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https://github.com/mxagar/webapp_deployment_guide

A guide on how to deploy simple web apps on several cloud services.
https://github.com/mxagar/webapp_deployment_guide

api aws azure backend blob-storage deployment docker flask frontend hetzner postgresql railway render terraform webapp

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A guide on how to deploy simple web apps on several cloud services.

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# Simple Web App Deployment Guide



A guide on how to deploy simple web apps on several cloud services.



## Goal



Create a small but realistic web application project for learning deployment concepts.



The focus is **not** machine learning infrastructure. The focus is deploying a conventional app that includes the resources many real applications need:



* a Python backend with Flask

* a simple frontend with HTML, CSS, and JavaScript

* a PostgreSQL database

* blob/object storage

* an API built with Flask

* containerization with Docker

* local orchestration with Docker Compose

* CI/CD for linting, tests, and deployment

* separate staging and production environments



ML functionality, if present, is treated as just another internal application feature.



## Project Scope



This project is meant to teach the deployment and operations fundamentals behind a typical small-to-medium web application.



It should help answer questions like:



* How do I package an app in Docker?

* How do I run it locally with its dependencies?

* How do I deploy it to a managed platform or VPS?

* How do I separate staging from production?

* How do I handle database access, file storage, authentication, and background work?

* How do I set up a basic CI/CD pipeline?



This architecture is representative of a large class of internal tools, SaaS products, dashboards, CRUD systems, admin portals, and API-backed web apps.



## Functional Components



### 1. Backend



The backend is a Flask application written in Python.



Responsibilities:



* serve server-side pages or API endpoints

* implement business logic

* connect to PostgreSQL

* manage authentication

* interact with blob storage

* expose HTTP endpoints for the frontend and other clients



### 2. Frontend



The frontend is intentionally simple:



* HTML

* CSS

* JavaScript



It can either:



* be served by Flask templates, or

* be built as static assets and served through the same app or reverse proxy



The goal is not frontend complexity, but deployment clarity.



### 3. SQL Database



Use PostgreSQL as the relational database.



Responsibilities:



* store users

* store business entities

* store application state

* store metadata for uploaded files

* store job state if needed



### 4. Blob/Object Storage



Use blob/object storage for large files and binary assets.



Examples:



* uploaded documents

* images

* exports

* media files

* optional model artifacts



Important principle:



* structured relational data goes into PostgreSQL

* large binary files go into object storage



### 5. API



The Flask backend also exposes an API.



Examples:



* authentication endpoints

* CRUD endpoints

* file upload/download endpoints

* status endpoints



This keeps the architecture simple: one backend, one app, multiple interfaces.



## Recommended Minimal Architecture



### Version 1: Minimal but realistic



This is the recommended first version.



Components:



* **Flask** application

* **Gunicorn** as the WSGI server

* **Caddy** as reverse proxy

* **PostgreSQL**

* **Object storage**

* **Flask-Login** for authentication

* **Dockerfile** for packaging

* **docker-compose.yaml** for local development

* **GitHub Actions** for CI/CD



Conceptual flow:



```text

Browser

  -> Caddy

  -> Gunicorn

  -> Flask app

  -> PostgreSQL

  -> Object storage

```



### Why these choices?



#### Caddy as reverse proxy



Caddy is a good learning choice because it is simpler than Nginx and handles HTTPS very smoothly.



Responsibilities:



* terminates HTTPS

* forwards requests to Gunicorn/Flask

* can serve static files

* can manage headers and routing



#### Gunicorn as app server



Gunicorn runs the Flask app in a production-ready way.



Responsibilities:



* runs Flask workers

* handles incoming requests from the reverse proxy



#### Flask-Login for auth



Flask-Login is a good fit for a classic web app with sessions, login/logout, and protected pages.



## Extended Architecture



### Version 2: Add realism gradually



Once the minimal version works, add:



* **Redis**

* **RQ** for background jobs

* optional caching through Redis

* basic monitoring and error tracking



Conceptual flow:



```text

Browser

  -> Caddy

  -> Gunicorn

  -> Flask app

      -> PostgreSQL

      -> Object storage

      -> Redis

  -> RQ worker

      -> Redis

      -> PostgreSQL

      -> Object storage

```



### Why Redis?



Redis is useful once the application needs:



* caching

* background jobs

* temporary shared state

* rate limiting

* session-related extensions



### Why RQ?



RQ is a simple Python background job system backed by Redis.



It is a good learning option because it is easier to understand than heavier alternatives.



Typical uses:



* processing uploaded files

* sending emails

* generating reports or PDFs

* slow external API calls

* long-running tasks that should not block web requests



## Environments: Local, Staging, Production



A key part of deployment learning is understanding that the same app usually runs in multiple environments.



### 1. Local development



Purpose:



* code locally

* run the full stack on your machine

* iterate quickly



Typical setup:



* Docker Compose

* Flask app

* PostgreSQL container

* optional Redis container

* local object storage emulator or mocked/external storage



Characteristics:



* fastest feedback loop

* debug-friendly

* not publicly exposed

* developer-oriented configuration



### 2. Staging



Purpose:



* environment for pre-production validation

* test deployment workflows

* verify migrations

* test integrations and secrets handling

* review features before production



Characteristics:



* should resemble production closely

* uses separate database and storage

* may use smaller resources than production

* can be deployed automatically from a develop or staging branch



Typical uses:



* manual QA

* integration testing

* smoke tests after deployment

* validating infrastructure changes



### 3. Production



Purpose:



* serve real users

* run the stable version of the system



Characteristics:



* real traffic

* real credentials and secrets

* separate database and storage from staging

* stronger monitoring and backup requirements

* stricter access control



## Why staging matters



Without staging, every deployment is effectively a production experiment.



Staging helps you validate:



* infrastructure configuration

* environment variables

* reverse proxy setup

* database migrations

* authentication flows

* file upload/download behavior

* CI/CD deployment logic



It also teaches an important operational principle:



* **same app, different environment configuration**



## Configuration Strategy



Each environment should have its own configuration values.



Examples:



* `APP_ENV=local|staging|production`

* database URL

* object storage bucket/container name

* secret keys

* OAuth credentials if added later

* Redis URL

* debug mode

* allowed hosts / domains



Important rules:



* never hardcode secrets in source code

* keep staging and production data isolated

* use environment variables or a secrets manager

* use different buckets/containers and databases per environment



## Suggested Deployment Targets



The project should focus on these targets:



* **AWS**

* **Azure**

* **Render**

* **Railway**

* **Hetzner VPS**



### How to think about them



#### Render / Railway



Best for learning deployment quickly with less operational burden.



Good for:



* container deployment

* managed PostgreSQL

* simpler developer experience

* CI/CD from Git



#### AWS / Azure



Best for learning mainstream cloud architecture.



Good for:



* managed app hosting

* managed Postgres

* managed blob storage

* infrastructure-as-code

* more production-like cloud patterns



#### Hetzner VPS



Best for learning infrastructure more directly.



Good for:



* running Docker Compose yourself

* configuring reverse proxy yourself

* managing Linux, certificates, and backups

* understanding what managed platforms abstract away



## Infrastructure as Code



### Dockerfile



The Dockerfile packages the Flask application into a deployable container image.



Responsibilities:



* define Python runtime

* install dependencies

* copy source code

* define startup command



### docker-compose.yaml



Docker Compose is mainly for **local development** and sometimes for VPS deployment.



Responsibilities:



* start the app and its dependencies together

* define service relationships

* provide local reproducibility



Typical services:



* app

* postgres

* caddy

* optional redis

* optional worker



### Terraform



Terraform is appropriate mainly when targeting AWS or Azure, and sometimes other platforms where infrastructure provisioning is useful.



Use it for:



* provisioning app infrastructure

* provisioning databases

* provisioning storage resources

* provisioning networking and DNS-related resources where relevant



For Render or Railway, Terraform is less central to the learning path, because those platforms abstract away much of the infrastructure.



## CI/CD



A simple CI/CD pipeline should include:



### Continuous Integration



On every push or pull request:



* install dependencies

* run linting

* run tests

* optionally build Docker image



Suggested checks:



* formatting

* linting

* unit tests

* import checks

* maybe type checks later



### Continuous Deployment



On merge to selected branches:



* deploy to **staging** from a staging/develop branch

* deploy to **production** from the main branch



Basic pipeline idea:



```text

Push / Pull Request

  -> lint

  -> test

  -> build image

  -> deploy to staging or production depending on branch

```



## Recommended Libraries and Tools



### Core app



* **Flask**

* **Gunicorn**

* **psycopg** or **psycopg2** for PostgreSQL access

* **SQLAlchemy** or Flask-SQLAlchemy if ORM support is desired



### Reverse proxy



* **Caddy**



### Authentication



* **Flask-Login**



### Optional later additions



* **Redis**

* **RQ**



### CI/CD



* **GitHub Actions**



## What this project teaches well



This project is good for learning:



* app packaging with Docker

* local orchestration with Docker Compose

* production serving with Gunicorn

* reverse proxy concepts

* environment separation

* managed vs self-managed deployment options

* PostgreSQL integration

* object storage integration

* authentication basics

* CI/CD basics

* gradual extension with caching and background jobs



## What is intentionally not the focus



This project is not primarily about:



* microservices

* Kubernetes

* high-scale distributed systems

* advanced frontend frameworks

* full ML platform design

* event-driven architecture



Those can be learned later.



The purpose here is to build a strong, realistic foundation.



## Recommended Learning Progression



### Phase 1



Build and run locally:



* Flask app

* PostgreSQL

* Caddy

* Docker Compose

* simple login

* file upload to blob storage or a mocked local equivalent



### Phase 2



Add CI/CD:



* linting

* tests

* Docker image build

* deploy to staging



### Phase 3



Add production deployment:



* production environment

* separate secrets

* separate database and storage

* monitoring basics



### Phase 4



Add optional realism:



* Redis

* RQ worker

* background jobs

* cache

* more robust observability



## Final Recommendation



For this project, the most sensible architecture is:



* **Flask** backend

* simple **HTML/CSS/JS** frontend

* **PostgreSQL** database

* **object/blob storage**

* **Gunicorn** as app server

* **Caddy** as reverse proxy

* **Flask-Login** for authentication

* **Dockerfile** for packaging

* **docker-compose.yaml** for local development

* **GitHub Actions** for CI/CD

* distinct **local**, **staging**, and **production** environments

* optional later addition of **Redis** and **RQ**



This gives you a deployment-oriented project that is simple enough to learn from, but realistic enough to reflect how many real applications are structured.