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Epsagon Introduces Agentless Monitoring for Cloud Applications

Epsagon, a microservices application monitoring company, officially unveiled its automated, agentless platform for any cloud application.

Originally built to support serverless applications on AWS, this release now enables DevOps and engineering teams to quickly troubleshoot, monitor, and visualize their cloud applications across any kind of microservice such as serverless frameworks, containers or Kubernetes. Epsagon's technology is fully automated and was built for modern environments where the host may not be accessible, which makes traditional monitoring agents obsolete.

Epsagon's technology combines automated distributed tracing and logging with no manual code instrumentation required. The product is suitable for container-based services which run on a VM or use Kubernetes, serverless services such as AWS Lambda, and modern orchestration services such as a Managed Kubernetes service or AWS Fargate.

"We're seeing a seismic shift in microservices with DevOps and engineering teams moving their workloads into multiple architectures like containers, serverless frameworks, and Kubernetes," said Nitzan Shapira, CEO and Co-founder, Epsagon. "But current microservices environments are a black box and fail to provide these teams with visibility into their applications in production. The traditional monitoring and logging solutions aren't built to handle these modern architectures, forcing teams to spend resources building in-house solutions just to keep development moving forward. That's precisely why we've built an automated monitoring and logging solution: to provide a zero-touch, low-maintenance approach to troubleshooting, monitoring, and visualizing cloud applications."

Shapira adds, "We started with automated monitoring for serverless and AWS Lambda-based applications. Our customers have gained great value from our serverless capabilities, and they kept asking for an extended, more generalized solution, which will bring the troubleshooting and monitoring capabilities of Epsagon to their cloud-based, microservices environments as well."

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Epsagon Introduces Agentless Monitoring for Cloud Applications

Epsagon, a microservices application monitoring company, officially unveiled its automated, agentless platform for any cloud application.

Originally built to support serverless applications on AWS, this release now enables DevOps and engineering teams to quickly troubleshoot, monitor, and visualize their cloud applications across any kind of microservice such as serverless frameworks, containers or Kubernetes. Epsagon's technology is fully automated and was built for modern environments where the host may not be accessible, which makes traditional monitoring agents obsolete.

Epsagon's technology combines automated distributed tracing and logging with no manual code instrumentation required. The product is suitable for container-based services which run on a VM or use Kubernetes, serverless services such as AWS Lambda, and modern orchestration services such as a Managed Kubernetes service or AWS Fargate.

"We're seeing a seismic shift in microservices with DevOps and engineering teams moving their workloads into multiple architectures like containers, serverless frameworks, and Kubernetes," said Nitzan Shapira, CEO and Co-founder, Epsagon. "But current microservices environments are a black box and fail to provide these teams with visibility into their applications in production. The traditional monitoring and logging solutions aren't built to handle these modern architectures, forcing teams to spend resources building in-house solutions just to keep development moving forward. That's precisely why we've built an automated monitoring and logging solution: to provide a zero-touch, low-maintenance approach to troubleshooting, monitoring, and visualizing cloud applications."

Shapira adds, "We started with automated monitoring for serverless and AWS Lambda-based applications. Our customers have gained great value from our serverless capabilities, and they kept asking for an extended, more generalized solution, which will bring the troubleshooting and monitoring capabilities of Epsagon to their cloud-based, microservices environments as well."

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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 ...

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 ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...