LogicMonitor vs Nagios: What are the differences?
LogicMonitor: SaaS-based, automated IT performance monitoring platform for On-Premise, Hybrid, and Cloud infrastructures. LogicMonitor provides the end-to-end visibility needed to maintain the performance and availability of business applications. It leverages automation and built-in intelligence to monitor today's complex and distributed infrastructures; Nagios: Complete monitoring and alerting for servers, switches, applications, and services. Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
LogicMonitor belongs to "Performance Monitoring" category of the tech stack, while Nagios can be primarily classified under "Monitoring Tools".
Some of the features offered by LogicMonitor are:
- Full Stack Performance Monitoring
- Pre-configured monitoring, with built-in alert thresholds, for 1000+ technologies
- Works with any Deployment Model: On-prem, Hybrid, & Hybrid Cloud
On the other hand, Nagios provides the following key features:
- Monitor your entire IT infrastructure
- Spot problems before they occur
- Know immediately when problems arise
"Auto discovery" is the top reason why over 2 developers like LogicMonitor, while over 49 developers mention "It just works" as the leading cause for choosing Nagios.
Nagios is an open source tool with 60 GitHub stars and 36 GitHub forks. Here's a link to Nagios's open source repository on GitHub.
What is LogicMonitor?
What is Nagios?
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Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:
By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.
To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...
(GitHub : https://github.com/m3db/m3)
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We use Nagios to monitor customer instances of Bridge and proactively alert us about issues like queue sizes, downed services, errors in logs, etc.
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