
Software AG has entered into an agreement to sell webMethods and StreamSets to IBM for a purchase price of €2.13 billion, subject to customary closing adjustments.
The acquisition is a continuation of IBM’s focus, investment and leadership in hybrid cloud and AI. This outcome is a testament to the hard work and collective achievements of the global Software AG team over the past five years.
Sanjay Brahmawar, Software AG CEO, said: “This transaction is a major validation of our strategy and a recognition of the products at the heart of our Super iPaaS vision. Given IBM’s global scale and focus on hybrid cloud and AI, our people have a fantastic opportunity to develop the Super iPaaS proposition with them. The brilliant people remaining with the company will continue innovating for the benefit of our customers, employees and all our other stakeholders.”
He added: “Both Software AG and IBM are fully committed to delivering the best products and services available to their customers, and to continuing to contribute to their success.”
Dinesh Nirmal, SVP, Products, IBM Software, commented: “As enterprises continue to accelerate their digital transformations, application and data integration solutions are critical elements for application modernization and effectively deploying AI across the enterprise. Combined with IBM’s watsonx AI and data platform, as well as its application modernization, data fabric and IT automation products, StreamSets and webMethods will help clients unlock the full potential of their applications and data. This helps drive innovation while preparing businesses for AI, no matter where applications or data reside.”
Christian Lucas, Chairman of the Supervisory Board of Software AG and Managing Partner of Silver Lake, added: “The Software AG team has done exceptionally good work in bringing its integration business to the cloud and setting it on a compelling path toward its Super iPaaS vision. Silver Lake is proud to have helped the team identify and enable the next great chapter for this business with IBM, and we remain fully committed to supporting the Software AG team in progressing the company’s strategy.”
The consummation of the transaction is subject to customary closing conditions, including regulatory approvals. It is expected to be completed in the second quarter of 2024.
The Latest
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...