OpsRamp announced OpsQ Recommend Mode, a capability for first-response and incident remediation, as part of the OpsRamp Winter 2020 Release.
OpsQ Recommend Mode lets digital operations teams use predictive analytics to reduce mean-time-to-resolution (MTTR).
Other artificial intelligence for IT operations (AIOps) innovations in the release include visualization of alert similarity patterns and new alert stats widgets to provide transparency into machine learning-driven decisions.
The OpsRamp Winter 2020 Release also introduces 19 new cloud monitoring integrations for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), along with dynamic topology maps for Azure and GCP. Highlights of the Winter 2020 Release include:
AIOps: OpsQ is OpsRamp’s intelligent event management, alert correlation, and remediation engine. New AIOps capabilities help IT operations teams ingest, analyze, and extract comprehensive insights for real-time event and incident management:
- OpsQ Recommend Mode. The Winter 2020 Release introduces the OpQ Bot and a new Recommend Mode for alert escalation policies so that IT teams can drive faster incident response with auto-suggested actions. OpsQ Recommend Mode lets IT teams stay in control by using explainable and transparent analytical recommendations for first-response and incident creation.
- Visualization of Alert Seasonality Patterns. Many alerts in IT environments recur at a predictable frequency. OpsRamp OpsQ can learn such seasonality patterns and automatically suppress these recurring alerts. With this release, IT teams can visualize seasonality patterns that OpsQ has learned. This visibility helps IT teams understand the auto-suppress decisions that OpsQ makes and trace recurring alert patterns to underlying IT activity.
- Alert Stats Widget. The Alert Stats widget shows the total number of raw events, correlated alerts, inference alerts, auto-ticketed alerts, and auto-suppressed alerts handled by the OpsQ event management engine. This widget shows how OpsRamp OpsQ reduces event volume at each stage so that IT teams can build more confidence in machine learning-based techniques for alert optimization.
Multi-Cloud Monitoring and Management: OpsRamp currently offers 120+ integrations across leading cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The OpsRamp Winter 2020 Release drives full-stack visibility for multi-cloud workloads with 19 new cloud monitoring integrations as well as dynamic topology maps for Azure and GCP:
- Deeper Cloud Monitoring. OpsRamp adds monitoring support for 4 AWS, 7 Azure, and 8 GCP cloud services:
AWS – Transit Gateway, AppSync, CloudSearch, and DocumentDB
Azure – Application Insights, Traffic Manager, Virtual Network, Route Table, Virtual Machine Scale Sets, SQL Elastic Pool, and Service Bus
GCP – Cloud BigTable, Cloud Composer, Cloud Filestore, Firebase, Cloud Memorystore for Redis, Cloud Run, Cloud TPU, and Cloud Tasks
- Cloud Topology Maps. In addition to AWS cloud topology maps, OpsRamp now offers automated topology discovery and mapping for Azure and GCP. IT teams can apply cloud topology maps to analyze the impact of changes in their multi-cloud environments. Cloud topology is also applied in OpsQ’s event correlation engine to increase the accuracy of machine learning models.
Hybrid Discovery and Synthetic Monitoring: The OpsRamp Winter 2020 Release introduces new platform capabilities for agentless discovery and synthetic monitoring:
- Agentless Discovery and Monitoring for Windows Servers. While OpsRamp offers agentless discovery for Linux and VMware compute, network, and storage resources, the Winter 2020 Release introduces agentless discovery and monitoring for Windows compute resources. Enterprises with remote offices can manage their distributed Windows infrastructure in a secure and frictionless way with agentless monitoring.
- Synthetic Monitoring. OpsRamp’s enhanced synthetic monitoring provides deeper insights and analysis for troubleshooting multi-step transactions. Application owners can break down each synthetic transaction and gain visibility into the performance of each step in a web transaction.
“While machine learning models for IT operations have generated considerable excitement among technology decision-makers, customers would like to understand how a black box model works before ceding control, ” said Bhanu Singh, SVP of Engineering and DevOps at OpsRamp. “The Winter 2020 Release ensures greater transparency of machine learning models for intelligent event and incident management, along with enhanced monitoring capabilities for leading public cloud platforms.”
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.