
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 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 ...