
OpsRamp announced its Summer Release which includes alert predictions for preventing outages and incidents, alert enrichment policies for faster incident troubleshooting, and auto monitoring enhancements for Alibaba Cloud and Prometheus metrics ingestion.
OpsRamp’s latest release helps IT teams avoid outages and prevent reputational damages with predictive alerting, alert enrichment, and dynamic workflows. The OpsRamp Summer 2021 Release also introduces new monitoring integrations for Alibaba Cloud, Prometheus metrics ingestion, Hitachi, VMware, Dell EMC, and Poly.
Highlights of the OpsRamp Summer 2021 Release include:
- Predictive Alerting. Alert prediction policies help IT teams anticipate which alerts repeat regularly and turn into performance-impacting incidents. With AIOps, operators can reduce service degradations by identifying seasonal alert patterns as well as lower incident volumes by forecasting repetitive alerts.
- Alert Enrichment. Organizations can accelerate incident troubleshooting by enriching the ‘problem area’ field in the alert description subject. IT operators can use regular expressions to populate alert context details so that they can identify problems faster with relevant information.
- Auto Monitoring. IT operators can now rapidly onboard and monitor their Windows infrastructure, including Windows Server, Active Directory, Exchange, IIS, and SQL Server through auto monitoring. Cloud engineers can ensure centralized data storage and retention of Prometheus metrics with support for Prometheus instances running on bare metal and virtualized infrastructure.
- Alibaba Cloud Monitoring. CloudOps engineers can now onboard and monitor their services running in Alibaba Cloud. They can visualize, alert, and perform root cause analysis on ECS instances, Auto Scaling, RDS, Load Balancer, EMR, and VPC services within Alibaba Cloud and accelerate troubleshooting for multicloud infrastructure within a single platform.
- Datacenter Monitoring. System administrators can now monitor the performance and health of popular datacenter infrastructure such as Hitachi VSP OpsCenter, NAS and HCI, VMware vSAN, NSX-T and NSX-V, Dell EMC PowerScale, PowerStore and PowerMax, and Poly Trio, VVX/CCX and Group.
- Dynamic Workflows (Beta). Instead of building a number of different automation workflows, IT operators can maintain a single decision table to address specific operational scenarios at scale. Dynamic workflows ensure faster incident response by invoking diagnostic actions for distinct scenarios.
- Mobile Application. IT teams can now respond to alerts and incidents through the OpsRamp mobile application with support for both Android and iOS devices. Operators can view, sort, search, filter, comment, and take action on alerts while also being able to access, edit, sort, filter, and reassign incidents from anywhere.
- Powerful Visualizations. Operators can now clearly visualize metric values that can arbitrarily increase or decrease within a fixed range using Gauge charts. For network operations teams that work in 24/7 shifts, dark mode reduces eye strain, improves readability, and offers ergonomic comfort.
OpsRamp Dark Mode reduces blue-light fatigue for ops teams that troubleshoot during late hours or are accustomed to darkened UIs.
“Modern IT teams have to deal with escalating customer expectations, constant toil, technical debt, and an overwhelming amount of operational data to process and analyze,” said Sheen Khoury, Chief Revenue Officer at OpsRamp. “OpsRamp’s digital operations management platform transforms reactive incidents workflows to proactive and preventive operations for faster incident prediction, recognition, and remediation.”
The Latest
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...
In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...
Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...
Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...