Alternatives to Observium logo

Alternatives to Observium

Zabbix, PRTG, Nagios, Cacti, and LibreNMS are the most popular alternatives and competitors to Observium.
18
58
+ 1
1

What is Observium and what are its top alternatives?

It is a low-maintenance auto-discovering network monitoring platform supporting a wide range of device types, platforms and operating systems
Observium is a tool in the Network Monitoring category of a tech stack.

Top Alternatives to Observium

  • Zabbix
    Zabbix

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

  • PRTG
    PRTG

    It 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. ...

  • Nagios
    Nagios

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

  • Cacti
    Cacti

    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. ...

  • LibreNMS
    LibreNMS

    It 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. ...

  • 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. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

Observium alternatives & related posts

Zabbix logo

Zabbix

670
971
66
Track, record, alert and visualize performance and availability of IT resources
670
971
+ 1
66
PROS OF ZABBIX
  • 21
    Free
  • 9
    Alerts
  • 5
    Service/node/network discovery
  • 5
    Templates
  • 4
    Base metrics from the box
  • 3
    Multi-dashboards
  • 3
    SMS/Email/Messenger alerts
  • 2
    Grafana plugin available
  • 2
    Supports Graphs ans screens
  • 2
    Support proxies (for monitoring remote branches)
  • 1
    Perform website checking (response time, loading, ...)
  • 1
    API available for creating own apps
  • 1
    Templates free available (Zabbix Share)
  • 1
    Works with multiple databases
  • 1
    Advanced integrations
  • 1
    Supports multiple protocols/agents
  • 1
    Complete Logs Report
  • 1
    Open source
  • 1
    Supports large variety of Operating Systems
  • 1
    Supports JMX (Java, Tomcat, Jboss, ...)
CONS OF ZABBIX
  • 5
    The UI is in PHP
  • 2
    Puppet module is sluggish

related Zabbix posts

Shared insights
on
DatadogDatadogZabbixZabbixCentreonCentreon

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!

See more
Shared insights
on
ZabbixZabbixCheckmkCheckmk

I am looking for an easy to set up and use monitoring solution for my servers and network infrastructure. What are the main differences between Checkmk and Zabbix? What would you recommend and why?

See more
PRTG logo

PRTG

55
65
0
A powerful & easy network monitoring software
55
65
+ 1
0
PROS OF PRTG
    Be the first to leave a pro
    CONS OF PRTG
    • 1
      Poor search capabilities
    • 1
      Graphs are static
    • 1
      Running on windows

    related PRTG posts

    Nagios logo

    Nagios

    826
    1.1K
    102
    Complete monitoring and alerting for servers, switches, applications, and services
    826
    1.1K
    + 1
    102
    PROS OF NAGIOS
    • 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
    CONS OF NAGIOS
      Be the first to leave a con

      related Nagios posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

      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...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more
      Shared insights
      on
      PrometheusPrometheusNagiosNagios

      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.

      See more
      Cacti logo

      Cacti

      88
      201
      10
      Cacti stores all of the necessary information to create graphs and populate them with data in a MySQL...
      88
      201
      + 1
      10
      PROS OF CACTI
      • 3
        Free
      • 3
        Rrdtool based
      • 2
        Fast poller
      • 1
        Graphs from snmp
      • 1
        Graphs from language independent scripts
      CONS OF CACTI
        Be the first to leave a con

        related Cacti posts

        LibreNMS logo

        LibreNMS

        56
        184
        0
        Opensource Auto-discoverying network monitoring system
        56
        184
        + 1
        0
        PROS OF LIBRENMS
          Be the first to leave a pro
          CONS OF LIBRENMS
            Be the first to leave a con

            related LibreNMS posts

            Prometheus logo

            Prometheus

            4.2K
            3.8K
            239
            An open-source service monitoring system and time series database, developed by SoundCloud
            4.2K
            3.8K
            + 1
            239
            PROS OF PROMETHEUS
            • 47
              Powerful easy to use monitoring
            • 38
              Flexible query language
            • 32
              Dimensional data model
            • 27
              Alerts
            • 23
              Active and responsive community
            • 22
              Extensive integrations
            • 19
              Easy to setup
            • 12
              Beautiful Model and Query language
            • 7
              Easy to extend
            • 6
              Nice
            • 3
              Written in Go
            • 2
              Good for experimentation
            • 1
              Easy for monitoring
            CONS OF PROMETHEUS
            • 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
            • 2
              Written in Go
            • 2
              TLS is quite difficult to understand
            • 2
              Requires multiple applications and tools
            • 1
              Single point of failure

            related Prometheus posts

            Matt Menzenski
            Senior Software Engineering Manager at PayIt · | 16 upvotes · 999.1K views

            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.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

            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...

            https://eng.uber.com/m3/

            (GitHub : https://github.com/m3db/m3)

            See more
            JavaScript logo

            JavaScript

            352.3K
            268.2K
            8.1K
            Lightweight, interpreted, object-oriented language with first-class functions
            352.3K
            268.2K
            + 1
            8.1K
            PROS OF JAVASCRIPT
            • 1.7K
              Can be used on frontend/backend
            • 1.5K
              It's everywhere
            • 1.2K
              Lots of great frameworks
            • 897
              Fast
            • 745
              Light weight
            • 425
              Flexible
            • 392
              You can't get a device today that doesn't run js
            • 286
              Non-blocking i/o
            • 237
              Ubiquitousness
            • 191
              Expressive
            • 55
              Extended functionality to web pages
            • 49
              Relatively easy language
            • 46
              Executed on the client side
            • 30
              Relatively fast to the end user
            • 25
              Pure Javascript
            • 21
              Functional programming
            • 15
              Async
            • 13
              Full-stack
            • 12
              Setup is easy
            • 12
              Future Language of The Web
            • 12
              Its everywhere
            • 11
              Because I love functions
            • 11
              JavaScript is the New PHP
            • 10
              Like it or not, JS is part of the web standard
            • 9
              Expansive community
            • 9
              Everyone use it
            • 9
              Can be used in backend, frontend and DB
            • 9
              Easy
            • 8
              Most Popular Language in the World
            • 8
              Powerful
            • 8
              Can be used both as frontend and backend as well
            • 8
              For the good parts
            • 8
              No need to use PHP
            • 8
              Easy to hire developers
            • 7
              Agile, packages simple to use
            • 7
              Love-hate relationship
            • 7
              Photoshop has 3 JS runtimes built in
            • 7
              Evolution of C
            • 7
              It's fun
            • 7
              Hard not to use
            • 7
              Versitile
            • 7
              Its fun and fast
            • 7
              Nice
            • 7
              Popularized Class-Less Architecture & Lambdas
            • 7
              Supports lambdas and closures
            • 6
              It let's me use Babel & Typescript
            • 6
              Can be used on frontend/backend/Mobile/create PRO Ui
            • 6
              1.6K Can be used on frontend/backend
            • 6
              Client side JS uses the visitors CPU to save Server Res
            • 6
              Easy to make something
            • 5
              Clojurescript
            • 5
              Promise relationship
            • 5
              Stockholm Syndrome
            • 5
              Function expressions are useful for callbacks
            • 5
              Scope manipulation
            • 5
              Everywhere
            • 5
              Client processing
            • 5
              What to add
            • 4
              Because it is so simple and lightweight
            • 4
              Only Programming language on browser
            • 1
              Test
            • 1
              Hard to learn
            • 1
              Test2
            • 1
              Not the best
            • 1
              Easy to understand
            • 1
              Subskill #4
            • 1
              Easy to learn
            • 0
              Hard 彤
            CONS OF JAVASCRIPT
            • 22
              A constant moving target, too much churn
            • 20
              Horribly inconsistent
            • 15
              Javascript is the New PHP
            • 9
              No ability to monitor memory utilitization
            • 8
              Shows Zero output in case of ANY error
            • 7
              Thinks strange results are better than errors
            • 6
              Can be ugly
            • 3
              No GitHub
            • 2
              Slow

            related JavaScript posts

            Zach Holman

            Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

            But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

            But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

            Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

            How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

            Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

            Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

            https://eng.uber.com/distributed-tracing/

            (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

            Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

            See more
            Git logo

            Git

            291.3K
            174.8K
            6.6K
            Fast, scalable, distributed revision control system
            291.3K
            174.8K
            + 1
            6.6K
            PROS OF GIT
            • 1.4K
              Distributed version control system
            • 1.1K
              Efficient branching and merging
            • 959
              Fast
            • 845
              Open source
            • 726
              Better than svn
            • 368
              Great command-line application
            • 306
              Simple
            • 291
              Free
            • 232
              Easy to use
            • 222
              Does not require server
            • 27
              Distributed
            • 22
              Small & Fast
            • 18
              Feature based workflow
            • 15
              Staging Area
            • 13
              Most wide-spread VSC
            • 11
              Role-based codelines
            • 11
              Disposable Experimentation
            • 7
              Frictionless Context Switching
            • 6
              Data Assurance
            • 5
              Efficient
            • 4
              Just awesome
            • 3
              Github integration
            • 3
              Easy branching and merging
            • 2
              Compatible
            • 2
              Flexible
            • 2
              Possible to lose history and commits
            • 1
              Rebase supported natively; reflog; access to plumbing
            • 1
              Light
            • 1
              Team Integration
            • 1
              Fast, scalable, distributed revision control system
            • 1
              Easy
            • 1
              Flexible, easy, Safe, and fast
            • 1
              CLI is great, but the GUI tools are awesome
            • 1
              It's what you do
            • 0
              Phinx
            CONS OF GIT
            • 16
              Hard to learn
            • 11
              Inconsistent command line interface
            • 9
              Easy to lose uncommitted work
            • 7
              Worst documentation ever possibly made
            • 5
              Awful merge handling
            • 3
              Unexistent preventive security flows
            • 3
              Rebase hell
            • 2
              When --force is disabled, cannot rebase
            • 2
              Ironically even die-hard supporters screw up badly
            • 1
              Doesn't scale for big data

            related Git posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Tymoteusz Paul
            Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

            Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

            It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

            I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

            We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

            If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

            The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

            Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

            See more