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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Monitoring Tools
  5. Cacti vs Nagios vs Sensu

Cacti vs Nagios vs Sensu

OverviewDecisionsComparisonAlternatives

Overview

Nagios
Nagios
Stacks811
Followers1.1K
Votes102
GitHub Stars57
Forks38
Cacti
Cacti
Stacks89
Followers202
Votes10
Sensu
Sensu
Stacks201
Followers251
Votes56
GitHub Stars2.9K
Forks386

Cacti vs Nagios vs Sensu: What are the differences?

**Introduction:**

Key differences between Cacti, Nagios, and Sensu:

1. **Functionality**: Cacti is primarily a graphing solution for time-series data providing real-time graphs, while Nagios focuses on monitoring network services and components and alerting if any issues are detected. Sensu offers a more comprehensive monitoring solution with event handling, targeted at cloud-scale infrastructures and dynamic environments.

2. **Alerting Mechanism**: Nagios uses a traditional alerting mechanism where notifications are sent directly based on predefined thresholds. Cacti, on the other hand, lacks built-in alerting capabilities and requires integration with external tools for alerting. Sensu allows for more customizable alerting using its event handler infrastructure, enabling users to define complex alerting logic.

3. **Scalability**: Cacti is more suitable for smaller environments with simpler monitoring needs due to its limitations in scalability compared to Nagios and Sensu. Nagios can easily scale to monitor large and complex networks with distributed monitoring setups. Sensu is designed to be highly scalable, supporting auto-discovery and flexible deployment options for dynamic environments.

4. **Agent-based vs. Agentless**: Nagios and Sensu are agent-based monitoring solutions, where agents are deployed on each host to collect data and send it back to the central monitoring server. In contrast, Cacti is agentless and relies on SNMP (Simple Network Management Protocol) for data collection, making it easier to set up for some users but limiting the depth of data that can be gathered.

5. **Community and Support**: Nagios has a large and active community with extensive documentation and plugins available, making it easier to find solutions and integrations. Cacti also has a good community but may have fewer resources compared to Nagios. Sensu, being a newer solution, has a growing community but may have limited resources and integrations available compared to Nagios and Cacti.

6. **Cost**: Cacti and Nagios are open-source solutions, providing cost-effective monitoring options for businesses. Sensu, while offering an open-source version, has an enterprise version with additional features and support, which may incur costs for businesses looking for advanced functionalities and support services.

In Summary, the key differences between Cacti, Nagios, and Sensu lie in their core functionality, alerting mechanisms, scalability, monitoring approach, community support, and cost implications.

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Advice on Nagios, Cacti, Sensu

Matthias
Matthias

Teamlead IT at NanoTemper Technologies

Jun 11, 2020

Decided
  • free open source
  • modern interface and architecture
  • large community
  • extendable I knew Nagios for decades but it was really outdated (by its architecture) at some point. That's why Icinga started first as a fork, not with Icinga2 it is completely built from scratch but backward-compatible with Nagios plugins. Now it has reached a state with which I am confident.
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Comments

Detailed Comparison

Nagios
Nagios
Cacti
Cacti
Sensu
Sensu

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Cacti is a complete network graphing solution designed to harness the power of RRDTool's data storage and graphing functionality. Cacti provides a fast poller, advanced graph templating, multiple data acquisition methods, and user management features out of the box.

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Monitor your entire IT infrastructure;Spot problems before they occur;Know immediately when problems arise;Share availability data with stakeholders;Detect security breaches;Plan and budget for IT upgrades;Reduce downtime and business losses
Unlimited number of graph items can be defined for each graph optionally utilizing CDEFs or data sources from within cacti.;Automatic grouping of GPRINT graph items to AREA, STACK, and LINE[1-3] to allow for quick re-sequencing of graph items.;Auto-Padding support to make sure graph legend text lines up.;Graph data can be manipulated using the CDEF math functions built into RRDTool. These CDEF functions can be defined in cacti and can be used globally on each graph.;Data sources can be created that utilize RRDTool's "create" and "update" functions. Each data source can be used to gather local or remote data and placed on a graph.
Health checks & custom metrics; alerts & incident management; real-time inventory; auto-remediation & custom workflows; container monitoring; Kubernetes monitoring; telemetry & service health checking; multi-cloud monitoring
Statistics
GitHub Stars
57
GitHub Stars
-
GitHub Stars
2.9K
GitHub Forks
38
GitHub Forks
-
GitHub Forks
386
Stacks
811
Stacks
89
Stacks
201
Followers
1.1K
Followers
202
Followers
251
Votes
102
Votes
10
Votes
56
Pros & Cons
Pros
  • 53
    It just works
  • 28
    The standard
  • 12
    Customizable
  • 8
    The Most flexible monitoring system
  • 1
    Huge stack of free checks/plugins to choose from
Pros
  • 3
    Rrdtool based
  • 3
    Free
  • 2
    Fast poller
  • 1
    Graphs from snmp
  • 1
    Graphs from language independent scripts
Pros
  • 13
    Support for almost anything
  • 11
    Easy setup
  • 9
    Message routing
  • 7
    Devs can code their own checks
  • 5
    Ease of use
Cons
  • 1
    Written in Go
  • 1
    Plugins
Integrations
No integrations available
RRDtool
RRDtool
ServiceNow.com
ServiceNow.com
Prometheus
Prometheus
InfluxDB
InfluxDB
Grafana
Grafana
PagerDuty
PagerDuty

What are some alternatives to Nagios, Cacti, Sensu?

Grafana

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Kibana

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Netdata

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Graphite

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Jaeger

Jaeger

Jaeger, a Distributed Tracing System

Telegraf

Telegraf

It is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics.

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