Alternatives to AWS OpsWorks logo

Alternatives to AWS OpsWorks

Chef, AWS Elastic Beanstalk, AWS Config, AWS CloudFormation, and AWS CodeDeploy are the most popular alternatives and competitors to AWS OpsWorks.
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What is AWS OpsWorks and what are its top alternatives?

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
AWS OpsWorks is a tool in the Server Configuration and Automation category of a tech stack.

Top Alternatives to AWS OpsWorks

  • 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. ...

  • AWS Elastic Beanstalk

    AWS Elastic Beanstalk

    Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. ...

  • AWS Config

    AWS Config

    AWS Config is a fully managed service that provides you with an AWS resource inventory, configuration history, and configuration change notifications to enable security and governance. With AWS Config you can discover existing AWS resources, export a complete inventory of your AWS resources with all configuration details, and determine how a resource was configured at any point in time. These capabilities enable compliance auditing, security analysis, resource change tracking, and troubleshooting. ...

  • AWS CloudFormation

    AWS CloudFormation

    You can use AWS CloudFormation鈥檚 sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don鈥檛 need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work. ...

  • AWS CodeDeploy

    AWS CodeDeploy

    AWS CodeDeploy is a service that automates code deployments to Amazon EC2 instances. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during deployment, and handles the complexity of updating your applications. ...

  • Beanstalk

    Beanstalk

    A single process to commit code, review with the team, and deploy the final result to your customers. ...

  • 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鈥檚 goals are foremost those of simplicity and maximum ease of use. ...

  • Terraform

    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

AWS OpsWorks alternatives & related posts

Chef logo

Chef

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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
    Be the first to leave a con

    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
    AWS Elastic Beanstalk logo

    AWS Elastic Beanstalk

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    Quickly deploy and manage applications in the AWS cloud.
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    PROS OF AWS ELASTIC BEANSTALK
    • 77
      Integrates with other aws services
    • 65
      Simple deployment
    • 44
      Fast
    • 28
      Painless
    • 16
      Free
    • 3
      Independend app container
    • 3
      Well-documented
    • 2
      Postgres hosting
    • 2
      Ability to be customized
    CONS OF AWS ELASTIC BEANSTALK
    • 2
      Charges appear automatically after exceeding free quota
    • 0
      Slow deployments

    related AWS Elastic Beanstalk posts

    Julien DeFrance
    Principal Software Engineer at Tophatter | 16 upvotes 路 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.

    See more

    We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

    We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

    In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

    Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

    See more
    AWS Config logo

    AWS Config

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    Config gives you a detailed inventory of your AWS resources and their current configuration, and continuously records configuration...
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    PROS OF AWS CONFIG
    • 4
      Backed by Amazon
    • 2
      One stop solution
    CONS OF AWS CONFIG
      Be the first to leave a con

      related AWS Config posts

      AWS CloudFormation logo

      AWS CloudFormation

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      Create and manage a collection of related AWS resources
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      PROS OF AWS CLOUDFORMATION
      • 42
        Automates infrastructure deployments
      • 21
        Declarative infrastructure and deployment
      • 13
        No more clicking around
      • 3
        Any Operative System you want
      • 3
        Infrastructure as code
      • 3
        Atomic
      • 1
        Automates Infrastructure Deployment
      • 1
        CDK makes it truly infrastructure-as-code
      CONS OF AWS CLOUDFORMATION
      • 3
        Brittle
      • 1
        No RBAC and policies in templates

      related AWS CloudFormation posts

      Joseph Kunzler
      DevOps Engineer at Tillable | 9 upvotes 路 128.5K views

      We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.

      See more

      I use Terraform because it hits the level of abstraction pocket of being high-level and flexible, and is agnostic to cloud platforms. Creating complex infrastructure components for a solution with a UI console is tedious to repeat. Using low-level APIs are usually specific to cloud platforms, and you still have to build your own tooling for deploying, state management, and destroying infrastructure.

      However, Terraform is usually slower to implement new services compared to cloud-specific APIs. It's worth the trade-off though, especially if you're multi-cloud. I heard someone say, "We want to preference a cloud, not lock in to one." Terraform builds on that claim.

      Terraform Google Cloud Deployment Manager AWS CloudFormation

      See more
      AWS CodeDeploy logo

      AWS CodeDeploy

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      Coordinate application deployments to Amazon EC2 instances
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      PROS OF AWS CODEDEPLOY
      • 17
        Automates code deployments
      • 9
        Backed by Amazon
      • 7
        Adds autoscaling lifecycle hooks
      • 5
        Git integration
      CONS OF AWS CODEDEPLOY
        Be the first to leave a con

        related AWS CodeDeploy posts

        Chris McFadden
        VP, Engineering at SparkPost | 9 upvotes 路 125.9K views

        The recent move of our CI/CD tooling to AWS CodeBuild / AWS CodeDeploy (with GitHub ) as well as moving to Amazon EC2 Container Service / AWS Lambda for our deployment architecture for most of our services has helped us significantly reduce our deployment times while improving both feature velocity and overall reliability. In one extreme case, we got one service down from 90 minutes to a very reasonable 15 minutes. Container-based build and deployments have made so many things simpler and easier and the integration between the tools has been helpful. There is still some work to do on our service mesh & API proxy approach to further simplify our environment.

        See more
        Sathish Raju
        Founder/CTO at Kloudio | 5 upvotes 路 60.8K views

        At Kloud.io we use Node.js for our backend Microservices and Angular 2 for the frontend. We also use React for a couple of our internal applications. Writing services in Node.js in TypeScript improved developer productivity and we could capture bugs way before they can occur in the production. The use of Angular 2 in our production environment reduced the time to release any new features. At the same time, we are also exploring React by using it in our internal tools. So far we enjoyed what React has to offer. We are an enterprise SAAS product and also offer an on-premise or hybrid cloud version of #kloudio. We heavily use Docker for shipping our on-premise version. We also use Docker internally for automated testing. Using Docker reduced the install time errors in customer environments. Our cloud version is deployed in #AWS. We use AWS CodePipeline and AWS CodeDeploy for our CI/CD. We also use AWS Lambda for automation jobs.

        See more
        Beanstalk logo

        Beanstalk

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        Private code hosting for teams.
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        PROS OF BEANSTALK
        • 14
          Ftp deploy
        • 9
          Deployment
        • 8
          Easy to navigate
        • 4
          Code Editing
        • 4
          HipChat Integration
        • 4
          Integrations
        • 3
          Code review
        • 2
          HTML Preview
        • 1
          Security
        • 1
          Blame Tool
        • 1
          Cohesion
        CONS OF BEANSTALK
          Be the first to leave a con

          related Beanstalk posts

          Ansible logo

          Ansible

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          Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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          PROS OF ANSIBLE
          • 275
            Agentless
          • 204
            Great configuration
          • 192
            Simple
          • 173
            Powerful
          • 150
            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
            Cloud Oriented
          • 6
            Easy to maintain
          • 4
            Because SSH
          • 4
            Multi language
          • 4
            Easy
          • 4
            Simple
          • 4
            Procedural or declarative, or both
          • 4
            Simple and powerful
          • 3
            Vagrant provisioner
          • 3
            Consistency
          • 2
            Debugging is simple
          • 2
            Well-documented
          • 2
            Merge hash to get final configuration similar to hiera
          • 2
            Fast as hell
          • 2
            Masterless
          • 1
            Work on windows, but difficult to manage
          CONS OF ANSIBLE
          • 5
            Hard to install
          • 4
            Dangerous
          • 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 | 21 upvotes 路 4.3M 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.

          See more
          Sebastian G臋bski

          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.

          See more
          Terraform logo

          Terraform

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          Describe your complete infrastructure as code and build resources across providers
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          PROS OF TERRAFORM
          • 103
            Infrastructure as code
          • 71
            Declarative syntax
          • 43
            Planning
          • 26
            Simple
          • 23
            Parallelism
          • 6
            Cloud agnostic
          • 5
            It's like coding your infrastructure in simple English
          • 4
            Well-documented
          • 3
            Automates infrastructure deployments
          • 3
            Platform agnostic
          • 3
            Immutable infrastructure
          • 2
            Automation
          • 2
            Portability
          • 2
            Scales to hundreds of hosts
          • 2
            Extendable
          • 1
            Lightweight
          CONS OF TERRAFORM
          • 1
            Doesn't have full support to GKE

          related Terraform posts

          Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

          Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

          Check Out My Architecture: CLICK ME

          Check out the GitHub repo attached

          See more
          Praveen Mooli
          Engineering Manager at Taylor and Francis | 14 upvotes 路 1.8M views

          We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

          To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

          To build #Webapps we decided to use Angular 2 with RxJS

          #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

          See more