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OpsClarity Introduces Automated Discovery and Monitoring for Containerized Applications

OpsClarity introduced an automated performance monitoring solution for dynamic and containerized applications running in either private data centers or on public clouds.

OpsClarity provides a novel and scalable approach to automatically track, discover and monitor dynamic infrastructure and containers. The solution is devoid of cumbersome and static configuration files. It discovers the containers and the associated services in real-time, collecting operational and performance metrics and providing operational intelligence and alerts.

Modern applications are comprised of stateless microservices paired with stateful data services. They run inside containers on shared or cloud infrastructure. They leverage message brokers, stream processors, NoSQL stores and resource managers to power business critical data-driven applications. While these modern application architectures offer unprecedented levels of flexibility, scale and agility, they also introduce complexity and change. This results in operational and manageability challenges. OpsClarity tracks them in real-time using completely automated infrastructure and service discovery. It keeps up with the dynamic applications, and provides deep metric driven operational insights – all without relying on static and ageing configuration files.

“As enterprises switch their business critical applications to architectures based on microservices and modern data services that are deployed on shared containerized infrastructure, it becomes essential to efficiently manage operations at scale,” said Alan Ngai, CTO and co-founder of OpsClarity. “You need a next generation solution that can automatically adapt to and understand dynamic application environments and proactively surface actionable insights. We are leveraging our roots in data science and large-scale streaming analytics to build a smart solution that brings new levels of visibility, focus, and productivity to modern software operations.”

OpsClarity monitors containerized microservices, application and data services across thousands of servers, VMs or cloud instances. The solution can continue to monitor even as microservices and data services are constantly changing or being moved around by resource managers like Mesos, Kubernetes and Docker Swarm. In addition to being easy to install either on-premise or on public cloud infrastructure, OpsClarity provides DevOps team with immediate visibility and insights from Day 1. This leads to operational improvements such as high availability, easy scalability and zero-downtime.

OpsClarity provides the following benefits for modern containerized applications:

- Automatic discovery for applications/services and containerized environments: OpsClarity automatically discovers dynamic infrastructure and application components and code changes based on process signatures, network connections and code change events. It applies advanced data science and artificial intelligence to accurately identify components, collect metrics and configure monitoring. This not only removes tedious, time-consuming configuration, but also provides DevOps engineers with real-time, up to date visibility of their applications and infrastructure.

- Zero-friction monitoring for dockerized services: Monitoring services can be complicated depending on whether the application placement inside the container is static or dynamic and how the respective ports are mapped. Rather than manually updating monitoring configuration to map internal docker ports with external host ports, which are constantly changing, OpsClarity completely automates this repetitive task.

- Docker container metrics: OpsClarity collects all the system level metrics for Docker, including CPU, memory, network, I/O usage and provides aggregations based on automatic tagging across applications, services, regions, containers, hosts and logical constructs like data pipelines.

- Correlation of metrics and concerns across applications, services, containers and hosts: OpsClarity automatically correlates metrics and provides a top down view of the health of applications and allows infinite drill down from applications to the services, containers and hosts, in that order, for logical and quicker analysis. This is all automatically configured and visualized.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

OpsClarity Introduces Automated Discovery and Monitoring for Containerized Applications

OpsClarity introduced an automated performance monitoring solution for dynamic and containerized applications running in either private data centers or on public clouds.

OpsClarity provides a novel and scalable approach to automatically track, discover and monitor dynamic infrastructure and containers. The solution is devoid of cumbersome and static configuration files. It discovers the containers and the associated services in real-time, collecting operational and performance metrics and providing operational intelligence and alerts.

Modern applications are comprised of stateless microservices paired with stateful data services. They run inside containers on shared or cloud infrastructure. They leverage message brokers, stream processors, NoSQL stores and resource managers to power business critical data-driven applications. While these modern application architectures offer unprecedented levels of flexibility, scale and agility, they also introduce complexity and change. This results in operational and manageability challenges. OpsClarity tracks them in real-time using completely automated infrastructure and service discovery. It keeps up with the dynamic applications, and provides deep metric driven operational insights – all without relying on static and ageing configuration files.

“As enterprises switch their business critical applications to architectures based on microservices and modern data services that are deployed on shared containerized infrastructure, it becomes essential to efficiently manage operations at scale,” said Alan Ngai, CTO and co-founder of OpsClarity. “You need a next generation solution that can automatically adapt to and understand dynamic application environments and proactively surface actionable insights. We are leveraging our roots in data science and large-scale streaming analytics to build a smart solution that brings new levels of visibility, focus, and productivity to modern software operations.”

OpsClarity monitors containerized microservices, application and data services across thousands of servers, VMs or cloud instances. The solution can continue to monitor even as microservices and data services are constantly changing or being moved around by resource managers like Mesos, Kubernetes and Docker Swarm. In addition to being easy to install either on-premise or on public cloud infrastructure, OpsClarity provides DevOps team with immediate visibility and insights from Day 1. This leads to operational improvements such as high availability, easy scalability and zero-downtime.

OpsClarity provides the following benefits for modern containerized applications:

- Automatic discovery for applications/services and containerized environments: OpsClarity automatically discovers dynamic infrastructure and application components and code changes based on process signatures, network connections and code change events. It applies advanced data science and artificial intelligence to accurately identify components, collect metrics and configure monitoring. This not only removes tedious, time-consuming configuration, but also provides DevOps engineers with real-time, up to date visibility of their applications and infrastructure.

- Zero-friction monitoring for dockerized services: Monitoring services can be complicated depending on whether the application placement inside the container is static or dynamic and how the respective ports are mapped. Rather than manually updating monitoring configuration to map internal docker ports with external host ports, which are constantly changing, OpsClarity completely automates this repetitive task.

- Docker container metrics: OpsClarity collects all the system level metrics for Docker, including CPU, memory, network, I/O usage and provides aggregations based on automatic tagging across applications, services, regions, containers, hosts and logical constructs like data pipelines.

- Correlation of metrics and concerns across applications, services, containers and hosts: OpsClarity automatically correlates metrics and provides a top down view of the health of applications and allows infinite drill down from applications to the services, containers and hosts, in that order, for logical and quicker analysis. This is all automatically configured and visualized.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.