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You can build a working Flask REST API on Ubuntu with a Python virtual environment, explicit routes, and JSON responses, then run it behind Gunicorn and nginx for production. Use Flask’s development server only while coding; the deployed service should run as an unprivileged systemd-managed Gunicorn process, with nginx handling public HTTP connections.
This guide starts with a small health and items API, shows how to test it with curl, and then moves the same application behind a Unix socket and nginx. Flask 3.1.x supports Python 3.9 and newer, but the current Gunicorn installation page requires Python 3.12 or newer, so check your interpreter before creating the production environment.
What you are building
The finished setup has four layers:
- Flask application: Python functions map URL paths and HTTP methods to responses.
- Gunicorn: A production WSGI server imports your Flask application and manages worker processes.
- nginx: A public reverse proxy accepts HTTP requests and forwards them to Gunicorn.
- systemd: Ubuntu starts, stops, restarts, and logs Gunicorn as a dedicated unprivileged service.
During development, Flask can listen on a local port directly. In production, clients should reach nginx on ports 80 or 443, while Gunicorn remains private on loopback or a Unix socket. Your API’s authentication, validation rules, database, logging policy, rate limits, and error format are part of the API contract and must be defined for the application rather than assumed by Flask.
Check Ubuntu and Python prerequisites
Verify the interpreter before installing anything
Run:
python3 --versionFlask 3.1.x supports Python 3.9 and later. The current Gunicorn installation requirement is Python 3.12 or later. If Ubuntu’s python3 is older than 3.12, install an approved Python 3.12-or-newer package for your Ubuntu release and use that executable when creating the production virtual environment. Do not assume that a virtual environment upgrades Python; it uses the interpreter that created it.
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Install the operating-system packages
Refresh package metadata and install Python, virtual-environment support, pip, and nginx:
sudo apt update
sudo apt install python3 python3-venv python3-pip nginxIf you selected a separate Python 3.12 executable, substitute it in the virtual-environment commands below, for example python3.12 -m venv .venv. Keep the interpreter choice consistent for Flask, Gunicorn, systemd, and any later deployment automation.
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Create an isolated Flask project
Make the project directory and virtual environment
Create a directory that is separate from Ubuntu’s system libraries:
mkdir -p ~/flask-api
cd ~/flask-api
python3 -m venv .venv
. .venv/bin/activateAfter activation, the shell prompt normally shows (.venv). Install Flask and Gunicorn inside this environment:
pip install Flask gunicornThe official Flask installation guidance recommends a virtual environment because each project then has its own package versions instead of changing the operating system’s Python installation or another application’s dependencies. If pip install gunicorn reports an unsupported Python version, recreate the environment with Python 3.12 or newer rather than installing packages into the system interpreter.
Confirm the installed tools
python --version
flask --version
gunicorn --version
which python
which gunicornThe final two commands should resolve into your project’s .venv/bin directory. If they point to /usr/bin, activate the environment again with . .venv/bin/activate.
Write a small JSON API
Create app.py
Save this file in ~/flask-api/app.py:
from flask import Flask, jsonify, request
app = Flask(__name__)
@app.get('/api/health')
def health():
return jsonify(status='ok')
@app.get('/api/items')
def list_items():
return jsonify(items=[])
@app.post('/api/items')
def create_item():
payload = request.get_json(silent=True) or {}
return jsonify(item=payload), 201app = Flask(__name__) creates the application object. The @app.get and @app.post decorators make the HTTP method part of the route definition, so a GET and a POST are not accidentally treated as the same operation. jsonify serializes Python values and sets a JSON response content type. Returning 201 from the POST route communicates that a resource was created.
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This example deliberately keeps items in memory as an empty list response and echoes a submitted object. It does not provide persistence, authentication, input validation, duplicate detection, or authorization. Add those rules before exposing a real resource.
Make the contract explicit
Document each endpoint before clients depend on it. At minimum, record:
- The exact path, including the
/apiprefix. - Allowed HTTP methods and what each method changes.
- Required headers such as
Content-Type: application/jsonand any authentication header. - Request fields, data types, size limits, and whether unknown fields are rejected.
- Successful response JSON shapes and status codes.
- Error response shape, status code, and whether error details are safe to expose.
For this sample, GET /api/health returns {status: ok}, GET /api/items returns an object containing an items array, and POST /api/items returns the submitted object under item with status 201. In a production contract, use a stable documented schema and reject malformed input instead of silently converting it to an empty object.
Run and exercise the API locally
Start Flask’s development server
With the virtual environment active and the current directory set to the project, run:
flask --app app run --debugThe server normally listens on 127.0.0.1:5000. The --debug option enables automatic reloads and the interactive debugger; both are development conveniences and must not be enabled for a public deployment.
Test each route with curl
curl http://127.0.0.1:5000/api/health
curl http://127.0.0.1:5000/api/items
curl -i -X POST http://127.0.0.1:5000/api/items -H 'Content-Type: application/json' -d '{"name":"Ada"}'The first request should return a JSON object whose status is ok. The second should return an empty items array. The POST response should include an HTTP/1.1 201 status and an item object containing the submitted name. The -i option displays response headers so you can verify status and content type as well as the body.
Test malformed and missing input deliberately
Try a POST without a body and with a non-JSON content type. The sample uses silent=True, so parsing failures become an empty object rather than a Flask exception. That behavior is convenient for a demonstration but usually too permissive for a business API. Replace it with explicit validation that returns a documented client error, commonly status 400, when required fields or types are missing.
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Separate development behavior from production behavior
Flask’s built-in server is intended for local development, not Internet-facing traffic. It is not the production process manager, does not provide the deployment isolation you need, and exposes a debugger when debug mode is enabled. Stop it with Ctrl+C after local testing.
Gunicorn is a WSGI server designed to run the Flask application with worker processes. Flask’s deployment guidance recommends putting nginx or Apache in front of the WSGI server as a reverse proxy. Gunicorn must run as an unprivileged account; running it as root would make application code run with root privileges if the process were compromised.
Try Gunicorn before creating a service
Bind to loopback for a private test
From the active virtual environment, run:
gunicorn --workers 4 --bind 127.0.0.1:8000 app:appThe target app:app means import the module app and use its Flask object named app. Four synchronous workers are the documented example and a reasonable starting point for many ordinary Flask workloads, not a universal capacity guarantee. Measure your application and tune worker count for its CPU, memory, request time, and traffic pattern.
In another shell, call:
curl http://127.0.0.1:8000/api/healthBinding to 127.0.0.1 keeps Gunicorn off the public network interface when nginx is the edge server. Press Ctrl+C to stop this foreground test.
Choose synchronous or asynchronous workers deliberately
Gunicorn’s default synchronous worker is appropriate for most uses. The Gunicorn Flask guidance documents gevent for applications that must handle numerous long-running concurrent connections. An asynchronous worker does not automatically make blocking application code safe or faster; evaluate library compatibility, timeout behavior, and observability before changing worker classes.
Create a durable systemd Gunicorn service
Prepare a production-owned directory
Do not run the deployed service from a personal home directory with broad permissions. Create a dedicated unprivileged account and application directory:
sudo adduser --system --group --home /srv/flask-api flaskapi
sudo mkdir -p /srv/flask-api
sudo chown -R flaskapi:flaskapi /srv/flask-apiCopy app.py and your application source into /srv/flask-api, then create and populate its environment as that account. The following commands assume the Python executable you selected satisfies Gunicorn’s Python 3.12-or-newer requirement:
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sudo -u flaskapi python3 -m venv /srv/flask-api/.venv
sudo -u flaskapi /srv/flask-api/.venv/bin/pip install Flask gunicornIn a real project, install pinned, reviewed dependencies from your deployment process rather than relying on an unrecorded set of latest packages.
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Create /etc/systemd/system/flask-api.service:
[Unit]
Description=Flask API Gunicorn service
After=network.target
[Service]
User=flaskapi
Group=www-data
WorkingDirectory=/srv/flask-api
RuntimeDirectory=flask-api
RuntimeDirectoryMode=0770
ExecStart=/srv/flask-api/.venv/bin/gunicorn --workers 4 --umask 007 --bind unix:/run/flask-api/gunicorn.sock app:app
Restart=always
[Install]
WantedBy=multi-user.targetUser=flaskapi prevents the application from running as root. Ubuntu’s nginx worker normally uses the www-data group; assigning that group and using a restrictive umask allows nginx to open the Unix socket. If your nginx process uses a different account or group, change the unit accordingly and verify the directory and socket permissions rather than making them world-writable.
Load, start, and inspect the service
sudo systemctl daemon-reload
sudo systemctl enable --now flask-api.service
sudo systemctl status flask-api.service
sudo journalctl -u flask-api.service -eenable --now starts the service immediately and enables it at boot. status shows whether systemd considers it active; journalctl displays Gunicorn’s standard output and errors. A repeated restart usually indicates an import error, an incorrect working directory, a missing dependency, an invalid Python version, or a socket-permission problem. Run the exact ExecStart command manually as flaskapi to expose configuration errors quickly, then check the journal again.
Put nginx in front of Gunicorn
Configure a reverse-proxy server block
Create an nginx site configuration such as /etc/nginx/sites-available/flask-api:
server {
listen 80;
server_name api.example.com;
location / {
proxy_pass http://unix:/run/flask-api/gunicorn.sock:;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
}Replace api.example.com with the hostname that resolves to the Ubuntu server. The trailing colon in the Unix-socket proxy_pass value is part of nginx’s socket syntax. The forwarded headers preserve the original host, client address, proxy chain, and scheme for application logging and URL generation.
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sudo ln -s /etc/nginx/sites-available/flask-api /etc/nginx/sites-enabled/flask-api
sudo nginx -t
sudo systemctl reload nginxRun nginx -t before every reload. If it reports a syntax error, nginx keeps the previous working configuration; correct the indicated line and test again. If nginx returns 502 Bad Gateway, inspect both layers: confirm flask-api.service is active, verify that /run/flask-api/gunicorn.sock exists, and check that nginx’s account can traverse the runtime directory and open the socket.
Keep public traffic at nginx
Do not bind Gunicorn to 0.0.0.0 when nginx is the intended public edge. A loopback bind or Unix socket prevents clients from bypassing nginx’s request limits, access logging, header handling, and future TLS termination. For HTTPS, configure TLS at nginx using certificates managed by your normal Ubuntu certificate process, then preserve X-Forwarded-Proto so the application can distinguish secure requests.
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Deployment choices at a glance
| Choice | Where it fits | Trade-off |
|---|---|---|
flask --app app run --debug |
Local development and interactive debugging | Convenient reloads and debugger; not a production server |
Gunicorn on 127.0.0.1:8000 |
Private production process or a quick deployment test | Simple to test, but another component must expose HTTP publicly and supervise restarts |
| Gunicorn on a Unix socket plus nginx | Typical Ubuntu production topology | Requires socket permissions and nginx configuration, but keeps Gunicorn private and gives nginx the public edge |
| systemd service | Long-running process with boot startup and journal logging | Requires a unit file and explicit user, directory, and environment settings |
| systemd socket activation | Deployments that want systemd to create and hand off the listening socket | Adds lifecycle configuration; use Gunicorn’s documented socket-activation pattern consistently rather than mixing incompatible service and socket units |
| Synchronous Gunicorn workers | Most conventional Flask request workloads | Each worker handles one request at a time |
| gevent workers | Numerous long-lived concurrent connections when the application stack supports cooperative I/O | Requires compatible dependencies and deliberate testing of blocking code |
Design the parts Flask does not decide for you
Validation and status codes
Check JSON types, required fields, maximum lengths, and allowed values before writing data. Return a consistent client-error document for malformed JSON or invalid fields, and reserve server-error responses for failures the client cannot correct. Keep successful response shapes stable so clients do not need to infer whether a missing key means null, empty, or an error.
Persistence and concurrency
The example returns an empty list because it has no database. A real API needs a persistence layer and a clear transaction strategy. Multiple Gunicorn workers do not share ordinary Python variables; an in-memory list would be separate in each worker and would disappear when a worker restarts. Store durable state in a database or another service designed for shared access.
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Decide how callers authenticate and which resources each identity may access. Keep credentials, signing keys, and database passwords outside source files, provide them through protected environment or secret-management mechanisms, and never return them in JSON or logs. Apply authorization checks to every mutating and sensitive route, not only to the login endpoint.
Logging, limits, and observability
Log request identifiers, method, path, status, and duration without recording passwords, tokens, or untrusted data that could forge log lines. Set request and upstream timeouts appropriate to the workload. Rate limiting and payload-size limits belong at the application, nginx, or an API gateway layer according to your threat model. Monitor systemd restarts, Gunicorn worker exits, nginx errors, disk space, and memory.
Common failures and fixes
ModuleNotFoundError or an unknown Gunicorn command
The service is probably using a different interpreter from your shell. Use an absolute executable path in ExecStart, confirm the module is under WorkingDirectory, and install dependencies into that exact .venv.
Gunicorn refuses to install
Check python --version inside the environment. Flask 3.1.x can run on Python 3.9 or newer, but the current Gunicorn requirement is Python 3.12 or newer. Recreate the environment with a supported interpreter.
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nginx reports 502
Check sudo systemctl status flask-api.service, then sudo journalctl -u flask-api.service. Confirm the socket path in the systemd unit exactly matches nginx, and inspect permissions on /run/flask-api and gunicorn.sock. A service running under the wrong group can create a socket that nginx cannot open.
Requests reach nginx but the application sees the wrong scheme or client address
Verify the four proxy_set_header directives in the server block and make sure the application framework is configured to trust proxy headers only from your controlled proxy. Do not blindly trust client-supplied forwarding headers when nginx is not the only network path.
Changes to app.py are not visible
Debug reloads apply to Flask’s development server, not automatically to a systemd Gunicorn service. Deploy the changed files, restart or reload the service using your chosen Gunicorn strategy, and watch the journal for import or migration errors.
Quick Recap
Production checklist
- Use a Python version supported by both Flask and the current Gunicorn release.
- Create and activate a project virtual environment; install dependencies there.
- Define routes, methods, JSON schemas, status codes, and errors explicitly.
- Test health, success, invalid-input, unauthorized, and server-failure paths with
curlor your API test suite. - Disable Flask debug mode outside local development.
- Run Gunicorn as a dedicated unprivileged user, never as root.
- Keep Gunicorn on loopback or a protected Unix socket behind nginx.
- Use systemd for startup, restart behavior, and journal logs.
- Validate nginx configuration before reloading it.
- Protect secrets, validate payloads, enforce authorization, and avoid sensitive data in logs.
- Document persistence, migrations, timeouts, rate limits, and operational ownership before launch.
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