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ScienceLogic SL1 Available on VMware Cloud on AWS

ScienceLogic SL1 is available to customers of VMware Cloud on AWS.

Powered by algorithmic IT operations (AIOps), SL1 delivers real-time context to operational data for customers of VMware Cloud on AWS.

VMware Cloud on AWS delivers a seamlessly integrated hybrid cloud that extends on-premises vSphere environments to a VMware SDDC running on Amazon EC2 elastic, bare-metal infrastructure that is fully integrated as part of the AWS Cloud.

SL1 enables VMware Cloud on AWS customers to understand how mission-critical applications connect to their underlying IT infrastructure through consistent, controlled visibility and management. Multi-dimensional topology maps, known as PowerMap, enable real-time views into infrastructure, application, and service health so IT can proactively and responsively inform, analyze, and act. This context is driven by PowerSync, which allows businesses to combine data across multiple silos and generate insights that drive automated actions, regardless of whether a service runs on-premises or in the cloud. The result offers enterprises the opportunity to stay ahead of market competition by applying machine-speed to ongoing business growth.

“Modern enterprises are in a digital, ephemeral phase of IT operations that relies heavily on the relationship between mission-critical applications and infrastructure. Success requires constant visibility across multiple data centers and cloud environments and demands contextualized results at machine-speed,” said Dave Link, founder and CEO of ScienceLogic. “Our partnership with VMware provides businesses with complete visibility across their complex IT infrastructure and applications, and offers real-time contextualized data insights, even as the infrastructure and applications change. SL1 applies AI/machine learning to optimize resources and remediate issues at machine-speed, which is critical to creating meaningful and resilient digital experiences.”

“VMware Cloud on AWS gives customers the SDDC experience from the leader in private cloud, running on the leading public cloud provider, AWS,” said Kristin Edwards, Director, Technology Alliance Partner Program, VMware. “Solutions such as ScienceLogic SL1 enable IT teams to reduce cost, increase efficiency, and create operational consistency across cloud environments. We’re excited to work with partners such as ScienceLogic to enhance native VMware Cloud on AWS capabilities and empower customers with flexibility and choice in solutions that can drive business value.”

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ScienceLogic SL1 Available on VMware Cloud on AWS

ScienceLogic SL1 is available to customers of VMware Cloud on AWS.

Powered by algorithmic IT operations (AIOps), SL1 delivers real-time context to operational data for customers of VMware Cloud on AWS.

VMware Cloud on AWS delivers a seamlessly integrated hybrid cloud that extends on-premises vSphere environments to a VMware SDDC running on Amazon EC2 elastic, bare-metal infrastructure that is fully integrated as part of the AWS Cloud.

SL1 enables VMware Cloud on AWS customers to understand how mission-critical applications connect to their underlying IT infrastructure through consistent, controlled visibility and management. Multi-dimensional topology maps, known as PowerMap, enable real-time views into infrastructure, application, and service health so IT can proactively and responsively inform, analyze, and act. This context is driven by PowerSync, which allows businesses to combine data across multiple silos and generate insights that drive automated actions, regardless of whether a service runs on-premises or in the cloud. The result offers enterprises the opportunity to stay ahead of market competition by applying machine-speed to ongoing business growth.

“Modern enterprises are in a digital, ephemeral phase of IT operations that relies heavily on the relationship between mission-critical applications and infrastructure. Success requires constant visibility across multiple data centers and cloud environments and demands contextualized results at machine-speed,” said Dave Link, founder and CEO of ScienceLogic. “Our partnership with VMware provides businesses with complete visibility across their complex IT infrastructure and applications, and offers real-time contextualized data insights, even as the infrastructure and applications change. SL1 applies AI/machine learning to optimize resources and remediate issues at machine-speed, which is critical to creating meaningful and resilient digital experiences.”

“VMware Cloud on AWS gives customers the SDDC experience from the leader in private cloud, running on the leading public cloud provider, AWS,” said Kristin Edwards, Director, Technology Alliance Partner Program, VMware. “Solutions such as ScienceLogic SL1 enable IT teams to reduce cost, increase efficiency, and create operational consistency across cloud environments. We’re excited to work with partners such as ScienceLogic to enhance native VMware Cloud on AWS capabilities and empower customers with flexibility and choice in solutions that can drive business value.”

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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 ...

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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 ...

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