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ScienceLogic SL1 AIOps Platform Launched

ScienceLogic announced the launch of ScienceLogic SL1, its new, industry-defining AIOps platform.

ScienceLogic SL1 was designed and developed with the specific goal of leveling the playing field between the pace at which Dev can create great digital experiences, and the ability of Ops to make them resilient experiences.

“Ten years ago, we saw enterprises moving to a state of ephemeral operations, motivated by speed and agility. It started at the infrastructure layer but evolved quickly towards DevOps movements emphasizing service development and rapid release. Today’s performance management systems must provide operational insights in real-time derived from a deep understanding of dependency mapping between applications and their underlying infrastructure,” said ScienceLogic CEO, Dave Link.

SL1 understands how mission-critical applications connect to the underlying infrastructure by deriving topology maps, which enable real-time service health views that inform, analyze and act. This context allows businesses to bring meaning across various data silos and generate insights that drive automated actions like never before.

ScienceLogic SL1 Core Capabilities

- See – Automated real-time discovery inclusive of applications and infrastructure across IT silos and multi-cloud environments

- Contextualize - Automated topology maps to establish real-time relationships between disparate data sets bringing context to data

- Act - Automated issue discovery and subsequent remediation across a diverse range of technologies including CMDB (Configuration Management Database), DevOps and APM (Application Performance Management)

SL1 Technologies:

- PowerMap – If context is king for AIOps, PowerMap is what provides that context. PowerMap creates a multi-dimensional topological map of dependencies in real-time between all components and across all layers of technology - providing immediate context to the underlying raw data. This becomes a crucial input source for Machine Learning engines to provide actionable insights and automations.

- PowerSync – If automation is the endgame of AIOps, PowerSync is the engine that powers it. By providing a universal communication bridge to get data in, share data out and keep data synchronized, PowerSync breaks down the data silos that currently limit automation. It provides multi-directional and intelligent data transformations asynchronously and concurrently among any number of platforms. PowerSync completes the story with a powerful automation engine to enact change to the environment.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

ScienceLogic SL1 AIOps Platform Launched

ScienceLogic announced the launch of ScienceLogic SL1, its new, industry-defining AIOps platform.

ScienceLogic SL1 was designed and developed with the specific goal of leveling the playing field between the pace at which Dev can create great digital experiences, and the ability of Ops to make them resilient experiences.

“Ten years ago, we saw enterprises moving to a state of ephemeral operations, motivated by speed and agility. It started at the infrastructure layer but evolved quickly towards DevOps movements emphasizing service development and rapid release. Today’s performance management systems must provide operational insights in real-time derived from a deep understanding of dependency mapping between applications and their underlying infrastructure,” said ScienceLogic CEO, Dave Link.

SL1 understands how mission-critical applications connect to the underlying infrastructure by deriving topology maps, which enable real-time service health views that inform, analyze and act. This context allows businesses to bring meaning across various data silos and generate insights that drive automated actions like never before.

ScienceLogic SL1 Core Capabilities

- See – Automated real-time discovery inclusive of applications and infrastructure across IT silos and multi-cloud environments

- Contextualize - Automated topology maps to establish real-time relationships between disparate data sets bringing context to data

- Act - Automated issue discovery and subsequent remediation across a diverse range of technologies including CMDB (Configuration Management Database), DevOps and APM (Application Performance Management)

SL1 Technologies:

- PowerMap – If context is king for AIOps, PowerMap is what provides that context. PowerMap creates a multi-dimensional topological map of dependencies in real-time between all components and across all layers of technology - providing immediate context to the underlying raw data. This becomes a crucial input source for Machine Learning engines to provide actionable insights and automations.

- PowerSync – If automation is the endgame of AIOps, PowerSync is the engine that powers it. By providing a universal communication bridge to get data in, share data out and keep data synchronized, PowerSync breaks down the data silos that currently limit automation. It provides multi-directional and intelligent data transformations asynchronously and concurrently among any number of platforms. PowerSync completes the story with a powerful automation engine to enact change to the environment.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...