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ScienceLogic Acquires Zebrium

ScienceLogic has acquired machine learning analytics firm Zebrium to automatically find the root cause of complex, modern (i.e., containerized, cloud-native) application problems.

This partnership drastically reduces the time it takes to identify, diagnose and resolve business-service impacting issues, lowering IT costs and delivering superior customer and employee experiences.

With this acquisition, ScienceLogic is combining its AIOps capabilities with the root-cause analysis (RCA) technology of Zebrium to provide enterprises with the ability to comprehensively understand their IT estate, from endpoint devices to SaaS and cloud environments. The solution provides powerful machine learning analytics that draw on both real-time and historical data for a contextual understanding of the service impact and root cause when issues arise so they can be automatically remediated.

“We understand that at the end of the day, customers care about a few core things. Among those are making sense of drastically increased amounts of data, maintaining a quick time to resolution, and focusing on customer experience,” says Mike Nappi, CPO at ScienceLogic. “Our acquisition of Zebrium has its genesis in those customer mandates and stems from years of conversations with partners and clients to understand where the gaps are and how ScienceLogic can help fill them. Combining our capabilities with that of Zebrium creates a whole new level of analytics-driven insights and automation we can bring to bear for our customers.”

What does this mean for IT operations? Drastically reduced hours and resources spent trying to determine what is a potential problem, configuring systems to alert on those problems, and when they occur, combing through large volumes of logs from the application and infrastructure stack to determine the root cause – leaving IT teams the time and capacity to devote to revenue-generating activities.

“This partnership means that ITOps and DevOps teams will have the breathing room to commit time, energy, and resources to improving and supporting infrastructure and analytics standards, all while cutting IT costs,” said Ajay Singh, CEO at Zebrium. “With our machine learning capabilities combined with ScienceLogic’s service context and automation, organizations can greatly reduce the time they spend identifying and remediating issues – leaving them more time to spend on operations that deliver stellar digital experiences for customers and employees alike.”

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Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

ScienceLogic Acquires Zebrium

ScienceLogic has acquired machine learning analytics firm Zebrium to automatically find the root cause of complex, modern (i.e., containerized, cloud-native) application problems.

This partnership drastically reduces the time it takes to identify, diagnose and resolve business-service impacting issues, lowering IT costs and delivering superior customer and employee experiences.

With this acquisition, ScienceLogic is combining its AIOps capabilities with the root-cause analysis (RCA) technology of Zebrium to provide enterprises with the ability to comprehensively understand their IT estate, from endpoint devices to SaaS and cloud environments. The solution provides powerful machine learning analytics that draw on both real-time and historical data for a contextual understanding of the service impact and root cause when issues arise so they can be automatically remediated.

“We understand that at the end of the day, customers care about a few core things. Among those are making sense of drastically increased amounts of data, maintaining a quick time to resolution, and focusing on customer experience,” says Mike Nappi, CPO at ScienceLogic. “Our acquisition of Zebrium has its genesis in those customer mandates and stems from years of conversations with partners and clients to understand where the gaps are and how ScienceLogic can help fill them. Combining our capabilities with that of Zebrium creates a whole new level of analytics-driven insights and automation we can bring to bear for our customers.”

What does this mean for IT operations? Drastically reduced hours and resources spent trying to determine what is a potential problem, configuring systems to alert on those problems, and when they occur, combing through large volumes of logs from the application and infrastructure stack to determine the root cause – leaving IT teams the time and capacity to devote to revenue-generating activities.

“This partnership means that ITOps and DevOps teams will have the breathing room to commit time, energy, and resources to improving and supporting infrastructure and analytics standards, all while cutting IT costs,” said Ajay Singh, CEO at Zebrium. “With our machine learning capabilities combined with ScienceLogic’s service context and automation, organizations can greatly reduce the time they spend identifying and remediating issues – leaving them more time to spend on operations that deliver stellar digital experiences for customers and employees alike.”

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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 ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...