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Paessler Acquires ITPS AG

Paessler AG announced the successful acquisition of ITPS Group, with subsidiaries in Switzerland, the Czech Republic, and Romania as well as business activities in India.

ITPS is known for its software products under the name CORP-IT. Based in Frauenfeld, Switzerland, the company was founded by Thomas Wächter, an entrepreneur from Baden, Germany. Prior to this strategic development, ITPS was a valued Paessler Alliance Partner & Paessler Gold Partner for many years.

Three of the central solutions of ITPS, which are now being further developed and marketed under the Paessler umbrella brand under new product names, are:

- PRTG SLA Reporter: Improved reporting capabilities for service level agreements (SLA), based on historical status data, characterize this extension for PRTG. Customers benefit from the creation of granular SLA reports that can show uptime/downtime values as well as root causes for outages. Data is stored in an MS SQL database, which facilitates integration with third-party tools. Stakeholders can be easily and efficiently informed about SLA compliance. This makes PRTG SLA Reporter the ideal tool to extend the included PRTG reporting features or to meet specific SLA reporting requirements.

- PRTG Data Exporter: This extension is a desktop application that lets you work even better with metric monitoring data from PRTG and integrate it into a database management system. Customers can combine data from multiple PRTG servers into one database and make this data accessible to external tools (directly through the database without an API). It supports MS SQL and MySQL and enables integration with tools such as Microsoft Power BI or Grafana. PRTG Data Exporter is the ideal complement for all users who want to connect their own analysis tools or who want to generate reports with their own reporting tools.

- PRTG Database Observer: This product extension adds new database types, such as SAP HANA and IBM DB2, to the database monitoring capabilities of PRTG. It enables detailed monitoring of additional relational database systems and provides predefined SQL queries from database experts. Users can easily manage queries and test them in a dedicated Windows application. Monitoring critical database metrics helps ensure optimal performance and user experience at all times. PRTG Database Observer is the ideal tool to keep an eye on values that are critical for business-relevant processes.

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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.

Paessler Acquires ITPS AG

Paessler AG announced the successful acquisition of ITPS Group, with subsidiaries in Switzerland, the Czech Republic, and Romania as well as business activities in India.

ITPS is known for its software products under the name CORP-IT. Based in Frauenfeld, Switzerland, the company was founded by Thomas Wächter, an entrepreneur from Baden, Germany. Prior to this strategic development, ITPS was a valued Paessler Alliance Partner & Paessler Gold Partner for many years.

Three of the central solutions of ITPS, which are now being further developed and marketed under the Paessler umbrella brand under new product names, are:

- PRTG SLA Reporter: Improved reporting capabilities for service level agreements (SLA), based on historical status data, characterize this extension for PRTG. Customers benefit from the creation of granular SLA reports that can show uptime/downtime values as well as root causes for outages. Data is stored in an MS SQL database, which facilitates integration with third-party tools. Stakeholders can be easily and efficiently informed about SLA compliance. This makes PRTG SLA Reporter the ideal tool to extend the included PRTG reporting features or to meet specific SLA reporting requirements.

- PRTG Data Exporter: This extension is a desktop application that lets you work even better with metric monitoring data from PRTG and integrate it into a database management system. Customers can combine data from multiple PRTG servers into one database and make this data accessible to external tools (directly through the database without an API). It supports MS SQL and MySQL and enables integration with tools such as Microsoft Power BI or Grafana. PRTG Data Exporter is the ideal complement for all users who want to connect their own analysis tools or who want to generate reports with their own reporting tools.

- PRTG Database Observer: This product extension adds new database types, such as SAP HANA and IBM DB2, to the database monitoring capabilities of PRTG. It enables detailed monitoring of additional relational database systems and provides predefined SQL queries from database experts. Users can easily manage queries and test them in a dedicated Windows application. Monitoring critical database metrics helps ensure optimal performance and user experience at all times. PRTG Database Observer is the ideal tool to keep an eye on values that are critical for business-relevant processes.

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.