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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 4 Cloud Platforms to Host or Run Docker Containers for Free

Ajay
Ajay Last Updated : 25 Oct, 2024
8 min read

Introduction

In recent years, containerization has become more popular and widely used by software developers. Docker is still considered one of the top tools for creating containers by building Images between containerization platforms or cloud platforms. Containerizing is all about bundling up a software application/service and isolating it from the host environment to run everywhere, be it an AMD or an ARM. Docker Cloud hosting has made it simple to bundle and share containers.

There will be situations where one wants to host and run these containers online in the cloud. For example, containerizing a webpage app and running it in the cloud to be shared and visible to everyone. Many cloud providers allow you to build containers online for the given application and run it continuously. Alternatively, they allow the user to input the Image file directly and create a container. Let’s discuss the free platforms to deploy and run these Docker containers.

Learning Objectives

  • Discover options for deploying Docker containers online without cost, with a focus on platforms that provide free hosting for developers seeking affordable solutions.
  • Compare leading Docker hosting providers, understanding their free-tier offerings and pricing structures and how they support containerized applications in the cloud.
  • Find the best cloud platforms to host or run Docker containers. Each offers unique features for reliable, scalable deployment without upfront costs.
  • Dive into options that enable you to run Docker containers online for free. This makes it easier to share applications across environments without payment or personal information requirements.
  • Learn which platforms, such as Zeet, Fly.io, Render, and Railway, are ideal for running Docker containers on a budget. These platforms offer flexible, production-ready environments with free deployment options.

This article was published as a part of the Data Science Blogathon.

Table of contents

End of Heroku

Hosting containerized applications on the cloud is the best way to share them with others and understand how well they can or must be scaled to make them available to millions of users with no connection lags. Many cloud platforms let you host your Docker containers/applications. However, most platforms are not free, and users are asked for their personal information, such as credit card details. Once, there was a platform called Heroku, which provided free application hosting in the cloud.

Heroku was famous among developers for its free tier and ease of use. But recently, Heroku removed its free tier, and now the time has come for developers to search for its next successor. This article gives you information on four platforms that help you host containerized applications for free on the cloud without charging anything or asking for personal information.

Zeet

Zeet is a cloud platform for deploying DevOps-operated, production-ready cloud services. With Zeet, any programmer can publish code on production-grade infrastructure, a software platform developed on top of one’s AWS or GCP, thus eliminating complex infrastructure management. It, by default, supports many programming languages like Python, Node, etc.

Zeet allows its users to deploy apps/websites using Dock Containers, which are run in a cluster managed by Zeet. The Docker Image can be directly provided to Zeet for deployment. Zeet is compatible with a range of programming languages.

Zeet has a free plan for developers. The following are included in this free plan:

  1. One can deploy three applications by providing the Images under the free tier in Zeet
  2. Zeet allows the user to deploy the container in one area
  3. Zeet allows the user to deploy the container to either AWS/GCP/DO
  4. The platform provides scalable architecture and serverless functions
  5. The free tier provides up to 500MB of RAM

The following are its disadvantages/limitations:

  1. The app can be deployed only to one cloud region
  2. No retention of log and metric data
  3. API access is not accessible in the free tier

Zeet is being used by Mythia as a replacement for AWS, thus saving hundreds of development hours. The Fairgrounds company utilizing Zeet is trying to build an entire Metaverse, and the Paper company employs Zeet for checkout solutions.

Fly.io

Fly.io is a new public cloud that makes it simple to deploy/publish real-time apps close to target users, regardless of whether they might be present in the world. It is based on bare-metal servers that operate in data centres all over the world. Fly.io’s main motto is to deliver apps to end users with high availability and low latency.

Fly.io doesn’t have a free plan or free tier in its pricing section, but it allows users to provide enough resources to run small full-stack websites/applications for free. One can host Dock Containers on this website.

Fly.io takes in the application and its Dockerfile, which the user wants to host. Then, based on the Dockerfile, it builds the image and hosts the following container. The user can provide the pre-built Docker Image if it already exists. The free tier includes the following:

  1. The developer can create up to 3 shared-CPU-1x VM instances.
  2. All the launched VM Instances come with 256MB RAM.
  3. A 3 GB disk space, i.e., persistence volume, is provided and shared between all apps.
  4. Allows up to 160GB of outbound data transfer.

The free tier of fly.io includes the following limitations/disadvantages:

  1. It provides the least amount of RAM compared to all the platforms in this article.
  2. The outbound data transfer varies from region to region.
  3. Only the first ten single hostname certificates are free.

Many websites, such as reactrouter.com, kentcdodds.com, unpkg.com, and aiven.io, host their websites on fly.io, which makes them highly available to users worldwide.

Docker Containers

Before working with fly.io, one needs to install their command line tool called flyctl in the host operating system, and it can be done as follows:

# Installing flyctl in windows
$ powershell -Command "iwr https://fly.io/install.ps1 -useb | iex"

# Installing flyctl in Linux
$ curl -L https://fly.io/install.sh | sh

# Installing flyctl in Mac
$ brew install flyctl

Render

Render is a fully managed seamless cloud platform for running an app or website. It provides a free TLS certificate, private network, and global CDN. Render offers various features, like managed databases, one-click deployments, automated scaling, and an easy-to-use UI. It also allows easy integration with development tools like Docker Containers and GitHub. Render provides native support for hosting containerized apps and services at scale.

Pricing

Render has a free tier plan, which is a free tier plan that increases and complex computations can then be upgraded. The free tier plan includes the following:

  1. Free 100GB egress bandwidth
  2. A shared CPU with 512MB RAM
  3. Users can run their application for free for 500 hours that renews monthly
  4. Render has support for auto CD(Continuous Deployment) from Git
  5. Custom domains with TLS for static websites
  6. Free 750hrs runtime that renews monthly for all the web services
  7. Free Postgres database
  8. Static websites have support for DDoS Protection, HTTP/2 out-of-the-box

Disadvantages of using Render

Disadvantages of using Render include:

  1. SSH access to the web services is not supported in the free plan
  2. The PostgreSQL database expires after 90 days
  3. Web Services may restart at any time
  4. No free instances for Private Services
  5. Free Redis instances can be restarted at any time

Companies like Indie Hackers, Pete for America, Zelos, etc., have chosen Render over AWS for its simplicity. It eliminates a lot of infrastructure complexity and helps them with automatic builds and zero downtime deployment.

Railway

The Railway is a cloud-based deployment platform that enables users to publish and host applications in the cloud. The railway manages these servers and databases, thus caring for the infrastructure. It supports various languages, including Python, Next.js, Node, and many others.

One of its key features is its support for Docker. During the build process, the Railway app looks for a Dockerfile to build an image for the application based on its configuration and run the following container in the cloud.

The railway comes with a free-tier plan, which includes the following:

  1. A 512 MB RAM and a shared CPU/container
  2. 1GB disk space is shared between the services created
  3. $5 credit and 500 hours of usage that get renewed monthly
  4. Upto 100GB data transfer that gets renewed monthly

Disadvantages of Railway

Disadvantages/limitations associated with this cloud platform include:

  1. Unlimited execution time is not provided in the free tier
  2. Shared projects are not supported in the individual plan
  3. Least disk space, i.e., 1GB, among the above platforms

Railway is employed by companies like Atmos, which deals with real estate home customization, and Zora, a company related to NFTs. Fion Tech provides Machine Learning models for fire prediction, Operand, and many more.

Docker Containers

Deploying a Flask Application

Let’s try deploying a simple Flask application to one of the platforms above. Here, we will deploy it to the Render platform. Let’s look at the sample code and the Dockerfile.

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello Flask App'

if __name__ == "__main__":
    app.run(debug=True)

The above code will start a flask server and display the message “Hello Flask App.” Below is the Dockerfile, which will help build an Image for the simple Flask app. Upload both of these files to a GitHub Repository.

FROM python:3.10-slim

WORKDIR /flask_app

COPY . .

RUN pip install -r requirements.txt
ENV FLASK_DEBUG=1
EXPOSE 5000

CMD ["flask","run","--host=0.0.0.0"]
  1. The first thing we must do now is sign up for Render using your GitHub account.
  2. To host a Flask application, click the New Web Services tab.
  3. Now, in the Public Git repository, add the link to your git repository that contains the Flask app and the Dockerfile. After pasting it, click the Continue button. Another page will appear.
  4. We will name our web service simple-flask-app; the region and the branch come by default. As we are providing a Dockerfile to build an Image for our flask app, the Runtime is populated as Docker by default. Now scroll down and press the Create Web Service button to create this new service.
  5. Now, Render will start building our simple flask application. It first searches for a Dockerfile. Based on the Dockerfile, Render will create and build an Image based on the Dockerfile and then deploy it to the Renders Server. The link for the website is below the simple-flask app. When opening the link, we get to see the following:
Docker Containers

Thus, the app is working perfectly. This way, one can create an application and then create a respective Dockerfile, which Render uses to build an Image out of it and run it on the cloud.

Conclusion

Creating an application is one task, and deploying that on the cloud as a container is another. Most of the cloud platforms do not support Docker/containerization. They don’t usually have free tiers/plans, even if they support them. Cloud services like AWS/GCP allow you to deploy these Containers but ask the user to enter their credit card details. So this article focuses on those websites/cloud services that support containerization and allow users to host them for free without providing a credit card and personal information.

Some of the key takeaways from this article include the following:

  1. Fly.io is the best option if one wants applications to run as edge applications, i.e., closer to the end users
  2. At Zeet, the user can directly search for the Docker Image in the Docker Hub and deploy it. At
  3. Render, by default, comes with many security features, even in the free tier plan, with zero downtime deployments.
  4. Railway provides the user with a free $5 credit and comes with a good UI.

Frequently Asked Questions

Q1. Where can I run Docker containers for free?

A. You can run Docker containers for free on cloud platforms like Zeet, Fly.io, Render, and Railway. These platforms provide free tiers for developers, allowing Docker container hosting without payment information. Each platform offers unique features, from edge computing to serverless infrastructure and built-in security measures.

Q2. What is the best platform to run Docker on?

A. The best platform to run Docker depends on your requirements. Render is highly recommended for security and seamless deployments, while Fly.io offers edge hosting for low latency. Zeet provides developer-friendly serverless options, and Railway has a straightforward UI with a free $5 credit for new users.

Q3. What are the 4 states of a Docker container?

A. Docker containers can be in one of four states: created, running, paused, or stopped. Created containers have been initialized but not started. Running containers actively perform tasks, while paused containers are temporarily inactive. Stopped containers have finished or ceased execution and await further commands.

Q4. How many containers can Docker run?

A. Docker can run multiple containers, limited by the system’s resources such as CPU and RAM. Technically, there’s no hard limit, but the maximum number depends on the server’s capabilities. Docker’s lightweight nature allows efficient scaling, so systems can often run hundreds of containers simultaneously.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

Ajay
Ajay

I work as a Developer in the field of Data Science. I constantly spend time learning new things be it related to AI, DataSceine, and CyberSecurity. Deep learning and machine learning are two topics that I find particularly fascinating, and Python is my preferred language for programming. Cyber Security is another field that I'm touching upon recently. I have experience with large-scale data analysis, and I have a solid grasp of a variety of deep learning and machine learning approaches, including neural networks, regression models, and natural language processing. I'm eager to take on new challenges and make a meaningful contribution to the industry, so I'm constantly seeking for ways to enlarge and deepen my knowledge and skills in the subject.

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