Alternatives to Sensu logo

Alternatives to Sensu

Prometheus, Nagios, Zabbix, Datadog, and Icinga are the most popular alternatives and competitors to Sensu.
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What is Sensu and what are its top alternatives?

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.
Sensu is a tool in the Monitoring Tools category of a tech stack.
Sensu is an open source tool with 2.9K GitHub stars and 397 GitHub forks. Here鈥檚 a link to Sensu's open source repository on GitHub

Top Alternatives to Sensu

  • 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. ...

  • Nagios

    Nagios

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

  • Zabbix

    Zabbix

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

  • Datadog

    Datadog

    Datadog 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! ...

  • Icinga

    Icinga

    It monitors availability and performance, gives you simple access to relevant data and raises alerts to keep you in the loop. It was originally created as a fork of the Nagios system monitoring application. ...

  • 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. ...

  • ELK

    ELK

    It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server鈥憇ide data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. ...

  • 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. ...

Sensu alternatives & related posts

Prometheus logo

Prometheus

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2.4K
217
An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
  • 40
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 31
    Dimensional data model
  • 22
    Alerts
  • 21
    Active and responsive community
  • 18
    Extensive integrations
  • 18
    Easy to setup
  • 11
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 1
    Easy for monitoring
  • 1
    Good for experimentation
CONS OF PROMETHEUS
  • 8
    Just for metrics
  • 5
    Needs monitoring to access metrics endpoints
  • 4
    Bad UI
  • 2
    Supports only active agents
  • 2
    Written in Go
  • 2
    Not easy to configure and use
  • 1
    Requires multiple applications and tools
  • 1
    TLS is quite difficult to understand

related Prometheus posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 13 upvotes 路 2.7M 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鈥檚 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鈥檚 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

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.

See more
Nagios logo

Nagios

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Complete monitoring and alerting for servers, switches, applications, and services
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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 | 13 upvotes 路 2.7M 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鈥檚 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鈥檚 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
    Zabbix logo

    Zabbix

    469
    631
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    Track, record, alert and visualize performance and availability of IT resources
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    PROS OF ZABBIX
    • 13
      Free
    • 7
      Alerts
    • 5
      Service/node/network discovery
    • 5
      Templates
    • 4
      Base metrics from the box
    • 3
      Multi-dashboards
    • 3
      SMS/Email/Messenger alerts
    • 2
      Supports Graphs ans screens
    • 2
      Support proxies (for monitoring remote branches)
    • 2
      Grafana plugin available
    • 1
      API available for creating own apps
    • 1
      Templates free available (Zabbix Share)
    • 1
      Works with multiple databases
    • 1
      Supports large variety of Operating Systems
    • 1
      Supports multiple protocols/agents
    • 1
      Complete Logs Report
    • 1
      Advanced integrations
    • 1
      Supports JMX (Java, Tomcat, Jboss, ...)
    • 1
      Perform website checking (response time, loading, ...)
    CONS OF ZABBIX
    • 4
      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
    Datadog logo

    Datadog

    5.1K
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    Unify logs, metrics, and traces from across your distributed infrastructure.
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    PROS OF DATADOG
    • 130
      Monitoring for many apps (databases, web servers, etc)
    • 103
      Easy setup
    • 83
      Powerful ui
    • 80
      Powerful integrations
    • 66
      Great value
    • 50
      Great visualization
    • 41
      Events + metrics = clarity
    • 39
      Custom metrics
    • 38
      Notifications
    • 36
      Flexibility
    • 16
      Free & paid plans
    • 13
      Great customer support
    • 12
      Makes my life easier
    • 7
      Easy setup and plugins
    • 6
      Adapts automatically as i scale up
    • 5
      Super easy and powerful
    • 4
      In-context collaboration
    • 4
      AWS support
    • 3
      Rich in features
    • 2
      Best than others
    • 2
      Cost
    • 2
      Docker support
    • 1
      Free setup
    • 1
      Easy to Analyze
    • 1
      Monitor almost everything
    • 1
      Automation tools
    • 1
      Source control and bug tracking
    • 1
      Full visibility of applications
    • 1
      Expensive
    • 1
      Cute logo
    • 1
      Simple, powerful, great for infra
    CONS OF DATADOG
    • 12
      Expensive
    • 2
      No errors exception tracking
    • 1
      Complicated

    related Datadog posts

    Robert Zuber

    Our primary source of monitoring and alerting is Datadog. We鈥檝e got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We鈥檝e definitely scaled past the point where managing dashboards is easy, but we haven鈥檛 had time to invest in using features like Anomaly Detection. We鈥檝e started using Honeycomb for some targeted debugging of complex production issues and we are liking what we鈥檝e 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.

    See more

    We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

    We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

    We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

    You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

    See more
    Icinga logo

    Icinga

    88
    59
    0
    A resilient, open source monitoring system
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    PROS OF ICINGA
      Be the first to leave a pro
      CONS OF ICINGA
        Be the first to leave a con

        related Icinga posts

        One size definitely doesn鈥檛 fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O鈥橰eilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company鈥檚 use cases.

        At the time, Slack used a few well-known monitoring tools since it鈥檚 Technical Operations team wasn鈥檛 large enough to build an in-house solution for all of these. Nor did the team think it鈥檚 sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack鈥檚 needs.

        On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they鈥檝e tried to build usability into Grafana that reflects the team鈥檚 mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

        See more
        Grafana logo

        Grafana

        8.6K
        6.5K
        369
        Open source Graphite & InfluxDB Dashboard and Graph Editor
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        PROS OF GRAFANA
        • 79
          Beautiful
        • 64
          Graphs are interactive
        • 52
          Easy
        • 50
          Free
        • 33
          Nicer than the Graphite web interface
        • 23
          Many integrations
        • 14
          Can build dashboards
        • 9
          Easy to specify time window
        • 8
          Can collaborate on dashboards
        • 8
          Dashboards contain number tiles
        • 5
          Open Source
        • 4
          Integration with InfluxDB
        • 4
          Authentification and users management
        • 4
          Click and drag to zoom in
        • 3
          Threshold limits in graphs
        • 2
          Alerts
        • 2
          Great community support
        • 2
          It is open to cloud watch and many database
        • 1
          You can visualize real time data to put alerts
        • 1
          You can use this for development to check memcache
        • 1
          Simple and native support to Prometheus
        • 0
          Plugin visualizationa
        • 0
          Grapsh as code
        CONS OF GRAFANA
          Be the first to leave a con

          related Grafana posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber | 13 upvotes 路 2.7M 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鈥檚 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鈥檚 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
          GrafanaGrafanaKibanaKibana
          at

          For our Predictive Analytics platform, we have used both Grafana and Kibana

          Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

          For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

          • Creating and organizing visualization panels
          • Templating the panels on dashboards for repetetive tasks
          • Realtime monitoring, filtering of charts based on conditions and variables
          • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
          See more
          ELK logo

          ELK

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          535
          8
          The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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          PROS OF ELK
          • 8
            Open source
          CONS OF ELK
          • 3
            Elastic Search is a resource hog
          • 3
            Logstash configuration is a pain

          related ELK posts

          Wallace Alves
          Cyber Security Analyst | 1 upvote 路 523.8K views

          Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

          See more
          Kibana logo

          Kibana

          12.3K
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          253
          Explore & Visualize Your Data
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          PROS OF KIBANA
          • 86
            Easy to setup
          • 61
            Free
          • 44
            Can search text
          • 21
            Has pie chart
          • 13
            X-axis is not restricted to timestamp
          • 8
            Easy queries and is a good way to view logs
          • 6
            Supports Plugins
          • 3
            Dev Tools
          • 3
            More "user-friendly"
          • 3
            Can build dashboards
          • 2
            Easy to drill-down
          • 2
            Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
          • 1
            Up and running
          CONS OF KIBANA
          • 5
            Unintuituve
          • 3
            Elasticsearch is huge
          • 3
            Works on top of elastic only
          • 2
            Hardweight UI

          related Kibana posts

          Tymoteusz Paul
          Devops guy at X20X Development LTD | 21 upvotes 路 4M 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.

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          Patrick Sun
          Software Engineer at Stitch Fix | 11 upvotes 路 403.1K views

          Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

          Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

          See more