Skip to main content

ManageEngine OpManager Nexus Introduces Real-Time Network Observability with OpenConfig and gNMI

Newly Introduced Capabilities Bring High-Fidelity Streaming Telemetry, Vendor-Neutral Visibility, and Unified Network Intelligence Onto a Single Platform

ManageEngine, a division of Zoho Corporation, announced the addition of native support for gNMI and OpenConfig streaming telemetry to its observability platform, OpManager Nexus. 

The capability enables enterprises to collect real-time, push-based network data directly from infrastructure without additional agents or external collectors. This gives network and operations teams the real-time visibility they need to act before services are affected.

In large enterprise environments, the SNMP-dependent visibility model is inherently constrained because the trade-off between the collection frequency and infrastructure overhead limits both the depth and reliability of insights. Streaming telemetry with gNMI and OpenConfig shifts this model to continuous, accurate visibility, making it the new industry objective. OpManager Nexus aligns with this shift, delivering real-time, end-to-end network intelligence at scale.

"gNMI and OpenConfig are increasingly becoming the preferred way to expose that data because they provide deeper visibility while placing less strain on network devices. With native integration in OpManager Nexus, customers can operationalize streaming telemetry within the platform they already trust, without adding new operational complexity," said Gowrisankar Chinnayan, head of product management at ManageEngine.

Built for Scalability, Resilience, and Vendor-Neutrality

Most enterprise network monitoring systems still depend on SNMP polling at multi-minute intervals, leaving network teams blind to transient congestion, BGP flaps, and other short-lived events that directly affect application performance. To work around these gaps, teams have been forced to stitch together data from multiple open-source collectors, time series databases, and dashboards just to approximate basic telemetry workflows. This results in issues like slower root cause analysis and low-resolution data that no longer reflects how modern networks actually behave.

OpManager Nexus’ new telemetry capability is built natively on gNMI streaming with OpenConfig support, enabling scalable, vendor-agnostic network monitoring. A single vendor-neutral integration layer normalizes telemetry across all supported devices, with every metric seamlessly connected to the existing topology, alerting, and reporting workflows without the need for additional tools.

Closing the Gaps of Traditional Monitoring

Beyond visibility, the architecture is designed to advance a more sustainable model for network observability. By replacing poll-heavy collection with event-driven streaming, it significantly reduces redundant data cycles, lowers compute and storage overhead, and minimizes the network load. This shift not only improves efficiency at scale but also aligns monitoring operations with broader energy optimization and sustainability objectives. The implementation also supports TLS-encrypted transport across both dial-in and dial-out modes, ensuring secure, high-fidelity telemetry.

"We started looking at streaming telemetry after [we had] a series of intermittent network slowdowns that never showed up in our polling data. By the time alerts came in, the actual spike was already gone. What stood out with OpManager Nexus was that we could finally see those short-lived events in real time without pushing our monitoring infrastructure harder or bringing in another telemetry tool," said Vikas Yadav, senior IT administrator at WAISL Limited.

Key Capabilities and Highlights

  • Secure gNMI dial-in and dial-out telemetry: Native gNMI dial-in and dial-out modes with TLS encryption for secure telemetry transport across both centralized and distributed deployments
  • Consistent multi-vendor data with OpenConfig YANG: Full OpenConfig YANG model support across major vendors, enabling consistent data structures and path definitions in multi-vendor environments
  • Sub-second telemetry for real-time visibility: Sub-second telemetry that provides near-instant visibility into key metrics, capturing events that polling misses, reducing the detection time, and avoiding added monitoring overhead
  • Unified SNMP, NetFlow, and gNMI collection: Unified collection of SNMP, NetFlow, and gNMI telemetry on a single platform, removing the need for parallel monitoring stacks
  • Native OpManager Nexus integration: Direct integration with the existing OpManager Nexus topology, alerts, and reporting with no separate dashboards or new tools to learn

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

ManageEngine OpManager Nexus Introduces Real-Time Network Observability with OpenConfig and gNMI

Newly Introduced Capabilities Bring High-Fidelity Streaming Telemetry, Vendor-Neutral Visibility, and Unified Network Intelligence Onto a Single Platform

ManageEngine, a division of Zoho Corporation, announced the addition of native support for gNMI and OpenConfig streaming telemetry to its observability platform, OpManager Nexus. 

The capability enables enterprises to collect real-time, push-based network data directly from infrastructure without additional agents or external collectors. This gives network and operations teams the real-time visibility they need to act before services are affected.

In large enterprise environments, the SNMP-dependent visibility model is inherently constrained because the trade-off between the collection frequency and infrastructure overhead limits both the depth and reliability of insights. Streaming telemetry with gNMI and OpenConfig shifts this model to continuous, accurate visibility, making it the new industry objective. OpManager Nexus aligns with this shift, delivering real-time, end-to-end network intelligence at scale.

"gNMI and OpenConfig are increasingly becoming the preferred way to expose that data because they provide deeper visibility while placing less strain on network devices. With native integration in OpManager Nexus, customers can operationalize streaming telemetry within the platform they already trust, without adding new operational complexity," said Gowrisankar Chinnayan, head of product management at ManageEngine.

Built for Scalability, Resilience, and Vendor-Neutrality

Most enterprise network monitoring systems still depend on SNMP polling at multi-minute intervals, leaving network teams blind to transient congestion, BGP flaps, and other short-lived events that directly affect application performance. To work around these gaps, teams have been forced to stitch together data from multiple open-source collectors, time series databases, and dashboards just to approximate basic telemetry workflows. This results in issues like slower root cause analysis and low-resolution data that no longer reflects how modern networks actually behave.

OpManager Nexus’ new telemetry capability is built natively on gNMI streaming with OpenConfig support, enabling scalable, vendor-agnostic network monitoring. A single vendor-neutral integration layer normalizes telemetry across all supported devices, with every metric seamlessly connected to the existing topology, alerting, and reporting workflows without the need for additional tools.

Closing the Gaps of Traditional Monitoring

Beyond visibility, the architecture is designed to advance a more sustainable model for network observability. By replacing poll-heavy collection with event-driven streaming, it significantly reduces redundant data cycles, lowers compute and storage overhead, and minimizes the network load. This shift not only improves efficiency at scale but also aligns monitoring operations with broader energy optimization and sustainability objectives. The implementation also supports TLS-encrypted transport across both dial-in and dial-out modes, ensuring secure, high-fidelity telemetry.

"We started looking at streaming telemetry after [we had] a series of intermittent network slowdowns that never showed up in our polling data. By the time alerts came in, the actual spike was already gone. What stood out with OpManager Nexus was that we could finally see those short-lived events in real time without pushing our monitoring infrastructure harder or bringing in another telemetry tool," said Vikas Yadav, senior IT administrator at WAISL Limited.

Key Capabilities and Highlights

  • Secure gNMI dial-in and dial-out telemetry: Native gNMI dial-in and dial-out modes with TLS encryption for secure telemetry transport across both centralized and distributed deployments
  • Consistent multi-vendor data with OpenConfig YANG: Full OpenConfig YANG model support across major vendors, enabling consistent data structures and path definitions in multi-vendor environments
  • Sub-second telemetry for real-time visibility: Sub-second telemetry that provides near-instant visibility into key metrics, capturing events that polling misses, reducing the detection time, and avoiding added monitoring overhead
  • Unified SNMP, NetFlow, and gNMI collection: Unified collection of SNMP, NetFlow, and gNMI telemetry on a single platform, removing the need for parallel monitoring stacks
  • Native OpManager Nexus integration: Direct integration with the existing OpManager Nexus topology, alerts, and reporting with no separate dashboards or new tools to learn

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...