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
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...