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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.

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

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

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...