
ManageEngine announced the launch of RMM Central.
Available immediately, RMM Central helps discover networks, monitor device performance, detect and manage faults, deploy missing patches, enable remote access and provide a complete overview of all hardware and software inventory.
Service providers need to understand their clients' IT networks and automate everyday tasks like adhering to specific security policies, meeting client service-level agreements (SLAs), generating specific reports and managing their billing. However, juggling multiple tools to monitor network performance and manage devices is time-consuming. With network management complexities and security threats increasing exponentially, MSPs need a single solution that can offer high scalability, security, quick setup, easy navigation, holistic features, seamless client onboarding and affordable pricing.
"Having worked with the MSP market for more than 10 years, ManageEngine is committed to this market and understands the demand for a unified IT management solution," said Mathivanan Venkatachalam, VP of ManageEngine. "To meet this demand, we've developed RMM Central by combining the capabilities from different products to offer holistic features, and we plan to launch similar products down the road. Using these solutions, service providers can optimize productivity with operational efficiency and provide exceptional service to their clients."
"RMM Central is an all-powerful, comprehensive IT management companion for MSPs to manage and monitor multiple client accounts, domains and networks from a unified, easy-to-use console," said Boobala Krishnan S, Solutions Delivery Manager at Soft Solutions, a ManageEngine partner based in New Zealand with early access to RMM Central.
With the launch of RMM Central, ManageEngine provides MSPs with a single tool that delivers 360-degree visibility into all managed client networks. RMM Central's highlights include:
- Seamless network performance monitoring: Identify and fix performance issues like faults, alarms and outages before they become significant problems and cause downtime.
- Automated, proactive IT security: Detect and deploy missing patches, make fault management foolproof, customize security policies and implement configurations for each client network to fortify them.
- Centralized asset management: Manage all hardware and software assets, meter software usage, manage software licenses as well as detect and blacklist malicious applications across all managed devices.
- Instant remote troubleshooting: Remotely connect to devices, coordinate with end users on voice and video calls during a troubleshooting session and collaborate with multiple technicians simultaneously to achieve SLAs faster.
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 ...
