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LogicMonitor Acquires Catchpoint

LogicMonitor has completed its acquisition of Catchpoint. 

Together, LogicMonitor and Catchpoint are redefining how modern businesses run, ending downtime and eliminating blind spots across the backbone of today’s connected world.

By combining LogicMonitor’s deep infrastructure and AI expertise with Catchpoint’s Internet-level intelligence, the new LogicMonitor platform delivers predictive visibility and control across cloud, code, and the Internet itself. The goal is simple: stop chasing alerts and start staying ahead of them.

“This is a defining moment for LogicMonitor and for enterprise technology,” said Christina Kosmowski, CEO of LogicMonitor. “Until now, IT teams have been juggling point tools that promise insight but deliver noise. That ends today. Together with Catchpoint we are giving customers the power to predict issues, prevent downtime, and finally make their systems as smart as the people who run them.”

Catchpoint spent a decade helping enterprises keep the Internet fast, reliable, and available. LogicMonitor brings the AI scale and infrastructure reach to make that reliability universal. The result is a comprehensive observability platform for the AI-era, one that connects what enterprises own with what they depend on and keeps everything running like it should.

“Catchpoint was founded to make the Internet better for everyone,” said Mehdi Daoudi, CEO and Co-Founder of Catchpoint. “We have helped teams detect issues faster, reduce MTTR, and protect billions of sessions. Now, as part of LogicMonitor, we can do it on a global scale and redefine what performance means in the AI era.”

Once integrated, Catchpoint’s global performance data including synthetic, network, and real-user monitoring will feed directly into Edwin AI, LogicMonitor’s intelligent engine that does more than raise alarms. It explains them. Together, the platform will predict incidents, ultimately automate fixes, and give enterprises the kind of full-stack clarity that makes finger-pointing obsolete.

Here is what customers get out of the deal:

  • Comprehensive insight: Infrastructure, Internet, and user experience data in one place.
  • Predictive performance: AI that spots trouble before it becomes downtime.
  • Simpler operations: Fewer tools, fewer alerts, fewer headaches.
  • Internet-aware reliability: Visibility from the cloud to the last mile.
  • Global scale: Monitoring from thousands of vantage points worldwide.

The transaction closed following customary approvals. LogicMonitor and Catchpoint teams are already integrating capabilities to accelerate AI-driven innovation for customers worldwide. Reactive IT had its moment. LogicMonitor just made it obsolete. 

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LogicMonitor Acquires Catchpoint

LogicMonitor has completed its acquisition of Catchpoint. 

Together, LogicMonitor and Catchpoint are redefining how modern businesses run, ending downtime and eliminating blind spots across the backbone of today’s connected world.

By combining LogicMonitor’s deep infrastructure and AI expertise with Catchpoint’s Internet-level intelligence, the new LogicMonitor platform delivers predictive visibility and control across cloud, code, and the Internet itself. The goal is simple: stop chasing alerts and start staying ahead of them.

“This is a defining moment for LogicMonitor and for enterprise technology,” said Christina Kosmowski, CEO of LogicMonitor. “Until now, IT teams have been juggling point tools that promise insight but deliver noise. That ends today. Together with Catchpoint we are giving customers the power to predict issues, prevent downtime, and finally make their systems as smart as the people who run them.”

Catchpoint spent a decade helping enterprises keep the Internet fast, reliable, and available. LogicMonitor brings the AI scale and infrastructure reach to make that reliability universal. The result is a comprehensive observability platform for the AI-era, one that connects what enterprises own with what they depend on and keeps everything running like it should.

“Catchpoint was founded to make the Internet better for everyone,” said Mehdi Daoudi, CEO and Co-Founder of Catchpoint. “We have helped teams detect issues faster, reduce MTTR, and protect billions of sessions. Now, as part of LogicMonitor, we can do it on a global scale and redefine what performance means in the AI era.”

Once integrated, Catchpoint’s global performance data including synthetic, network, and real-user monitoring will feed directly into Edwin AI, LogicMonitor’s intelligent engine that does more than raise alarms. It explains them. Together, the platform will predict incidents, ultimately automate fixes, and give enterprises the kind of full-stack clarity that makes finger-pointing obsolete.

Here is what customers get out of the deal:

  • Comprehensive insight: Infrastructure, Internet, and user experience data in one place.
  • Predictive performance: AI that spots trouble before it becomes downtime.
  • Simpler operations: Fewer tools, fewer alerts, fewer headaches.
  • Internet-aware reliability: Visibility from the cloud to the last mile.
  • Global scale: Monitoring from thousands of vantage points worldwide.

The transaction closed following customary approvals. LogicMonitor and Catchpoint teams are already integrating capabilities to accelerate AI-driven innovation for customers worldwide. Reactive IT had its moment. LogicMonitor just made it obsolete. 

The Latest

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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