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Remote monitoring and management (RMM) is a category of information technology (IT) management software that enables internal IT departments and external managed service providers (MSPs) to monitor, manage, and support endpoints such as servers, desktops, laptops, and mobile devices remotely by means of locally installed agents that can be accessed by a management service provider. [1][2] RMM software provides centralized oversight of distributed systems, automates routine maintenance, and allows proactive support without requiring technicians to be physically present at the endpoint location.[3]
RMM tools deploy lightweight agents on client devices, which communicate status and event data back to a centralized management console.[4] From this console, administrators can remotely troubleshoot issues, push updates, enforce policies, and generate performance and security reports.
Originally developed for MSPs serving small and medium-sized businesses, RMM platforms are now also used by enterprise IT teams and organizations with remote or hybrid workforces.[5]
The RMM Software as a Service (SaaS) market has grown significantly as IT outsourcing and remote work have expanded. Industry research estimated the global market at approximately USD 5.4 billion in 2024, with forecasts projecting it to exceed USD 12 billion by 2033, representing a compound annual growth rate (CAGR) of 10–15%.[8]
Adoption is especially strong among MSPs serving small and medium-sized businesses, though enterprise IT teams increasingly use RMM platforms to support geographically dispersed workforces.[9] Cloud-based deployment has become the dominant model, accounting for more than 60% of RMM usage.[10]
Regional adoption is led by North America, followed by Europe and Asia-Pacific, with growth tied to digital transformation initiatives and the increasing need for cybersecurity and compliance management.[11]
RMM solutions evolved from early network monitoring tools in the late 1990s and early 2000s, expanding to include patching, automation, and PSA integrations.[17][18] The shift to cloud computing accelerated the transition from on-premises installations to SaaS-based platforms.[19] In the 2020s, vendors began incorporating artificial intelligence (AI) and machine learning (ML) capabilities to improve anomaly detection, predictive maintenance, and automation.[20][21]
