Test-api

project to set-up a mock-up backend for a website with:

• User authentication (login, signup, logout)
• Database operations
• API endpoints for frontend interaction

GPT-5 recommendations:

Great question. There are a few solid ways to add email/password login to an API. Below is a practical blueprint you can adapt, plus an alternative token-based approach if you need a more “API-first” style.

First, key terms:

• Authentication: verifying the user is who they say they are (login).
• Authorization: deciding what the authenticated user can access (roles/permissions).
• Two common approaches for web backends:
  • Session cookies (stateful): server stores a session; browser gets a secure cookie with session ID. Simple, secure, easy to revoke. Best default for a website.
  • Tokens (stateless): server issues tokens (e.g., JWT) that the client presents. Often paired with refresh tokens. Good when you have multiple clients (mobile, SPA on a different domain), but more moving parts.

Recommended default for a website: session cookie auth

• Why: Safer defaults, easy logout, simpler server logic, fewer pitfalls than rolling your own JWT system. Works great with server-rendered apps or SPAs on the same site.

Data model (minimum)

• users: id, email (unique, lowercased), password_hash, email_verified, created_at, updated_at.
• sessions: id (random), user_id, created_at, expires_at, revoked_at, user_agent, ip.
• email_verifications: id, user_id, token (random), expires_at, used_at.
• password_resets: id, user_id, token (random), expires_at, used_at.

Security must-haves

• Hash passwords with Argon2id (preferred) or bcrypt with a strong cost. Never store plain passwords.
• Validate inputs; use rate limiting for login and reset endpoints; add a small delay on failed login.
• Use constant-time compare for secrets.
• Lock account temporarily after many failures (e.g., 10 attempts within 10 minutes).
• Don’t leak which emails exist; use generic messages like “If that email exists, we sent instructions.”
• Log and monitor auth events (without sensitive data).
• Optional but recommended: 2FA (TOTP) later.

Cookie and CORS/CSRF

• Set an HttpOnly, Secure, SameSite cookie for session_id.
• Use SameSite=Lax by default; if cross-site (frontend at a different domain), use SameSite=None; Secure and implement CSRF protection (e.g., double-submit token or Origin checks) on state-changing routes.
• If your API and frontend are on different domains, configure CORS to allow your frontend origin and include credentials.

Core flows (session-based)

1. Register (POST /auth/register)

• Input: email, password.
• Validate password strength and email format; normalize email (trim, lowercase).
• Hash password and create user with email_verified=false.
• Create email_verifications token and email the link (e.g., GET /auth/verify-email?token=...).
• Respond 200 with a generic message.

2. Verify email (GET /auth/verify-email)

• Validate token, set email_verified=true, mark token used. Optionally auto-login (create session and set cookie).

3. Login (POST /auth/login)

• Input: email, password.
• Find user, verify password hash. If email not verified, respond with instruction to verify (optionally re-send link).
• Create a session record with expiry (e.g., 30 days rolling or shorter).
• Set cookie:
  • Name: session_id
  • Value: random 256-bit token
  • Flags: HttpOnly, Secure, SameSite=Lax (or None if cross-site), Path=/, Max-Age or Expires set to session lifetime.
• Respond with minimal user info and/or a CSRF token if you need cross-site requests.

4. Auth middleware (for protected routes)

• Read session_id cookie, look up active session, ensure not expired or revoked.
• Attach user to the request context.
• Optionally rotate session_id periodically to prevent fixation.

5. Logout (POST /auth/logout)

• Read session_id, revoke/delete the session, clear the cookie.

6. Password reset

• Request reset (POST /auth/request-password-reset): accept email, create token, email link.
• Reset (POST /auth/reset-password): verify token, set new password hash, invalidate all active sessions for that user.

7. Session management

• Endpoint to get current user (GET /me).
• Endpoint to list/revoke other sessions if you want “log out from other devices.”

Minimal endpoint list

• POST /auth/register
• GET /auth/verify-email?token=...
• POST /auth/login
• POST /auth/logout
• GET /me
• POST /auth/request-password-reset
• POST /auth/reset-password
• (Optional) POST /auth/resend-verification
• (Optional) GET/DELETE /auth/sessions

Implementation sketch (Node.js + Express example)

• Password hashing: argon2 or bcrypt
• Sessions: store in Redis or DB
• Cookies: set with res.cookie

Flow (pseudocode):

• POST /auth/register:
  • validate(email, password)
  • hash = argon2.hash(password)
  • insert user(email, hash, email_verified=false)
  • create verification token; send email
  • return 200
• POST /auth/login:
  • find user by email
  • if not user or !argon2.verify(hash, password): return 401
  • if !email_verified: return 403 with “verify email”
  • sessionId = randomBytes(32).toString('hex')
  • store session(sessionId, user_id, expiry)
  • res.cookie('session_id', sessionId, { httpOnly: true, secure: true, sameSite: 'lax', path: '/', maxAge: 2592000 })
  • return user profile
• Auth middleware:
  • sid = req.cookies.session_id
  • session = lookup(sid); if missing/expired -> 401
  • req.user = user
  • next()
• POST /auth/logout:
  • delete session by sid
  • clear cookie

Implementation sketch (Python + FastAPI)

• Use passlib (argon2/bcrypt), starlette responses for cookies, and a DB/Redis for sessions.
• Same flow as above; set cookie via response.set_cookie with httponly=True, secure=True, samesite="lax".

Alternative: token-based (JWT + refresh token)

• When to use: multiple clients, mobile apps, or cross-domain SPAs.
• Flow:
  • On login, issue:
    • Access token (JWT, short-lived, e.g., 5–15 min).
    • Refresh token (opaque random string or JWT), long-lived (e.g., 30 days), stored server-side with rotation.
  • Store tokens in HttpOnly cookies or keep access token in memory and refresh via HttpOnly refresh cookie.
  • Rotate refresh tokens on every use; revoke on logout or suspected theft.
  • Protect against CSRF if tokens are in cookies; if using Authorization headers and no cookies, CSRF is less of a concern but protect against XSS by not using localStorage.
• Endpoints:
  • POST /auth/login -> set refresh cookie and return access token
  • POST /auth/refresh -> validate refresh, rotate, return new access token
  • POST /auth/logout -> revoke refresh token(s)
• Be careful with token invalidation, rotation, audience/issuer claims, clock skew, and key management.

Operational considerations

• Email delivery: use a provider (SendGrid, Postmark, SES); include proper from/reply-to, rate limit sends.
• Environment secrets: store safely (dotenv, secret manager).
• HTTPS only in production. Secure cookies require HTTPS.
• Observability: structured logs, trace auth flows, but never log passwords or tokens.
• Testing: unit-test hashing/validation, integration-test full flows, simulate CSRF/CORS.

If you’d rather not build all of this:

• Managed identity providers: Auth0, Clerk, Stytch, Firebase Auth, Supabase. They handle hashing, sessions/tokens, emails, MFA, etc., and you integrate via SDKs and webhooks. This can save time and reduce risk.

What would help me tailor concrete code samples:

• Your backend stack (Node/Express, NestJS, Django/DRF, FastAPI, Rails, Go, etc.)
• Whether your frontend is same-origin or on a different domain
• Preference for session cookies vs JWTs
• Database choice (Postgres/MySQL) and whether you have Redis available

If you share those, I can provide a minimal, copy-pasteable implementation with the exact endpoints and middleware.

API and DB SCHEMA details

Great! Since you have already created the database with the necessary tables, the next steps involve implementing the backend functionality using Python with FastAPI. Here’s a structured approach to help you move forward:

1. Set Up Your FastAPI Project

1. Create a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

2. Install FastAPI and Required Libraries:

   pip install fastapi uvicorn passlib[bcrypt] python-multipart sqlalchemy databases

2. Define Your Database Models

Use SQLAlchemy for ORM and define your models based on the tables you created. Here’s an example:

from sqlalchemy import create_engine, Column, Integer, String, Boolean, DateTime
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

DATABASE_URL = "sqlite:///./test.db"  # Replace with your actual database URL
engine = create_engine(DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True, index=True)
    email = Column(String, unique=True, index=True)
    password_hash = Column(String)
    email_verified = Column(Boolean, default=False)
    created_at = Column(DateTime)
    updated_at = Column(DateTime)

# Define other models (sessions, email_verifications, password_resets) similarly

3. Create Dependency for Database Session

from fastapi import Depends
from sqlalchemy.orm import Session

def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

4. Implement Authentication Functions

You will need functions for hashing passwords, verifying passwords, and handling user registration and login.

from passlib.context import CryptContext

pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")

def hash_password(password: str) -> str:
    return pwd_context.hash(password)

def verify_password(plain_password: str, hashed_password: str) -> bool:
    return pwd_context.verify(plain_password, hashed_password)

5. Create API Endpoints

Implement the endpoints as per your TO-DO list. Here’s an example for user registration and login:

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel
from sqlalchemy.orm import Session

app = FastAPI()

class UserCreate(BaseModel):
    email: str
    password: str

@app.post("/auth/register")
def register(user: UserCreate, db: Session = Depends(get_db)):
    # Validate email and password
    hashed_password = hash_password(user.password)
    # Create user in the database
    new_user = User(email=user.email, password_hash=hashed_password)
    db.add(new_user)
    db.commit()
    db.refresh(new_user)
    return {"message": "User registered successfully"}

class UserLogin(BaseModel):
    email: str
    password: str

@app.post("/auth/login")
def login(user: UserLogin, db: Session = Depends(get_db)):
    db_user = db.query(User).filter(User.email == user.email).first()
    if not db_user or not verify_password(user.password, db_user.password_hash):
        raise HTTPException(status_code=401, detail="Invalid credentials")
    # Create and set session here
    return {"message": "Logged in successfully"}

6. Set Up Middleware for Session Management

You can implement middleware to handle session cookies and protect routes as needed.

7. Run Your FastAPI Application

Run the application using Uvicorn:

uvicorn main:app --reload

CORRECTIONS:

PYTHONPATH=src poetry run uvicorn src.test_backend.api:app --reload

8. Test Your Endpoints

Use tools like Postman or Curl to test your API endpoints.

9. Implement Additional Functionalities

• Email verification
• Password reset
• Session management
• CSRF protection

10. Consider Security Best Practices

Ensure you follow security best practices as outlined in your TO-DO list, such as hashing passwords, validating inputs, and implementing rate limiting.

By following these steps, you should be able to build a robust backend with FastAPI for your application. If you have specific questions or need help with certain parts of the implementation, feel free to ask!

Middleware

Setting up middleware for session management in FastAPI involves creating a custom middleware class that will handle session cookies, check for active sessions, and protect routes as necessary. Here’s how to implement it step by step:

Step 1: Create a Middleware Class

You can create a custom middleware class that will intercept requests and manage session validation.

from fastapi import FastAPI, Request, Response, HTTPException
from starlette.middleware.base import BaseHTTPMiddleware
from sqlalchemy.orm import Session
from fastapi import Depends

class SessionMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next):
        # Extract the session_id from cookies
        session_id = request.cookies.get("session_id")
        
        # Check if session_id is present and valid
        if session_id:
            # Here you would typically look up the session in your database
            db: Session = request.state.db
            session = db.query(SessionModel).filter(SessionModel.id == session_id).first()
            
            if session is None or session.revoked_at is not None:
                raise HTTPException(status_code=401, detail="Invalid session")
            
            # Attach the user to the request state for later use
            request.state.user = session.user  # Assuming you have a user relationship
        
        # Proceed to the next middleware or request handler
        response = await call_next(request)
        return response

# Add this middleware to your FastAPI app
app = FastAPI()

# Add middleware to the FastAPI app
app.add_middleware(SessionMiddleware)

Step 2: Modify Your FastAPI App

Make sure to include the middleware in your FastAPI application, as shown above.

Step 3: Protect Routes with Dependency Injection

You can create a dependency that checks if the user is authenticated by checking the request state.

from fastapi import Depends

def get_current_user(request: Request):
    if not hasattr(request.state, "user"):
        raise HTTPException(status_code=401, detail="Not authenticated")
    return request.state.user

@app.get("/protected-route")
def protected_route(current_user: User = Depends(get_current_user)):
    return {"message": f"Hello, {current_user.email}!"}

Step 4: Handle Session Creation and Deletion

In your login and logout endpoints, ensure that you create and revoke sessions properly.

Example: Creating a Session on Login

@app.post("/auth/login")
def login(user: UserLogin, db: Session = Depends(get_db)):
    db_user = db.query(User).filter(User.email == user.email).first()
    if not db_user or not verify_password(user.password, db_user.password_hash):
        raise HTTPException(status_code=401, detail="Invalid credentials")
    
    # Create a new session
    session_id = generate_random_session_id()  # Implement this function to create a random session ID
    new_session = SessionModel(user_id=db_user.id, id=session_id, ...)
    db.add(new_session)
    db.commit()
    
    # Set the session_id cookie
    response = {"message": "Logged in successfully"}
    response.set_cookie(key="session_id", value=session_id, httponly=True, secure=True, samesite="lax")
    return response

Example: Revoking a Session on Logout

@app.post("/auth/logout")
def logout(current_user: User = Depends(get_current_user), db: Session = Depends(get_db)):
    # Revoke the session
    db.query(SessionModel).filter(SessionModel.user_id == current_user.id).delete()
    db.commit()
    
    # Clear the session_id cookie
    response = {"message": "Logged out successfully"}
    response.delete_cookie("session_id")
    return response

Summary

1. Create a custom middleware class to handle session validation and attach the user to the request state.
2. Add the middleware to your FastAPI application.
3. Use dependency injection to protect routes and check user authentication.
4. Handle session creation and deletion in your login and logout endpoints.

By following these steps, you will have a session management middleware in place that protects routes and manages user sessions effectively. If you have any further questions or need clarification on specific parts, feel free to ask!

Deploy

Great job getting your API running locally. Below is a practical, production-minded path to make it publicly available on AWS, plus a couple of alternatives depending on how much control vs. simplicity you want.

High-level choices

• Fastest to production (recommended): AWS App Runner + Amazon RDS for PostgreSQL
  • Pros: Simplest managed HTTPS endpoint, autoscaling, minimal ops. No load balancer or servers to manage.
  • Cons: Limited custom networking control vs ECS.
• More control: Amazon ECS on Fargate + Application Load Balancer (ALB) + RDS
  • Pros: Flexible, common pattern, easy blue/green, sidecars, private networking.
  • Cons: More moving parts.
• Serverless: API Gateway + Lambda + RDS Proxy (or Aurora Serverless)
  • Pros: Scale-to-zero, pay-per-use.
  • Cons: Cold starts, VPC networking for DB access, connection management is trickier.

Below are detailed steps for the recommended App Runner + RDS approach, followed by brief outlines for ECS and Lambda.

A. Prepare your app for deployment

1. Make the app 12-factor friendly

• All config via environment variables (e.g., database URL, secret keys).
• Add a health endpoint like /healthz returning 200.

2. Containerize with Docker (Poetry)

• Keep the image small and deterministic; do not bake secrets into the image.
• Example Dockerfile (works for Flask/FastAPI/Starlette with gunicorn/uvicorn):

# syntax=docker/dockerfile:1

FROM python:3.11-slim AS builder
ENV POETRY_VERSION=1.8.3 \
    POETRY_VIRTUALENVS_CREATE=false \
    PIP_NO_CACHE_DIR=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1
RUN apt-get update && apt-get install -y --no-install-recommends build-essential && rm -rf /var/lib/apt/lists/*
RUN pip install "poetry==$POETRY_VERSION"

WORKDIR /app
COPY pyproject.toml poetry.lock /app/
# Only install runtime deps; skip dev
RUN poetry install --no-interaction --no-ansi --only main

# Copy the application code
COPY . /app

# Optionally compile Python files
RUN python -m compileall -q /app

# Final image
FROM python:3.11-slim
ENV PIP_NO_CACHE_DIR=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1
WORKDIR /app
# Copy installed packages and app
COPY --from=builder /usr/local /usr/local
COPY --from=builder /app /app

# Non-root user
RUN useradd -m appuser
USER appuser

# App Runner defaults to port 8080; ensure your server binds there
ENV PORT=8080
# Example for FastAPI: adjust import path as needed
# CMD can be changed to your framework's server process
CMD ["gunicorn", "-k", "uvicorn.workers.UvicornWorker", "-w", "2", "-b", "0.0.0.0:8080", "your_module.app:app"]

• Test locally: docker build -t myapi:latest . and run with local env vars.

3. Externalize DB configuration

• Use a DATABASE_URL or discrete env vars: DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD, DB_SSLMODE=require.

B. Create managed Postgres in AWS

1. Set up AWS basics (once)

• Create an AWS account (if needed), set a budget alarm, create an IAM user/role with least privilege, enable MFA.
• Pick a region (e.g., us-east-1 or your closest).

2. Networking

• Use an existing VPC or create a new one with at least two private subnets in different AZs.
• You do not need a public subnet for App Runner + RDS, but many templates create both public and private subnets. RDS should be in private subnets.

3. Create Amazon RDS for PostgreSQL

• Engine: PostgreSQL, single-AZ for dev or Multi-AZ for prod.
• Instance class: start small (e.g., db.t4g.micro) and scale later.
• Publicly accessible: No.
• Security: Create a security group for RDS that allows inbound 5432 only from your App Runner connector’s security group (we’ll create that next).
• Create initial database and master user.
• Record endpoint, port, db name.

4. Store DB credentials in AWS Secrets Manager

• Create a secret for Postgres (username/password/host/port/db).
• Optionally enable rotation via a rotation function.
• Tag the secret so you can manage access policies easily.

C. Build and publish your container

1. Create an ECR repository

• Name: myapi
• Authenticate and push:
  • aws ecr get-login-password | docker login ...
  • docker build -t myapi:latest .
  • docker tag myapi:latest <aws_account_id>.dkr.ecr..amazonaws.com/myapi:prod
  • docker push <aws_account_id>.dkr.ecr..amazonaws.com/myapi:prod

D. Create App Runner service

1. Create an App Runner service from ECR image

• Select your ECR image and tag.
• CPU/Memory: start with 1 vCPU / 2 GB for Python frameworks; adjust after measuring.
• Port: 8080 or your chosen port (must match the container).
• Health check path: /healthz.

2. VPC connector for DB access

• Create a VPC Connector pointing to the same VPC and private subnets as RDS.
• Create a security group for the connector (App Runner egress) and allow outbound to RDS.
• In the RDS security group, add inbound rule TCP 5432 from the connector SG.
• Note: App Runner still has public egress for the internet; the connector is used only for VPC destinations like RDS. You don’t need a NAT gateway for this pattern.

3. Environment variables and secrets

• Set env vars for your app.
• Prefer Secrets Manager references. In App Runner, you can map a Secrets Manager secret to environment variables (or expose the whole secret and parse in code).
• Example env:
  • DB_HOST, DB_PORT, DB_NAME
  • DB_USER, DB_PASSWORD (from secret)
  • DB_SSLMODE=require

4. IAM permissions

• App Runner service role needs permission to read the secret:
  • secretsmanager:GetSecretValue on the secret ARN.
• Optionally, CloudWatch Logs permissions are managed automatically by App Runner.

5. Deploy

• App Runner provides a public HTTPS URL out of the box.
• Test the endpoint.

E. Database migration and data import

1. Schema migrations

• If you use Alembic or Django migrations, ensure they run during deployment or on first boot.
• Easiest path: have the app run migrations on startup when an env var like RUN_MIGRATIONS=true is set. Alternatively:
  • Create a one-off container job via a GitHub Action that connects with psql over the internet if RDS is temporarily made publicly accessible and restricted by IP (not ideal), or via an EC2 bastion within the VPC.
  • For App Runner, many teams bake a “manage.py” or “alembic upgrade head” step into a separate CI/CD job that connects to RDS using the RDS endpoint and credentials via Secrets Manager (requires network access, e.g., from a GitHub self-hosted runner in the VPC).

2. Import existing local data (optional)

• From your local machine:
  • Create an SSH tunnel into the VPC (via a temporary bastion) and run pg_dump | pg_restore, or
  • Temporarily make RDS publicly accessible and restrict inbound to your IP only while you import, then revert to private.
• Commands:
  • Backup local: pg_dump -Fc -h localhost -U localuser localdb > backup.dump
  • Restore to RDS:
    • createdb -h -U (if needed)
    • pg_restore -h -U -d --no-owner --no-privileges backup.dump

F. Domain and TLS

1. Custom domain

• In App Runner, add a custom domain. App Runner provisions an HTTPS cert for you, or
• Use Route 53 to point your domain to the App Runner default domain via CNAME.
• If you later move to ALB, you’ll request an ACM cert and attach to the ALB.

G. Observability, scaling, and hardening

• Logs/metrics: App Runner sends logs to CloudWatch; set retention policies and create CloudWatch alarms (e.g., 5xx, high latency).
• Health checks: Ensure /healthz verifies dependencies (optionally checks DB with a timeout).
• Autoscaling: Configure concurrency and scale-out thresholds in App Runner settings.
• Security:
  • Keep RDS private; only allow from App Runner connector SG.
  • Store all secrets in Secrets Manager.
  • Keep container non-root, minimal packages.
  • Consider WAF if needed later (requires ALB or API Gateway).
• Performance:
  • Use a DB connection pool; tune max pool size so you don’t exhaust RDS max_connections.
  • Consider RDS Proxy if you later move to Lambda or have spiky connection patterns.

H. CI/CD

• Option 1: App Runner auto-deploy from ECR on new image tags.
• Option 2: GitHub Actions pipeline:
  • On push to main:
    • Build image.
    • Push to ECR.
    • Call UpdateService on App Runner to deploy the new tag.
• Save AWS credentials in GitHub OIDC and use role assumption (no long-lived keys).

Alternative path 1: ECS Fargate + ALB + RDS (summary)

• Create VPC with public subnets (for ALB) and private subnets (for ECS tasks and RDS).
• Create RDS PostgreSQL in private subnets.
• Create ECR repo; build and push image.
• Create ECS cluster and Fargate task definition (map port 8080).
• Create security groups:
  • ALB SG: inbound 80/443 from internet; outbound to ECS tasks.
  • ECS tasks SG: inbound from ALB SG on app port; outbound to RDS SG on 5432.
  • RDS SG: inbound 5432 from ECS tasks SG.
• Create ALB + target group + listener (HTTP->HTTPS redirect, HTTPS listener with ACM certificate).
• Create ECS Fargate service in private subnets, target group registration.
• Route 53: point your domain to ALB.
• Run migrations via a one-off ECS task.

Alternative path 2: API Gateway + Lambda + RDS Proxy (summary)

• Package your app for Lambda (e.g., FastAPI + Mangum, or use AWS Lambda Web Adapter).
• Create RDS PostgreSQL and RDS Proxy.
• Place Lambda in the same VPC subnets; allow Lambda SG to RDS Proxy SG.
• Configure API Gateway HTTP API to integrate with Lambda; set custom domain and certificate.
• Manage cold starts and connection pooling via RDS Proxy.
• Great when traffic is very bursty/low and you want lower idle cost.

Costs to watch

• App Runner: charges per vCPU/memory-hour and requests; simple to estimate.
• RDS: instance-hours + storage + I/O; consider Single-AZ for dev to save cost, or Aurora Serverless v2 for autoscaling.
• CloudWatch Logs storage.
• Avoid NAT Gateway unless you truly need VPC egress for your service; App Runner avoids this for most cases.

Common pitfalls

• Making RDS publicly accessible long-term. Keep it private; use VPC connector and SGs.
• Not setting timeouts and health checks. Add them early.
• Not setting up budgets/alarms and leaving services running.
• Missing migrations step in deployment.
• Building huge Docker images; keep them lean for faster deploys.

If you share a bit more about your API framework (Flask, FastAPI, Django, etc.), traffic expectations, and whether you want the simplest path or more control, I can tailor the steps, provide a ready-to-use GitHub Actions workflow, and adjust the Dockerfile to your exact structure.

On AWS Beanstalk

Short answer: Elastic Beanstalk is a “bundler” that stands up and manages the compute, load balancing, scaling, health checks, logging, and wiring inside your VPC. You still provision data stores, secrets, and developer tooling separately.

What Elastic Beanstalk manages for you

• Compute
  • EC2 instances, Auto Scaling Group, instance health checks
• Load balancing and traffic
  • Application Load Balancer (ALB), listeners, target groups, rolling/rolling‑with‑additional‑batch deployments
  • HTTPS termination on the ALB; you can attach an ACM certificate
• Scaling and availability
  • Auto scaling policies, multi‑AZ placement, health monitoring
• Networking integration
  • Placement in your VPC subnets, security groups for ALB and EC2, inbound/outbound rules
• Observability
  • CloudWatch metrics and log streaming from instances and the web server
• App config and lifecycle
  • Environment variables, platform hooks/.ebextensions, blue/green via CNAME swap, versioning in S3
• Containers and runtimes
  • Native Python platform or Docker platform (single or multicontainer). It can build from your Dockerfile on the instances or pull images from a registry (e.g., ECR) if you grant permissions
• Optional database (tightly coupled)
  • EB can create an RDS instance as part of the environment, but this ties DB lifecycle to the app; for prod it’s recommended to create RDS outside EB

What Elastic Beanstalk does not manage (you handle these separately)

• Databases (recommended path)
  • Amazon RDS for PostgreSQL (create and manage outside EB), RDS Proxy
• Container registry
  • Amazon ECR repositories and image builds/pushes
• Serverless/API front doors
  • App Runner, API Gateway, Lambda, ECS/Fargate (EB is an alternative to these, not a manager of them)
• Secrets and config stores
  • AWS Secrets Manager, Parameter Store (you can integrate via code/IAM, but EB doesn’t create/manage them)
• DNS and edge
  • Route 53 hosted zones/records, CloudFront, WAF (you can attach WAF to the ALB yourself)
• Certificates
  • ACM certificate issuance/validation (you request in ACM; EB lets you attach it to the ALB)
• CI/CD
  • Pipelines like GitHub Actions/CodePipeline; EB provides a deployment target but not the pipeline itself
• VPC creation and NAT
  • EB uses your existing VPC/subnets; it doesn’t create a VPC or NAT Gateway

Practical takeaway

• If you choose Elastic Beanstalk for your API, EB will host your app (Python or Docker), provide a public endpoint behind an ALB with autoscaling, put it in your VPC, and wire logging/metrics.
• You should still provision RDS (PostgreSQL) separately, store credentials in Secrets Manager (or env vars), optionally pull images from ECR if you use Docker, and set up Route 53 for your custom domain.
• Database migrations are not automatic; add them via platform hooks/.ebextensions or your CI/CD job.

Useful: https://medium.com/creative-technology-concepts-code/python-docker-and-amazon-elastic-beanstalk-7f9a45c2c9db -> it seems that beanstalk can directly deploy via the beasnstalk cli :)