Fly.io vs Railway: Modern App Deployment

When you’re ready to ship a modern web app, the choice of deployment platform can feel like navigating a maze of buzzwords and pricing tables. Two services that keep popping up in developer conversations are Fly.io and Railway. Both promise “serverless‑ish” simplicity, global reach, and a developer‑first CLI, yet they differ in architecture, pricing granularity, and the kinds of workloads they shine on. In this deep dive we’ll unpack their core concepts, stack them side‑by‑side on real‑world scenarios, and hand you a few pro tips to make sure your next launch is smooth, cost‑effective, and future‑proof.

Fly.io: Edge‑First Containers

Fly.io is built around the idea of running tiny, geographically distributed VMs (called “machines”) that can host Docker containers, static sites, or even full‑stack frameworks. The platform automatically places instances close to your users, reducing latency without you having to manage any CDN configuration. Under the hood, Fly uses Firecracker micro‑VMs, giving you near‑bare‑metal performance while still providing the isolation of traditional VMs.

Core Concepts

• Apps & Services: An “app” is a logical grouping of one or more services (e.g., a web server, a background worker, a database). Each service runs in its own set of machines.
• Machines: Scalable, lightweight VMs that you can spin up in any region Fly supports. They start in seconds and can be resized on the fly.
• Volumes: Persistent block storage that can be attached to machines, ideal for stateful workloads like databases.
• Fly.toml: A declarative configuration file that describes your app, regions, scaling rules, and secrets.

Because Fly treats every deployment as a container, you have full control over the runtime. Whether you need a custom Go binary, a Python virtual environment, or a Node.js app with native dependencies, you simply ship a Docker image and let Fly handle the rest.

Railway: Plug‑and‑Play Full‑Stack Platform

Railway markets itself as the “infrastructure for developers” that abstracts away the nitty‑gritty of provisioning servers. Instead of dealing with containers directly, you connect your code repository, select a “plugin” (like PostgreSQL, Redis, or a static site), and Railway spins up the necessary resources. The platform’s UI is exceptionally clean, and the CLI mirrors that simplicity, making it a favorite for rapid prototyping and hackathons.

Core Concepts

• Projects: A container for one or more services, environment variables, and plugins. Projects map neatly to a GitHub repository.
• Services: Individual runtime environments (e.g., Node, Python, Docker) that execute your code.
• Plugins: Managed add‑ons such as PostgreSQL, MySQL, or Kafka that Railway provisions and wires into your services automatically.
• Variables: Secure key‑value pairs (secrets or config) that are injected at runtime.

Railway’s “Zero‑Config” philosophy means you often don’t need a Dockerfile at all—just a requirements.txt or package.json and Railway will detect the language, install dependencies, and expose a public URL.

Feature Comparison

Deployment Workflow

Both platforms use a CLI that integrates with Git, but the mental model differs. With Fly, you push a Docker image (or let Fly build one from a Dockerfile) and then run fly deploy. The command reads fly.toml, creates or updates machines, and rolls out the new version across all selected regions. Railway, on the other hand, watches your repository for changes. A railway up command triggers a build, and the platform decides whether to spin up a new container or reuse an existing one based on the detected language.

Fly gives you explicit control over scaling policies (e.g., “2 machines in NYC, 1 in London”), while Railway auto‑scales based on request volume and provides a “preview” environment for each pull request out of the box.

Pricing & Limits

Fly’s pricing is usage‑based: you pay for the number of vCPU‑seconds, RAM‑seconds, and outbound data per month. The free tier includes 3 small machines (256 MiB RAM each) and 3 GB of outbound traffic, which is generous for hobby projects. Railway offers a free tier with 500 hours of runtime and 1 GB of storage, plus free managed PostgreSQL up to 500 MB. Once you exceed those limits, Railway switches to a flat‑rate per service model, which can be cheaper for low‑traffic APIs but may become pricey for high‑throughput edge workloads.

In practice, if you need global latency reduction and fine‑grained scaling, Fly’s per‑region billing often wins. If you prefer a “set‑and‑forget” experience with managed databases and automatic previews, Railway’s bundled pricing can be more predictable.

Real‑World Use Case: Deploying a FastAPI App on Fly.io

FastAPI is a modern Python framework that shines with async I/O and automatic OpenAPI docs. Below is a minimal FastAPI project that returns a greeting and echoes request headers—perfect for demonstrating edge deployment.

# app/main.py
from fastapi import FastAPI, Request

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "👋 Hello from Fly.io!"}

@app.get("/headers")
async def echo_headers(request: Request):
    return {"headers": dict(request.headers)}

Next, create a Dockerfile that builds a lightweight image using python:3.12-slim. The fly.toml file tells Fly to run one instance in three regions.

# Dockerfile
FROM python:3.12-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]

# fly.toml
app = "fastapi-demo"

[build]
  dockerfile = "Dockerfile"

[env]
  PORT = "8080"

[[services]]
  internal_port = 8080
  protocol = "tcp"
  ports = [{ port = 80, handlers = ["http"] }]

  [[services.routers]]
    region = "iad"   # Virginia, USA
    max_concurrency = 50

  [[services.routers]]
    region = "fra"   # Frankfurt, Germany
    max_concurrency = 50

  [[services.routers]]
    region = "nrt"   # Tokyo, Japan
    max_concurrency = 50

Deploy with a single command:

fly launch --now

Fly builds the image, provisions three machines (one per region), and attaches a global load balancer. You can verify latency improvements by curling the endpoint from different continents; the response time should be consistently sub‑100 ms thanks to edge proximity.

Real‑World Use Case: Deploying a Node.js Microservice with PostgreSQL on Railway

Railway’s built‑in PostgreSQL plugin makes it trivial to spin up a relational database alongside your service. Below is a simple Express API that stores and retrieves user records. The code demonstrates how Railway injects the database URL into the environment.

// index.js
const express = require('express');
const { Pool } = require('pg');

const app = express();
app.use(express.json());

const pool = new Pool({
  connectionString: process.env.DATABASE_URL, // auto‑injected by Railway
  ssl: { rejectUnauthorized: false } // required for Railway's managed DB
});

app.post('/users', async (req, res) => {
  const { name, email } = req.body;
  const result = await pool.query(
    'INSERT INTO users (name, email) VALUES ($1, $2) RETURNING *',
    [name, email]
  );
  res.json(result.rows[0]);
});

app.get('/users/:id', async (req, res) => {
  const { id } = req.params;
  const result = await pool.query('SELECT * FROM users WHERE id = $1', [id]);
  if (result.rowCount === 0) return res.status(404).json({ error: 'User not found' });
  res.json(result.rows[0]);
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => console.log(`🚀 Service listening on ${PORT}`));

Before deploying, add a package.json and a simple migration script to create the users table.

// package.json
{
  "name": "railway-express",
  "version": "1.0.0",
  "main": "index.js",
  "scripts": {
    "start": "node index.js",
    "migrate": "psql $DATABASE_URL -c \"CREATE TABLE IF NOT EXISTS users (id SERIAL PRIMARY KEY, name TEXT NOT NULL, email TEXT NOT NULL UNIQUE);\""
  },
  "dependencies": {
    "express": "^4.18.2",
    "pg": "^8.11.0"
  }
}

Deploy steps:

1. Initialize the project on Railway: railway init.
2. Add the PostgreSQL plugin via the dashboard or railway add postgres.
3. Run the migration once: railway run npm run migrate.
4. Start the service: railway up. Railway builds the container, attaches the DB URL, and gives you a public .railway.app domain.

Because Railway automatically creates a preview environment for each PR, you can test new API features without touching production data. The built‑in health checks also restart the service if the DB connection fails, providing an extra safety net.

Pro Tips for Getting the Most Out of Both Platforms

Tip 1 – Use Fly’s “sticky sessions” for stateful websockets. Add [[services.tcp_checks]] in fly.toml and enable sticky_sessions = true to keep a single client bound to the same edge machine, reducing reconnect latency.

Tip 2 – Leverage Railway’s “Variables” for feature flags. Store a JSON flag map in an environment variable, and toggle behavior without redeploying. Because Railway injects variables at container start, you can roll out changes instantly by updating the variable in the UI.

Tip 3 – Combine both services for a hybrid architecture. Deploy static assets (e.g., a React SPA) on Fly for ultra‑low latency edge caching, while running the API layer on Railway for its managed DB and preview environments. Use a custom domain with a DNS CNAME pointing to both services, and let Cloudflare handle the routing logic.

Conclusion

Fly.io and Railway each excel at a different slice of the modern deployment pie. Fly’s edge‑first containers give you granular control, global latency benefits, and a pricing model that scales with actual resource consumption. Railway, meanwhile, shines when you want a frictionless “code‑to‑cloud” experience, especially for data‑centric apps that benefit from managed add‑ons and automatic previews. By understanding their strengths—and by applying the pro tips above—you can choose the right tool for your project, or even blend them into a hybrid workflow that maximizes performance, developer velocity, and cost efficiency.