OpsRamp announced new topology maps, enhanced artificial intelligence for IT operations (AIOps) features and new monitoring capabilities for cloud native workloads.
The OpsRamp Winter Release provides greater service-centricity and context for hybrid infrastructure monitoring and management allowing enterprise IT teams to embrace more intelligent incident management and deliver exceptional customer experiences.
Key highlights of the OpsRamp Winter Release include:
Impact Visibility and Service Context: OpsRamp helps digital operations teams drive resilient and responsive IT services by discovering topological relationships between resources at multiple levels in the increasingly hybrid and multi-cloud IT stack. OpsRamp’s topology maps enable infrastructure and operations teams to understand the impact that IT resources have on each other and on end-user facing IT services. OpsRamp’s topology discovery now includes:
- Application Topology. OpsRamp discovers more than forty popular enterprise applications and establishes topological relationships between application components and infrastructure.
- Hypervisor Topology. OpsRamp discovers virtual machines, hypervisor servers and clusters in VMware vSphere and KVM environments and their relationships.
- Enhanced Service Maps. Representing logical IT services, OpsRamp service maps now have a new user interface that makes it easy to identify underlying resources behind an IT service outage so that operations teams can hone in on the right course of action to restore services.
AIOps for Proactive IT Operations: The Winter Release introduces new capabilities for OpsRamp OpsQ, the intelligent event management engine for alert correlation, automation, and remediation. New features include:
- Auto-Incident Creation and Routing. The biggest priority during a major outage is to assign and route incidents to the right on-call teams. OpsRamp OpsQ now enables automatic incident creation and routing using alert escalation policies to auto-assign incidents based on prior alert, incident, and notification data. Machine learning-driven alert escalation uses specific learned patterns (assignee groups, business impact, urgency, and priority) to route incident assignments for different types of alerts.
- Augmented Training for Inference Models. OpsRamp’s machine learning-based inference models correlate alerts linked by a common cause using historical alert data. Opsramp’s OpsQ now allows users to augment these models with additional user-provided training data. With such augmented training, IT operations teams can bootstrap OpsQ to recognize alert sequences that are uncommon in everyday operations, but important to identify when they occur.
- Frequency-Driven Alert Escalation. OpsQ now supports policies to escalate alerts based on how often an alert has recently occurred. With frequency-based alerting, operations teams can filter out alerts that flap only occasionally and escalate alerts that flap repeatedly.
Cloud Native Monitoring and Event Management. The OpsRamp platform provides comprehensive capabilities for multi-cloud event monitoring as well as features to discover and monitor container infrastructure supporting modern microservices architectures.
- Cloud Native Monitoring. Enterprises are increasingly adopting cloud native technologies like Docker containers and Kubernetes container orchestration for faster time-to-market. OpsRamp now discovers and monitors Kubernetes environments across on-prem and cloud services like Azure Kubernetes Services, Google Kubernetes Engine, and Amazon Elastic Container Service for Kubernetes. DevOps teams can understand the total services (nodes and containers for each cluster, a breakdown of pods by namespace) and resource trends (CPU and memory utilization) for each Kubernetes cluster.
- Cloud Event Monitoring. Events are a key medium of communication for operational issues in the public cloud. Given that events are a primary source of signal in multi-cloud environments, OpsRamp can now collect, aggregate, correlate and escalate events from AWS services such as AWS Health, ECS, Redshift, Data Migration Services, and CloudWatch. With this capability, OpsRamp serves as a single point of monitoring, management, and remediation for cloud events across multiple cloud accounts.
“Our Winter Release will support digital operations teams on their journey to cloud and cloud-native architectures with enhanced AIOps capabilities for reduced mean-time-to-resolution,” said Mahesh Ramachandran, VP of Product Management, OpsRamp. “We want to keep IT infrastructure teams focused on offering the best customer experiences and delivering value back to the business.”
The OpsRamp Winter Release also includes new patch management capabilities for patch compliance verification, synthetic transaction and SSL certificate monitoring, new integrations for monitoring open source applications, and knowledge base enhancements for easier categorization and linking.
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.