or, if resourcing actions are automated, automatically absorb demand spikes with no degradation to end user response time.

Another major benefit for customers is the potential for sustainability improvements related to lower server, facilities and carbon usage afforded by Turbonomic’s ability to continually right size resources, without compromising application performance.

As 5G adoption continues to grow, enterprises are also looking to move workloads to the edge. This is driving networking to be an integral component of the application deployment strategy. With this acquisition, IBM plans to leverage Turbonomic’s NPM products and strong presence in the telecommunications industry to complement its own offerings and expertise in this area, helping customers intelligently optimize applications running in 5G environments.

Turbonomic has built and maintains an OEM relationship with Cisco through Cisco Intersight Workload Optimizer. The acquisition also builds on IBM’s growing investment in its ecosystem of business partners such as Cisco to help customers accelerate their journey to hybrid cloud and AI.

IBM continues its focus on providing organizations with a one-stop shop of AI-powered automation capabilities for business and IT all built on Red Hat OpenShift, helping to automate their entire enterprise, from robotic process automation (RPA), to AIOps, ARM and process mining. This is the latest move in a series of recent IBM acquisitions – including myInvenio, Instana and WDG Automation; ecosystem partnerships; organic R&D – including the launch of Cloud Pak for Watson AIOps; and customer adoption by leading brands -including CaixaBank, PNC Financial Services and Banco Popular.

The transaction is subject to customary closing conditions. It is anticipated the transaction will close in the second quarter of 2021.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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