Alternatives to Puppet Labs logo

Alternatives to Puppet Labs

Ansible, Chef, Capistrano, Fabric, and Salt are the most popular alternatives and competitors to Puppet Labs.
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What is Puppet Labs and what are its top alternatives?

Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification.
Puppet Labs is a tool in the Server Configuration and Automation category of a tech stack.
Puppet Labs is an open source tool with 6.3K GitHub stars and 2.2K GitHub forks. Here’s a link to Puppet Labs's open source repository on GitHub

Top Alternatives to Puppet Labs

  • Ansible

    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Chef

    Chef

    Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others. ...

  • Capistrano

    Capistrano

    Capistrano is a remote server automation tool. It supports the scripting and execution of arbitrary tasks, and includes a set of sane-default deployment workflows. ...

  • Fabric

    Fabric

    Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution. ...

  • Salt

    Salt

    Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more. ...

  • AWS OpsWorks

    AWS OpsWorks

    Start from templates for common technologies like Ruby, Node.JS, PHP, and Java, or build your own using Chef recipes to install software packages and perform any task that you can script. AWS OpsWorks can scale your application using automatic load-based or time-based scaling and maintain the health of your application by detecting failed instances and replacing them. You have full control of deployments and automation of each component ...

  • EasyEngine

    EasyEngine

    It is a command-line tool for the Nginx web servers to manage WordPress sites that are running on the LEMP Stack (Linux, Nginx, MySQL/MariaDB, and PHP-FPM). It is created with python and can be installed on Ubuntu and Linux Debian distributions. ...

  • Rundeck

    Rundeck

    A self-service operations platform used for support tasks, enterprise job scheduling, deployment, and more. ...

Puppet Labs alternatives & related posts

Ansible logo

Ansible

13.8K
11K
1.3K
Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
13.8K
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+ 1
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PROS OF ANSIBLE
  • 275
    Agentless
  • 204
    Great configuration
  • 194
    Simple
  • 173
    Powerful
  • 151
    Easy to learn
  • 66
    Flexible
  • 54
    Doesn't get in the way of getting s--- done
  • 33
    Makes sense
  • 29
    Super efficient and flexible
  • 27
    Powerful
  • 11
    Dynamic Inventory
  • 8
    Backed by Red Hat
  • 7
    Works with AWS
  • 6
    Easy to maintain
  • 6
    Cloud Oriented
  • 4
    Simple
  • 4
    Because SSH
  • 4
    Multi language
  • 4
    Easy
  • 4
    Procedural or declarative, or both
  • 4
    Simple and powerful
  • 3
    Consistency
  • 3
    Vagrant provisioner
  • 2
    Debugging is simple
  • 2
    Well-documented
  • 2
    Fast as hell
  • 2
    Masterless
  • 2
    Merge hash to get final configuration similar to hiera
  • 1
    Certified Content
  • 1
    Work on windows, but difficult to manage
CONS OF ANSIBLE
  • 5
    Dangerous
  • 5
    Hard to install
  • 3
    Bloated
  • 3
    Backward compatibility
  • 2
    Doesn't Run on Windows
  • 2
    No immutable infrastructure

related Ansible posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.6M 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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Sebastian Gębski

Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

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Chef logo

Chef

1.1K
963
344
Build, destroy and rebuild servers on any public or private cloud
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PROS OF CHEF
  • 109
    Dynamic and idempotent server configuration
  • 76
    Reusable components
  • 47
    Integration testing with Vagrant
  • 43
    Repeatable
  • 30
    Mock testing with Chefspec
  • 14
    Ruby
  • 8
    Can package cookbooks to guarantee repeatability
  • 7
    Works with AWS
  • 3
    Has marketplace where you get readymade cookbooks
  • 3
    Matured product with good community support
  • 2
    Less declarative more procedural
  • 2
    Open source configuration mgmt made easy(ish)
CONS OF CHEF
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    related Chef posts

    In late 2013, the Operations Engineering team at PagerDuty was made up of 4 engineers, and was comprised of generalists, each of whom had one or two areas of depth. Although the Operations Team ran its own on-call, each engineering team at PagerDuty also participated on the pager.

    The Operations Engineering Team owned 150+ servers spanning multiple cloud providers, and used Chef to automate their infrastructure across the various cloud providers with a mix of completely custom cookbooks and customized community cookbooks.

    Custom cookbooks were managed by Berkshelf, andach custom cookbook contained its own tests based on ChefSpec 3, coupled with Rspec.

    Jenkins was used to GitHub for new changes and to handle unit testing of those features.

    See more
    Marcel Kornegoor

    Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

    For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

    For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

    Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

    See more
    Capistrano logo

    Capistrano

    1K
    596
    234
    A remote server automation and deployment tool written in Ruby
    1K
    596
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    PROS OF CAPISTRANO
    • 123
      Automated deployment with several custom recipes
    • 63
      Simple
    • 23
      Ruby
    • 11
      Release-folders with symlinks
    • 9
      Multistage deployment
    • 2
      Cryptic syntax
    • 2
      Integrated rollback
    • 1
      Supports aws
    CONS OF CAPISTRANO
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      related Capistrano posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.3M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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      Kir Shatrov
      Engineering Lead at Shopify · | 13 upvotes · 111K views

      Shipit, our deployment tool, is at the heart of Continuous Delivery at Shopify. Shipit is an orchestrator that runs and tracks progress of any deploy script that you provide for a project. It supports deploying to Rubygems, Pip, Heroku and Capistrano out of the box. For us, it's mostly kubernetes-deploy or Capistrano for legacy projects.

      We use a slightly tweaked GitHub flow, with feature development going in branches and the master branch being the source of truth for the state of things in production. When your PR is ready, you add it to the Merge Queue in ShipIt. The idea behind the Merge Queue is to control the rate of code that is being merged to master branch. In the busy hours, we have many developers who want to merge the PRs, but at the same time we don't want to introduce too many changes to the system at the same time. Merge Queue limits deploys to 5-10 commits at a time, which makes it easier to identify issues and roll back in case we notice any unexpected behaviour after the deploy.

      We use a browser extension to make Merge Queue play nicely with the Merge button on GitHub:

      Both Shipit and kubernetes-deploy are open source, and we've heard quite a few success stories from companies who have adopted our flow.

      #BuildTestDeploy #ContainerTools #ApplicationHosting #PlatformAsAService

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      Fabric logo

      Fabric

      414
      282
      74
      Simple, Pythonic remote execution and deployment
      414
      282
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      PROS OF FABRIC
      • 22
        Python
      • 21
        Simple
      • 5
        Low learning curve, from bash script to Python power
      • 5
        Installation feedback for Twitter App Cards
      • 3
        Easy to add any type of job
      • 3
        Single config file
      • 3
        Easy on maintainance
      • 3
        Installation? pip install fabric... Boom
      • 3
        Agentless
      • 2
        Easily automate any set system automation
      • 1
        Backward compatibility
      • 1
        Remote sudo execution
      • 1
        Crash Analytics
      • 1
        Flexible
      CONS OF FABRIC
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        related Fabric posts

        Salt logo

        Salt

        386
        377
        164
        Fast, scalable and flexible software for data center automation
        386
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        PROS OF SALT
        • 47
          Flexible
        • 30
          Easy
        • 27
          Remote execution
        • 24
          Enormously flexible
        • 12
          Great plugin API
        • 9
          Python
        • 5
          Extensible
        • 3
          Scalable
        • 2
          nginx
        • 1
          Vagrant provisioner
        • 1
          HipChat
        • 1
          Best IaaC
        • 1
          Automatisation
        • 1
          Parallel Execution
        CONS OF SALT
        • 1
          Bloated
        • 1
          Dangerous
        • 1
          No immutable infrastructure

        related Salt posts

        Shared insights
        on
        Salt
        Puppet Labs
        Ansible
        at

        By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.

        “The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”

        To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.

        They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.

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        AWS OpsWorks logo

        AWS OpsWorks

        205
        188
        51
        Model and manage your entire application from load balancers to databases using Chef
        205
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        PROS OF AWS OPSWORKS
        • 32
          Devops
        • 19
          Cloud management
        CONS OF AWS OPSWORKS
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          related AWS OpsWorks posts

          EasyEngine logo

          EasyEngine

          203
          35
          0
          A command line tool to easily manage WordPress sites
          203
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          + 1
          0
          PROS OF EASYENGINE
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            CONS OF EASYENGINE
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              related EasyEngine posts

              Rundeck logo

              Rundeck

              159
              242
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              A platform for Self-Service Operations
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              PROS OF RUNDECK
              • 3
                Easy to understand
              • 3
                Role based access control
              • 1
                Doesn't need containers
              CONS OF RUNDECK
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                related Rundeck posts