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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Monitoring Tools
  5. Cacti vs Prometheus vs Zabbix

Cacti vs Prometheus vs Zabbix

OverviewDecisionsComparisonAlternatives

Overview

Cacti
Cacti
Stacks89
Followers202
Votes10
Zabbix
Zabbix
Stacks684
Followers981
Votes66
GitHub Stars5.3K
Forks1.1K
Prometheus
Prometheus
Stacks4.8K
Followers3.8K
Votes239
GitHub Stars61.1K
Forks9.9K

Cacti vs Prometheus vs Zabbix: What are the differences?

Monitoring Tools Comparison

Monitoring tools play a crucial role in managing and maintaining the performance and availability of systems. In this comparison, we will discuss the key differences between Cacti, Prometheus, and Zabbix.

  1. Architecture: Cacti is a network monitoring tool based on the Simple Network Management Protocol (SNMP) and uses RRDtool as a data logging and graphing tool. Prometheus, on the other hand, uses a pull-based model where it scrapes metrics from various targets using HTTP. Zabbix uses an agent-based approach, where agents are installed on target systems and send data to the Zabbix server.

  2. Support for Cloud-native Environments: Prometheus is well-suited for monitoring cloud-native environments, such as microservices or containerized applications, as it integrates seamlessly with container orchestration platforms like Kubernetes. Cacti and Zabbix, although flexible, may require additional configurations and plugins to monitor such environments effectively.

  3. Data Storage and Retention: Cacti stores data in RRD files, which have a fixed resolution and retention period. This makes it suitable for collecting and visualizing time series data with a fixed granularity. Prometheus, on the other hand, uses a powerful multi-dimensional data model and a custom time series database. It provides flexible queries, enabling ad hoc analysis and monitoring with higher resolution and longer retention. Zabbix utilizes its own database format and offers options for partitioning and archiving historical data efficiently.

  4. Alerting and Notification: Prometheus has a built-in alerting system that allows creating alert rules to trigger notifications based on specific conditions. It can send alerts to various endpoints, including email, pager, and chat platforms. Cacti and Zabbix also support alerting and notifications, but they may require additional configurations or plugins to achieve similar functionality.

  5. Ecosystem and Integrations: Prometheus has a rich ecosystem with many exporters, which are used to expose metrics from a wide range of technologies and applications. It also has native integrations with Grafana for visualization and Alertmanager for alert routing and deduplication. Cacti and Zabbix have their own plugins and integrations, but they may have a narrower range of options compared to Prometheus.

  6. Ease of Setup and Configuration: Cacti is known for its user-friendly interface, making it relatively easy to set up and configure for basic monitoring needs. Zabbix provides a comprehensive web-based interface and offers a wide range of pre-configured templates, making it easier to get started with monitoring complex systems. Prometheus, being more focused on cloud-native environments, may require more expertise and configuration to set up effectively.

In summary, Cacti is a traditional SNMP-based network monitoring tool with RRDtool for graphing, Prometheus is well-suited for cloud-native environments with flexible metrics collection and query capabilities, and Zabbix offers a comprehensive solution with agent-based data collection and a rich feature set. The choice between these tools depends on the specific requirements and the environment being monitored.

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Advice on Cacti, Zabbix, Prometheus

Leonardo Henrique da
Leonardo Henrique da

Pleno QA Enginneer at SolarMarket

Dec 8, 2020

Decided

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

402k views402k
Comments
Raja Subramaniam
Raja Subramaniam

Aug 27, 2019

Needs adviceonPrometheusPrometheusKubernetesKubernetesSysdigSysdig

We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.

779k views779k
Comments
vivek
vivek

Jun 8, 2020

Needs adviceonCentreonCentreonZabbixZabbixDatadogDatadog

My team is divided on using Centreon or Zabbix for enterprise monitoring and alert automation. Can someone let us know which one is better? There is one more tool called Datadog that we are using for cloud assets. Of course, Datadog presents us with huge bills. So we want to have a comparative study. Suggestions and advice are welcome. Thanks!

795k views795k
Comments

Detailed Comparison

Cacti
Cacti
Zabbix
Zabbix
Prometheus
Prometheus

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.

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

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.

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.
Smart, Highly Automated Metric Collection; Advanced Problem Detection; Intelligent Alerting and Remediation
Dimensional data; Powerful queries; Great visualization; Efficient storage; Precise alerting; Simple operation
Statistics
GitHub Stars
-
GitHub Stars
5.3K
GitHub Stars
61.1K
GitHub Forks
-
GitHub Forks
1.1K
GitHub Forks
9.9K
Stacks
89
Stacks
684
Stacks
4.8K
Followers
202
Followers
981
Followers
3.8K
Votes
10
Votes
66
Votes
239
Pros & Cons
Pros
  • 3
    Free
  • 3
    Rrdtool based
  • 2
    Fast poller
  • 1
    Graphs from language independent scripts
  • 1
    Graphs from snmp
Pros
  • 21
    Free
  • 9
    Alerts
  • 5
    Service/node/network discovery
  • 5
    Templates
  • 4
    Base metrics from the box
Cons
  • 5
    The UI is in PHP
  • 2
    Puppet module is sluggish
Pros
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
Cons
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
Integrations
RRDtool
RRDtool
Slack
Slack
Jira
Jira
PagerDuty
PagerDuty
Grafana
Grafana
Ansible
Ansible
Skype
Skype
Chef
Chef
Bugzilla
Bugzilla
HipChat
HipChat
ServiceNow.com
ServiceNow.com
Grafana
Grafana

What are some alternatives to Cacti, Zabbix, Prometheus?

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.

Nagios

Nagios

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

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

Sensu

Sensu

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.

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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