What is Zabbix and what are its top alternatives?
Zabbix is a popular open-source monitoring solution that offers network and infrastructure monitoring capabilities. It provides real-time monitoring, alerting, and visualization features to help organizations monitor their IT environment efficiently. Key features include auto-discovery of devices, flexible alerting mechanisms, customizable dashboards, and support for various data sources. However, some limitations of Zabbix include a steep learning curve for beginners, complex configuration settings, and resource-intensive performance.
- Prometheus: Prometheus is a cloud-native monitoring solution that specializes in monitoring dynamic container environments. It offers powerful querying, alerting, and visualization functionalities. Pros include flexible data processing, multi-dimensional data model, and integrations with various exporters. Cons compared to Zabbix are the need for additional components for full functionality and a steeper learning curve.
- Nagios: Nagios is a widely-used monitoring tool that focuses on monitoring IT infrastructure components like servers, switches, and applications. It provides comprehensive monitoring capabilities, plugins for extending functionality, and strong notification features. Pros include a large community, extensive plugin ecosystem, and historical performance data. Cons include an outdated web interface and complex setup and configuration process.
- Grafana: Grafana is a visualization tool that works well with data sources like Prometheus, Graphite, and InfluxDB. It offers advanced visualization features, dashboard creation, and alerting capabilities. Pros include a user-friendly interface, extensive graphing options, and community-built dashboards. Cons compared to Zabbix are the lack of in-depth monitoring functionalities and the need for additional data sources.
- Icinga: Icinga is an open-source monitoring solution that focuses on extensibility and scalability. It provides monitoring for networks, servers, and services with features like reporting, graphing, and distributed monitoring. Pros include a modular design, REST API for integration, and strong community support. Cons compared to Zabbix are the complexity of setting up advanced monitoring configurations and the learning curve for beginners.
- Observium: Observium is a network monitoring tool that specializes in monitoring network devices like routers, switches, and firewalls. It offers automatic discovery, detailed network insights, and SNMP monitoring capabilities. Pros include a simple setup process, support for multiple network device vendors, and customizable dashboards. Cons compared to Zabbix are the limited support for non-network infrastructure monitoring and fewer alerting options.
- Netdata: Netdata is a real-time monitoring and performance optimization tool for servers, containers, and applications. It provides per-second metrics, customizable dashboards, and anomaly detection features. Pros include a lightweight agent, simple installation process, and cloud monitoring capabilities. Cons compared to Zabbix are the lack of long-term data storage and the focus on real-time monitoring rather than historical data analysis.
- Opsgenie: Opsgenie is an incident and alert management tool that helps teams respond to alerts and incidents effectively. It offers alert routing, on-call scheduling, and incident visualization features. Pros include integrations with monitoring tools, escalation policies, and mobile alerting options. Cons compared to Zabbix are the focus on incident response rather than monitoring and the additional cost for alert management capabilities.
- Zenoss: Zenoss is an enterprise monitoring solution that provides unified monitoring for networks, infrastructure, and applications. It offers automated discovery, event correlation, and performance analytics features. Pros include a single-pane-of-glass view, support for hybrid environments, and customizable reporting. Cons compared to Zabbix are the higher cost for enterprise features and the complexity of setting up advanced configurations.
- Checkmk: Checkmk is a monitoring tool that focuses on simplicity and ease of use. It offers monitoring for servers, networks, applications, and cloud environments with features like agent-based monitoring, automation, and reporting. Pros include a user-friendly web interface, pre-configured monitoring checks, and scalability for large environments. Cons compared to Zabbix are the lack of customization options and the reliance on predefined check plugins.
- Splunk: Splunk is a data analytics and monitoring platform that specializes in log monitoring and analysis. It offers real-time visibility, search capabilities, and machine learning features for troubleshooting and monitoring. Pros include advanced analytics capabilities, machine learning algorithms, and customizable dashboards. Cons compared to Zabbix are the high cost for enterprise features and the focus on log monitoring rather than infrastructure monitoring.
Top Alternatives to Zabbix
NagiosNagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...
GraphiteGraphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand ...
DatadogDatadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...
InfluxDBInfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. ...
PrometheusPrometheus 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. ...
PRTGIt can monitor and classify system conditions like bandwidth usage or uptime and collect statistics from miscellaneous hosts as switches, routers, servers and other devices and applications. ...
LibreNMSIt is an auto-discovering PHP/MySQL/SNMP based network monitoring which includes support for a wide range of network hardware and operating systems including Cisco, Linux, FreeBSD, Juniper, Brocade, Foundry, HP and many more. ...
GrafanaGrafana 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. ...
Zabbix alternatives & related posts
Nagios
- It just works53
- The standard28
- Customizable12
- The Most flexible monitoring system8
- Huge stack of free checks/plugins to choose from1
related Nagios posts
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)
I am new to DevOps and looking for training in DevOps. Some institutes are offering Nagios while some Prometheus in their syllabus. Please suggest which one is being used in the industry and which one should I learn.
- Render any graph16
- Great functions to apply on timeseries9
- Well supported integrations8
- Includes event tracking6
- Rolling aggregation makes storage managable3
related Graphite posts
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)
A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).
- Monitoring for many apps (databases, web servers, etc)139
- Easy setup107
- Powerful ui87
- Powerful integrations84
- Great value70
- Great visualization54
- Events + metrics = clarity46
- Notifications41
- Custom metrics41
- Flexibility39
- Free & paid plans19
- Great customer support16
- Makes my life easier15
- Adapts automatically as i scale up10
- Easy setup and plugins9
- Super easy and powerful8
- AWS support7
- In-context collaboration7
- Rich in features6
- Docker support5
- Cost4
- Full visibility of applications4
- Monitor almost everything4
- Cute logo4
- Automation tools4
- Source control and bug tracking4
- Simple, powerful, great for infra4
- Easy to Analyze4
- Best than others4
- Best in the field3
- Expensive3
- Good for Startups3
- Free setup3
- APM2
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
related Datadog posts
Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.
We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.
Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.
Current Environment: .NET Core Web app hosted on Microsoft IIS
Future Environment: Web app will be hosted on Microsoft Azure
Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server
Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.
Please advise on the above. Thanks!
- Time-series data analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
related InfluxDB posts
Hi everyone. I'm trying to create my personal syslog monitoring.
To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.
To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.
I would like to know... Which is a cheaper and scalable solution?
Or even if there is a better way to do it.
Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
related Prometheus posts
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
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)
- Poor search capabilities1
- Graphs are static1
- Running on windows1
related PRTG posts
related LibreNMS posts
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
- No interactive query builder1
related Grafana posts
Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.
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)