Alternatives to Thanos logo

Alternatives to Thanos

Magneto, Sentry, Kibana, Grafana, and Prometheus are the most popular alternatives and competitors to Thanos.
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What is Thanos and what are its top alternatives?

Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity. It can be added seamlessly on top of existing Prometheus deployments and leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.
Thanos is a tool in the Monitoring Tools category of a tech stack.
Thanos is an open source tool with GitHub stars and GitHub forks. Here’s a link to Thanos's open source repository on GitHub

Top Alternatives to Thanos

  • Magneto
    Magneto

    Magneto was built by Automation Engineers for Automation Engineers out of necessity for a mobile centric test automation framework that's easy to setup, run and utilize. ...

  • Sentry
    Sentry

    Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Graphite
    Graphite

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

Thanos alternatives & related posts

Magneto logo

Magneto

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24
0
Android Test Automation
12
24
+ 1
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PROS OF MAGNETO
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      Sentry logo

      Sentry

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      PROS OF SENTRY
      • 236
        Consolidates similar errors and makes resolution easy
      • 121
        Email Notifications
      • 108
        Open source
      • 84
        Slack integration
      • 71
        Github integration
      • 48
        Easy
      • 44
        User-friendly interface
      • 28
        The most important tool we use in production
      • 18
        Hipchat integration
      • 17
        Heroku Integration
      • 15
        Good documentation
      • 14
        Free tier
      • 9
        Easy setup
      • 9
        Self-hosted
      • 7
        Realiable
      • 6
        Provides context, and great stack trace
      • 4
        Feedback form on error pages
      • 4
        Love it baby
      • 3
        Gitlab integration
      • 3
        Easy Integration
      • 3
        Filter by custom tags
      • 3
        Super user friendly
      • 3
        Captures local variables at each frame in backtraces
      • 1
        Performance measurements
      CONS OF SENTRY
      • 12
        Confusing UI
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        Bundle size

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      Johnny Bell

      For my portfolio websites and my personal OpenSource projects I had started exclusively using React and JavaScript so I needed a way to track any errors that we're happening for my users that I didn't uncover during my personal UAT.

      I had narrowed it down to two tools LogRocket and Sentry (I also tried Bugsnag but it did not make the final two). Before I get into this I want to say that both of these tools are amazing and whichever you choose will suit your needs well.

      I firstly decided to go with LogRocket the fact that they had a recorded screen capture of what the user was doing when the bug happened was amazing... I could go back and rewatch what the user did to replicate that error, this was fantastic. It was also very easy to setup and get going. They had options for React and Redux.js so you can track all your Redux.js actions. I had a fairly large Redux.js store, this was ended up being a issue, it killed the processing power on my machine, Chrome ended up using 2-4gb of ram, so I quickly disabled the Redux.js option.

      After using LogRocket for a month or so I decided to switch to Sentry. I noticed that Sentry was openSorce and everyone was talking about Sentry so I thought I may as well give it a test drive. Setting it up was so easy, I had everything up and running within seconds. It also gives you the option to wrap an errorBoundry in React so get more specific errors. The simplicity of Sentry was a breath of fresh air, it allowed me find the bug that was shown to the user and fix that very simply. The UI for Sentry is beautiful and just really clean to look at, and their emails are also just perfect.

      I have decided to stick with Sentry for the long run, I tested pretty much all the JS error loggers and I find Sentry the best.

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      Kibana logo

      Kibana

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      PROS OF KIBANA
      • 88
        Easy to setup
      • 62
        Free
      • 45
        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

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      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 5.3M 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 · 522.2K 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.

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      Grafana logo

      Grafana

      13.6K
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      Open source Graphite & InfluxDB Dashboard and Graph Editor
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      PROS OF GRAFANA
      • 84
        Beautiful
      • 67
        Graphs are interactive
      • 57
        Free
      • 56
        Easy
      • 33
        Nicer than the Graphite web interface
      • 24
        Many integrations
      • 17
        Can build dashboards
      • 10
        Easy to specify time window
      • 9
        Dashboards contain number tiles
      • 8
        Can collaborate on dashboards
      • 5
        Open Source
      • 5
        Click and drag to zoom in
      • 5
        Integration with InfluxDB
      • 4
        Authentification and users management
      • 4
        Threshold limits in graphs
      • 3
        It is open to cloud watch and many database
      • 3
        Simple and native support to Prometheus
      • 2
        Great community support
      • 2
        Alerts
      • 2
        You can use this for development to check memcache
      • 2
        You can visualize real time data to put alerts
      • 0
        Grapsh as code
      • 0
        Plugin visualizationa
      CONS OF GRAFANA
      • 1
        No interactive query builder

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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
      Matt Menzenski
      Senior Software Engineering Manager at PayIt · | 14 upvotes · 255.5K 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.

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      Prometheus logo

      Prometheus

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      238
      An open-source service monitoring system and time series database, developed by SoundCloud
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      PROS OF PROMETHEUS
      • 46
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      • 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

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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
      Matt Menzenski
      Senior Software Engineering Manager at PayIt · | 14 upvotes · 255.5K 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
      Nagios logo

      Nagios

      809
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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
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        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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
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        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.

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        Zabbix logo

        Zabbix

        589
        857
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        Track, record, alert and visualize performance and availability of IT resources
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        PROS OF ZABBIX
        • 18
          Free
        • 9
          Alerts
        • 5
          Service/node/network discovery
        • 4
          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

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        Shared insights
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        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!

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        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?

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        Graphite logo

        Graphite

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        A highly scalable real-time graphing system
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        PROS OF GRAPHITE
        • 16
          Render any graph
        • 9
          Great functions to apply on timeseries
        • 7
          Well supported integrations
        • 5
          Includes event tracking
        • 2
          Rolling aggregation makes storage managable
        CONS OF GRAPHITE
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          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 3.2M 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

          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).

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