Exoprise announced the availability of a new set of cloud and network health sensors.
The new offerings extend the company’s existing CloudReady Monitor solution by enabling IT teams to monitor the performance and availability of nearly any website or cloud service in real time and identify issues before they impact users.
Exoprise CloudReady Monitor provides IT teams with real-time performance and availability insight into their mission-critical cloud apps and services including Microsoft Office 365,Google Apps and Salesforce.com. It enables IT to analyze measurements from their locations against global and regional crowd data, helping teams quickly identify and fix performance-impacting issues regardless of whether they happen in their network, at their ISP, or in the cloud. CloudReady Monitor’s sophisticated synthetic transaction monitoring technology emulates real user actions across a variety of network protocols and cloud service API’s, without the complex agent scripting required with other solutions.
With the new CloudReady cloud and network health sensors, IT teams can test and compare response times for multiple network services across multiple locations, compare local response times to crowd averages and set alarms for immediate notification in the event of errors or abnormal response times. The new sensors’ address some of the most common and challenging web performance issues for IT Teams today and include:
- Azure & AWS Bandwidth Monitoring: Any organization running apps and services on Amazon Web Services or Microsoft Azure needs to ensure the consistent health and capacity of the network connections between their users to them. CloudReady Bandwidth Sensors continuously measure end-to-end bandwidth between user access locations and selected Amazon or Microsoft datacenters, enabling IT to detect changes or problems in available bandwidth that can be caused by local gateway issues, problems at the ISP, or virtual network configuration problems at the IaaS provider.
- DNS Monitoring: Problems with DNS can make it impossible for users to access services outside an organization’s network. And due to distribution and caching characteristics, IT teams need an easy way to test cloud service DNS entries – for both their own sites and external services – from all locations where users access them. CloudReady DNS Sensors make it easy to monitor, analyze, and troubleshoot propagation, configuration and security issues in real time - across all locations and against crowd performance averages.
- Remote Server Monitoring: IT teams need to be alerted when specific servers are slow to respond or stop responding altogether. CloudReady Ping Sensors monitor both the internal and external servers that are critical to users, capturing response times for any server supporting ICMP echo requests. And unlike manual command line ping tests, CloudReady continuously monitors target servers, providing real-time alarms as well as historical trend and cross-site comparison analysis.
- Web Site Availability Monitoring: For some sites and services you only need to monitor the availability and responsiveness at the application network layer (HTTP) without needing to perform detailed page rendering performance diagnostics. CloudReady WGet Sensors allow you to compare HTTP page delivery timings across multiple servers in a single view. This is useful in evaluating relative performance across a farm of web servers as well as being able to compare web page access times across your different locations.
- Web App Performance Monitoring: Although web apps appear to users as a single application, their performance depends on multiple services that are meshed together within the user’s browser. If one of these services is unresponsive, the user may lose the ability to use entirely. CloudReady WMon Sensors allow IT to detect and pinpoint the specific problem service(s) impacting overall app performance, with visibility into performance history data for each individual service.
The new CloudReady Cloud and Network Health sensors are available immediately through Exoprise and its valued-added reseller partners.
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...