Skip to main content

OpsRamp Launches OpsQ

OpsRamp announced the launch of OpsRamp OpsQ, an intelligent event management, alert correlation, and remediation solution for the hybrid enterprise.

IT operations teams can optimize and automate routine tasks with context and insight at scale by understanding the business impact of an IT issue and ensure rapid service restoration. OpsRamp OpsQ introduces machine learning inference models that learn how frequently specific alert sequences occur and recognize events correlated to the same cause.

With OpsQ, IT operations teams can analyze IT event streams in real-time, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection. OpsQ displays the critical root-cause alerts from native instrumentation and third-party event streams, suppresses non-emergency alerts, escalates critical events, and integrates with ITSM tools for faster remediation.

OpsRamp OpsQ includes several features to drive greater efficiency within modern IT operational environments, including:

- Inference Models. Enjoy a richer, deeper and more contextual view of your IT incidents using statistical evidence and reasoning with three inference models today:
o Topology-based correlation helps you visualize the upstream and downstream resources that comprise an IT service.
o Clustering-based correlation lets you create rules to correlate events based on their attributes.
o Co-occurrence-based correlation automatically recognizes alerts that are related by the same cause.

- Intelligent Alerting. Proactively identify service disruptions with forecasting and change detection alerts. Detect potential triggers and then speed incident detection by dramatically reducing the volume of alerts, so that IT teams can quickly focus on restoring the IT services that matter the most to the business.

- Alert Correlation. Surface accurate insights for problem isolation by ingesting, understanding and organizing alerts across dynamic and distributed IT environments.

- Alert Escalation. Drive faster mean-time-to-acknowledgement with smart and tailored notifications based on first-responder communication preferences (email, text, voice and chat) for faster reaction times.

- Auto-Incident Routing. Automatically send incidents to an appropriate team for rapid incident response using on-call schedules for your global NOC teams.

- Auto Remediation. Speed execution for incident remediation with automation policies that reduce administrative effort.

“Our service-centric AIOps platform represents a fundamental transformation in how IT operations teams maintain business services and deliver exceptional customer experiences,” said Bhanu Singh,VP of Product Development and Operations for OpsRamp. “OpsQ helps enterprises and managed service providers handle previously unmanageable alert volumes, while OpsRamp’s service and topology maps let you visualize overall business-service health. Together, they’re a modern solution for IT monitoring and management in the hybrid, multi-cloud world.”

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

OpsRamp Launches OpsQ

OpsRamp announced the launch of OpsRamp OpsQ, an intelligent event management, alert correlation, and remediation solution for the hybrid enterprise.

IT operations teams can optimize and automate routine tasks with context and insight at scale by understanding the business impact of an IT issue and ensure rapid service restoration. OpsRamp OpsQ introduces machine learning inference models that learn how frequently specific alert sequences occur and recognize events correlated to the same cause.

With OpsQ, IT operations teams can analyze IT event streams in real-time, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection. OpsQ displays the critical root-cause alerts from native instrumentation and third-party event streams, suppresses non-emergency alerts, escalates critical events, and integrates with ITSM tools for faster remediation.

OpsRamp OpsQ includes several features to drive greater efficiency within modern IT operational environments, including:

- Inference Models. Enjoy a richer, deeper and more contextual view of your IT incidents using statistical evidence and reasoning with three inference models today:
o Topology-based correlation helps you visualize the upstream and downstream resources that comprise an IT service.
o Clustering-based correlation lets you create rules to correlate events based on their attributes.
o Co-occurrence-based correlation automatically recognizes alerts that are related by the same cause.

- Intelligent Alerting. Proactively identify service disruptions with forecasting and change detection alerts. Detect potential triggers and then speed incident detection by dramatically reducing the volume of alerts, so that IT teams can quickly focus on restoring the IT services that matter the most to the business.

- Alert Correlation. Surface accurate insights for problem isolation by ingesting, understanding and organizing alerts across dynamic and distributed IT environments.

- Alert Escalation. Drive faster mean-time-to-acknowledgement with smart and tailored notifications based on first-responder communication preferences (email, text, voice and chat) for faster reaction times.

- Auto-Incident Routing. Automatically send incidents to an appropriate team for rapid incident response using on-call schedules for your global NOC teams.

- Auto Remediation. Speed execution for incident remediation with automation policies that reduce administrative effort.

“Our service-centric AIOps platform represents a fundamental transformation in how IT operations teams maintain business services and deliver exceptional customer experiences,” said Bhanu Singh,VP of Product Development and Operations for OpsRamp. “OpsQ helps enterprises and managed service providers handle previously unmanageable alert volumes, while OpsRamp’s service and topology maps let you visualize overall business-service health. Together, they’re a modern solution for IT monitoring and management in the hybrid, multi-cloud world.”

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...