IBM has completed its acquisition of Apptio Inc. after receiving all required regulatory approvals.
The acquisition brings together Apptio's FinOps offerings, including ApptioOne, Cloudability and Targetprocess, and IBM's automation portfolio of Turbonomic, AIOps and Instana to give clients a "virtual command center" for managing, optimizing and automating technology spending decisions.
With AI and foundation models top of mind for clients and partners, IBM will also augment its watsonx AI and data platform with Apptio's $450 billion in anonymized IT spend data, unlocking new innovation, insight and value.
"The combination of Apptio products and IBM's IT automation portfolio will give businesses a 360-degree technology management platform they can use to optimize and automate decisions across their IT landscapes," said Rob Thomas, Senior Vice President, Software and Chief Commercial Officer, IBM. "We are bringing together market-leading and best-in-class solutions to continue to reshape IT from a cost center to a true competitive advantage, powered by automation and AI."
Starting immediately, clients can leverage the early integration between Apptio and IBM through their Cloudability and Turbonomic offerings. This is an important first step as IBM looks to drive significant synergy across several key growth areas, including automation, Red Hat, IBM Consulting, and IBM's broader AI portfolio.
Cloudability gives organizations the data, insights and recommendations needed to understand and eliminate waste from their cloud spend, while Turbonomic generates trustworthy optimization decisions that can be automated to unlock true cloud elasticity, getting rid of overprovisioning to protect performance. Together, these products can give clients full coverage for the "Inform," "Optimize" and "Operate" stages of the FinOps Framework, providing what they need to control cloud spend without slowing innovation or negatively impacting operational performance.
Cloudability can ingest Turbonomic executed and proposed actions to provide a shared, single view across services that helps stakeholders understand the impact that has been, and can be, achieved by bringing these two leading IT automation offerings together.
The close of the Apptio acquisition is one of a series of investments in IT Automation by IBM over the last three years to help solve the problems facing today's IT and business leaders. In 2020, IBM launched its IT Automation portfolio when it announced its AIOps offerings that used AI and automation to help enterprises self-detect, diagnose and respond to IT anomalies in real time. Later that year, IBM acquired Instana, recognizing that modern applications and operations required real-time observability. Then, in 2021, IBM acquired Turbonomic which has specialized in helping clients optimize for application performance at the lowest cost with automation. Now, with the acquisition of Apptio, IBM will provide real-time data and actionable insights for leaders to make smarter spending decisions and realize value faster as they transform their operations.
IBM previously announced a definitive agreement to acquire Apptio from Vista Equity Partners on June 26, 2023.
"Our journey with Apptio is a testament to Vista's ability to create consistent outcomes that drive value for our stakeholders," said Robert F. Smith, Founder, Chairman and CEO of Vista Equity Partners. "We are proud of our continued momentum, even amidst these challenged market conditions, and look forward to seeing how Apptio's technology will bolster IBM's IT automation and AI capabilities in the years ahead. It's been an honor to partner with a visionary founder like Sunny and we wish the entire Apptio team the best in the next phase of their growth with IBM."
The Latest
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 ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.