Alternatives to Bokeh logo

Alternatives to Bokeh

Plotly.js, Matplotlib, Dash, D3.js, and Tableau are the most popular alternatives and competitors to Bokeh.
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What is Bokeh and what are its top alternatives?

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.
Bokeh is a tool in the Charting Libraries category of a tech stack.
Bokeh is an open source tool with 19K GitHub stars and 4.2K GitHub forks. Here’s a link to Bokeh's open source repository on GitHub

Top Alternatives to Bokeh

  • Plotly.js
    Plotly.js

    It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more. ...

  • Matplotlib
    Matplotlib

    It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. ...

  • Dash
    Dash

    Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included. ...

  • D3.js
    D3.js

    It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework. ...

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • Shiny
    Shiny

    It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. ...

  • JavaScript
    JavaScript

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

  • Git
    Git

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

Bokeh alternatives & related posts

Plotly.js logo

Plotly.js

353
691
69
A high-level, declarative charting library
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691
+ 1
69
PROS OF PLOTLY.JS
  • 16
    Bindings to popular languages like Python, Node, R, etc
  • 10
    Integrated zoom and filter-out tools in charts and maps
  • 9
    Great support for complex and multiple axes
  • 8
    Powerful out-of-the-box featureset
  • 6
    Beautiful visualizations
  • 4
    Active user base
  • 4
    Impressive support for webgl 3D charts
  • 3
    Charts are easy to share with a cloud account
  • 3
    Webgl chart types are extremely performant
  • 2
    Interactive charts
  • 2
    Easy to use online editor for creating plotly.js charts
  • 2
    Publication quality image export
CONS OF PLOTLY.JS
  • 18
    Terrible document

related Plotly.js posts

Tim Abbott
Shared insights
on
Plotly.jsPlotly.jsD3.jsD3.js
at

We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.

If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.

See more

Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.

See more
Matplotlib logo

Matplotlib

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A plotting library for the Python programming language
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PROS OF MATPLOTLIB
  • 10
    The standard Swiss Army Knife of plotting
CONS OF MATPLOTLIB
  • 5
    Lots of code

related Matplotlib posts

Shared insights
on
MatplotlibMatplotlibBokehBokehDjangoDjango

Hi - I am looking to develop an app accessed by a browser that will display interactive networks (including adding or deleting nodes, edges, labels (or changing labels) based on user input. Look to use Django at the backend. Also need to manage graph versions if one person makes a graph change while another person is looking at it. Mainly tree networks for starters anyway. I probably will use the Networkx package. Not sure what the pros and cons are using Bokeh vs Matplotlib. I would be grateful for any comments or suggestions. Thanks.

See more
Dash logo

Dash

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409
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Gives your Mac instant offline access to 150+ API documentation sets
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PROS OF DASH
  • 17
    Dozens of API docs and Cheat-Sheets
  • 12
    Great for offline use
  • 8
    Works with Alfred
  • 8
    Excellent documentation
  • 8
    Quick API search
  • 5
    Fast
  • 3
    Good integration with Xcode and AppCode
  • 2
    Great for mobile dev work
CONS OF DASH
    Be the first to leave a con

    related Dash posts

    My company wants to make some relatively small, self-contained web apps to go through specific engineering analysis workflows.

    Each app would involve:

    (a) User inputs numbers and tabular data either in a table or from a csv import

    (b) App makes plots of this data

    (c) App performs calculations based on user input and outputs results as either plots or numbers or tabular data

    It seems like there must be zillions of applications where people want these things, so I want a 'low code' approach that already handles a bunch of details so we don't have to. Experience in the past with Angular has involved, in my experience, a lot of low-level coding to 'reinvent the wheel', creating capabilities (like menus to control plotting options like font size) that I'd expect to be very common.

    Specific wants:

    (a) Plotting capabilities with prebuilt convenient plotting controls

    (b) Ability to 'save' and 'load' (as in, you do the analysis and get results and want to save so that you can reopen this save environment with the data and analysis, as if you'd never closed it)

    (c) For specific components, ability to swap out the built-in components with a customized plot/widget.

    For example, with (c), we might have a situation where we do want to make a custom plot or tool, and would like to be able to drop that into the general application

    Question is - does something exist that does what I am describing? What would you recommend? On our list to check out: Microsoft PowerApps , Dash , UI Bakery, Retool , Tibco Spotfire , Outsystems, Zoho, Creatio, or any other suggestions.

    Other considerations:

    (a) How easy are these apps to maintain (i.e., do they frequently make non back compatible, breaking updates, like they do with Angular)

    (b) Need excellent security so I can deploy web apps for large companies

    (c) General ease of use (would like to be efficient with developer time).

    See more
    D3.js logo

    D3.js

    1.8K
    1.7K
    653
    A JavaScript visualization library for HTML and SVG
    1.8K
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    PROS OF D3.JS
    • 195
      Beautiful visualizations
    • 103
      Svg
    • 92
      Data-driven
    • 81
      Large set of examples
    • 61
      Data-driven documents
    • 24
      Visualization components
    • 20
      Transitions
    • 18
      Dynamic properties
    • 16
      Plugins
    • 11
      Transformation
    • 7
      Makes data interactive
    • 4
      Open Source
    • 4
      Enter and Exit
    • 4
      Components
    • 3
      Exhaustive
    • 3
      Backed by the new york times
    • 2
      Easy and beautiful
    • 1
      Highly customizable
    • 1
      Awesome Community Support
    • 1
      Simple elegance
    • 1
      Templates, force template
    • 1
      Angular 4
    CONS OF D3.JS
    • 11
      Beginners cant understand at all
    • 6
      Complex syntax

    related D3.js posts

    Tim Abbott
    Shared insights
    on
    Plotly.jsPlotly.jsD3.jsD3.js
    at

    We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.

    If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.

    See more
    Amit Garg
    Shared insights
    on
    D3.jsD3.jsApexChartsApexChartsReactReact

    Hi,

    I am looking at integrating a charting library in my React frontend that allows me to create appealing and interactive charts. I have basic familiarity with ApexCharts with React but have also read about D3.js charts and it seems a much more involved integration. Can someone please share their experience across the two libraries on the following dimensions:

    1. Amount of work needed for integration
    2. Amount of work or ease for creating new charts in either of the libraries.

    Regards

    Amit

    See more
    Tableau logo

    Tableau

    1.3K
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    Tableau helps people see and understand data.
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    PROS OF TABLEAU
    • 6
      Capable of visualising billions of rows
    • 1
      Intuitive and easy to learn
    • 1
      Responsive
    CONS OF TABLEAU
    • 2
      Very expensive for small companies

    related Tableau posts

    Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

    See more
    Shared insights
    on
    TableauTableauQlikQlikPowerBIPowerBI

    Hello everyone,

    My team and I are currently in the process of selecting a Business Intelligence (BI) tool for our actively developing company, which has over 500 employees. We are considering open-source options.

    We are keen to connect with a Head of Analytics or BI Analytics professional who has extensive experience working with any of these systems and is willing to share their insights. Ideally, we would like to speak with someone from companies that have transitioned from proprietary BI tools (such as PowerBI, Qlik, or Tableau) to open-source BI tools, or vice versa.

    If you have any contacts or recommendations for individuals we could reach out to regarding this matter, we would greatly appreciate it. Additionally, if you are personally willing to share your experiences, please feel free to reach out to me directly. Thank you!

    See more
    Shiny logo

    Shiny

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    An R package that makes it easy to build interactive web apps
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    PROS OF SHINY
    • 8
      R Compatibility
    • 3
      Free
    • 2
      Highly customizable and extensible
    CONS OF SHINY
      Be the first to leave a con

      related Shiny posts

      We have decided to make use of R for ML and Shiny for UI. We are debating usage of self hosted shiny server v/s shinyapp . Our Decision to go with R was to do with Sizes of data and availability of tools. R Shiny

      See more
      JavaScript logo

      JavaScript

      352.7K
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      Lightweight, interpreted, object-oriented language with first-class functions
      352.7K
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      PROS OF JAVASCRIPT
      • 1.7K
        Can be used on frontend/backend
      • 1.5K
        It's everywhere
      • 1.2K
        Lots of great frameworks
      • 897
        Fast
      • 745
        Light weight
      • 425
        Flexible
      • 392
        You can't get a device today that doesn't run js
      • 286
        Non-blocking i/o
      • 237
        Ubiquitousness
      • 191
        Expressive
      • 55
        Extended functionality to web pages
      • 49
        Relatively easy language
      • 46
        Executed on the client side
      • 30
        Relatively fast to the end user
      • 25
        Pure Javascript
      • 21
        Functional programming
      • 15
        Async
      • 13
        Full-stack
      • 12
        Setup is easy
      • 12
        Future Language of The Web
      • 12
        Its everywhere
      • 11
        Because I love functions
      • 11
        JavaScript is the New PHP
      • 10
        Like it or not, JS is part of the web standard
      • 9
        Expansive community
      • 9
        Everyone use it
      • 9
        Can be used in backend, frontend and DB
      • 9
        Easy
      • 8
        Most Popular Language in the World
      • 8
        Powerful
      • 8
        Can be used both as frontend and backend as well
      • 8
        For the good parts
      • 8
        No need to use PHP
      • 8
        Easy to hire developers
      • 7
        Agile, packages simple to use
      • 7
        Love-hate relationship
      • 7
        Photoshop has 3 JS runtimes built in
      • 7
        Evolution of C
      • 7
        It's fun
      • 7
        Hard not to use
      • 7
        Versitile
      • 7
        Its fun and fast
      • 7
        Nice
      • 7
        Popularized Class-Less Architecture & Lambdas
      • 7
        Supports lambdas and closures
      • 6
        It let's me use Babel & Typescript
      • 6
        Can be used on frontend/backend/Mobile/create PRO Ui
      • 6
        1.6K Can be used on frontend/backend
      • 6
        Client side JS uses the visitors CPU to save Server Res
      • 6
        Easy to make something
      • 5
        Clojurescript
      • 5
        Promise relationship
      • 5
        Stockholm Syndrome
      • 5
        Function expressions are useful for callbacks
      • 5
        Scope manipulation
      • 5
        Everywhere
      • 5
        Client processing
      • 5
        What to add
      • 4
        Because it is so simple and lightweight
      • 4
        Only Programming language on browser
      • 1
        Test
      • 1
        Hard to learn
      • 1
        Test2
      • 1
        Not the best
      • 1
        Easy to understand
      • 1
        Subskill #4
      • 1
        Easy to learn
      • 0
        Hard 彤
      CONS OF JAVASCRIPT
      • 22
        A constant moving target, too much churn
      • 20
        Horribly inconsistent
      • 15
        Javascript is the New PHP
      • 9
        No ability to monitor memory utilitization
      • 8
        Shows Zero output in case of ANY error
      • 7
        Thinks strange results are better than errors
      • 6
        Can be ugly
      • 3
        No GitHub
      • 2
        Slow

      related JavaScript posts

      Zach Holman

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

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

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

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

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

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

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

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

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

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

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

      See more
      Git logo

      Git

      291.7K
      175K
      6.6K
      Fast, scalable, distributed revision control system
      291.7K
      175K
      + 1
      6.6K
      PROS OF GIT
      • 1.4K
        Distributed version control system
      • 1.1K
        Efficient branching and merging
      • 959
        Fast
      • 845
        Open source
      • 726
        Better than svn
      • 368
        Great command-line application
      • 306
        Simple
      • 291
        Free
      • 232
        Easy to use
      • 222
        Does not require server
      • 27
        Distributed
      • 22
        Small & Fast
      • 18
        Feature based workflow
      • 15
        Staging Area
      • 13
        Most wide-spread VSC
      • 11
        Role-based codelines
      • 11
        Disposable Experimentation
      • 7
        Frictionless Context Switching
      • 6
        Data Assurance
      • 5
        Efficient
      • 4
        Just awesome
      • 3
        Github integration
      • 3
        Easy branching and merging
      • 2
        Compatible
      • 2
        Flexible
      • 2
        Possible to lose history and commits
      • 1
        Rebase supported natively; reflog; access to plumbing
      • 1
        Light
      • 1
        Team Integration
      • 1
        Fast, scalable, distributed revision control system
      • 1
        Easy
      • 1
        Flexible, easy, Safe, and fast
      • 1
        CLI is great, but the GUI tools are awesome
      • 1
        It's what you do
      • 0
        Phinx
      CONS OF GIT
      • 16
        Hard to learn
      • 11
        Inconsistent command line interface
      • 9
        Easy to lose uncommitted work
      • 7
        Worst documentation ever possibly made
      • 5
        Awful merge handling
      • 3
        Unexistent preventive security flows
      • 3
        Rebase hell
      • 2
        When --force is disabled, cannot rebase
      • 2
        Ironically even die-hard supporters screw up badly
      • 1
        Doesn't scale for big data

      related Git posts

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

      Our whole DevOps stack consists of the following tools:

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

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

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

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

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

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

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

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

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

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

      See more