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ScienceLogic Acquires AppFirst

ScienceLogic has acquired AppFirst.

The acquisition included several patented technologies and scale-out data processing capability. Transaction terms were not disclosed.

ScienceLogic plans on releasing the industry’s first converged platform offering later this year. With newly added best in class agent-based analytics and sub-second application data collection, alongside ScienceLogic’s existing discovery and performance visibility, customers will benefit from an entirely new and better way to manage IT.

“We believe that application dependency discovery and management innovation will accelerate Hybrid Cloud production environments within the $23 billion cloud-computing market,” said Dave Link, CEO, ScienceLogic. “As we now embed deep application discovery and analytics capabilities into our Hybrid IT Monitoring platform, organizations for the first time can monitor all business-critical services, including deep application performance, fault and configuration analytics, across their Hybrid IT environments. Our acquisition of AppFirst represents a major leap in ScienceLogic’s sophisticated service assurance capabilities core to helping our customers run their businesses better.”

Customer benefits include:

- Real-time visibility: High Definition Monitoring enables detection of transient problems when they occur, enabling proactive monitoring and better availability. DevOps teams will appreciate enhanced support for dynamic workloads that live for minutes or even seconds covering application containers and virtual services.

- Scale-out architecture: Monitor any technology, any vendor, anywhere. Scale-out, microservices-based architecture ensures the business never misses a metric or log file.

- Enhanced Virtualized Systems support: Visibility across public clouds and converged compute private clouds, provides actionable analytics in environments that are more dynamic in nature

- SaaS enabled: Cloud-neutral and on-prem ready, ScienceLogic can be deployed anywhere and managed in one place. This reduces the cost of monitoring by giving customers the flexibility to determine where and how they wish to run their monitoring platform.

- Application-aware: Provides a complete view of Hybrid IT environments, from the business service down to the automatically correlated infrastructure elements. The result is higher quality service delivery at lower cost.

- Log and network layer analytics: Connecting real-time log and network performance data provides unprecedented visibility into potential service problems, resulting in faster root cause analysis and better proactive monitoring - enabling IT agility in solving problems before they impact the business.

- Automation: Enhanced automation actions from automated provisioning and discovery to corrective actions delivered via smart targeted runbook automation actions.

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 ...

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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.

ScienceLogic Acquires AppFirst

ScienceLogic has acquired AppFirst.

The acquisition included several patented technologies and scale-out data processing capability. Transaction terms were not disclosed.

ScienceLogic plans on releasing the industry’s first converged platform offering later this year. With newly added best in class agent-based analytics and sub-second application data collection, alongside ScienceLogic’s existing discovery and performance visibility, customers will benefit from an entirely new and better way to manage IT.

“We believe that application dependency discovery and management innovation will accelerate Hybrid Cloud production environments within the $23 billion cloud-computing market,” said Dave Link, CEO, ScienceLogic. “As we now embed deep application discovery and analytics capabilities into our Hybrid IT Monitoring platform, organizations for the first time can monitor all business-critical services, including deep application performance, fault and configuration analytics, across their Hybrid IT environments. Our acquisition of AppFirst represents a major leap in ScienceLogic’s sophisticated service assurance capabilities core to helping our customers run their businesses better.”

Customer benefits include:

- Real-time visibility: High Definition Monitoring enables detection of transient problems when they occur, enabling proactive monitoring and better availability. DevOps teams will appreciate enhanced support for dynamic workloads that live for minutes or even seconds covering application containers and virtual services.

- Scale-out architecture: Monitor any technology, any vendor, anywhere. Scale-out, microservices-based architecture ensures the business never misses a metric or log file.

- Enhanced Virtualized Systems support: Visibility across public clouds and converged compute private clouds, provides actionable analytics in environments that are more dynamic in nature

- SaaS enabled: Cloud-neutral and on-prem ready, ScienceLogic can be deployed anywhere and managed in one place. This reduces the cost of monitoring by giving customers the flexibility to determine where and how they wish to run their monitoring platform.

- Application-aware: Provides a complete view of Hybrid IT environments, from the business service down to the automatically correlated infrastructure elements. The result is higher quality service delivery at lower cost.

- Log and network layer analytics: Connecting real-time log and network performance data provides unprecedented visibility into potential service problems, resulting in faster root cause analysis and better proactive monitoring - enabling IT agility in solving problems before they impact the business.

- Automation: Enhanced automation actions from automated provisioning and discovery to corrective actions delivered via smart targeted runbook automation actions.

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.