Fly.io vs Railway in 2026: Deploying Full-Stack Apps

Fly.io and Railway have become the default answer for backend app deployment when Vercel is too limited and AWS is too much work. Vercel and Netlify are excellent at what they do: static sites, edge functions, frontend frameworks. They’re less suited to backend services that need persistent connections, long-running processes, background workers, or databases that live close to the application. If you’re weighing Next.js against Astro or Remix for the frontend, that decision is mostly separate from where the backend API ends up running.

AWS, GCP, and Azure can handle all of that, but the configuration surface area is enormous. A three-person team spending two days setting up networking, IAM roles, and load balancers for a new service is burning time that should go elsewhere.

Fly.io and Railway sit between those options, alongside containerized deployment on AWS ECS or Cloud Run for teams who want to stay closer to hyperscaler infrastructure. Both Fly and Railway are designed for developers who want to deploy real backend workloads (APIs, workers, databases, websocket servers) without standing up cloud infrastructure by hand. Both have matured significantly over the past two years and are now used in production by companies with real scale.

Fly.io

Fly.io runs your applications as lightweight VMs (using Firecracker microVMs) on their own global network of datacenters. When you deploy to Fly, your app runs as an actual VM in a specific region, not as a function that spins up on demand.

This distinction matters for:

• Persistent connections. WebSocket servers, gRPC services, and anything that maintains long-lived connections work correctly. Serverless cold starts and connection limits don’t apply.
• Anycast networking. Fly routes incoming traffic to the nearest region where your app is running. A user in Singapore hits your Singapore VM, not your US one.
• Stateful workloads. Fly Volumes are persistent SSD storage attached to a VM. This makes it practical to run databases (Postgres, SQLite with Litestream, Redis) directly on Fly infrastructure.

Deploying a Node.js API to Fly

# Install flyctl
curl -L https://fly.io/install.sh | sh

# Log in
fly auth login

# In your project directory
fly launch
# Fly detects your framework, suggests a config, and creates a fly.toml

The generated fly.toml:

app = "my-api"
primary_region = "bom"  # Mumbai

[build]

[http_service]
  internal_port = 3000
  force_https = true
  auto_stop_machines = true
  auto_start_machines = true
  min_machines_running = 0

[[vm]]
  cpu_kind = "shared"
  cpus = 1
  memory_mb = 512

fly deploy
# Builds a Docker image, pushes it, and deploys to your primary region

That’s the full deployment. Fly detects your Dockerfile if you have one, including one already tightened with multi-stage Docker builds, or generates one from the framework it detects. Your app gets a *.fly.dev URL immediately, plus easy custom domain configuration.

Scaling on Fly

Fly’s machine model gives you granular control. For a typical web service:

# Scale to two machines for redundancy
fly scale count 2

# Scale to multiple regions
fly regions add sin  # add Singapore
fly scale count 4   # 2 per region, or distribute as needed

# View machine status
fly status

Auto-scaling is configured via auto_stop_machines and auto_start_machines in fly.toml. With this enabled, machines that receive no traffic stop (you’re not charged) and start when traffic arrives (there’s a cold-start delay, typically under a second for Fly’s microVMs, well under the multi-second delays covered in how serverless cold starts work).

For always-on services, set min_machines_running = 1.

Managed Postgres on Fly

fly postgres create --name my-api-db --region bom

# Attach to your app (sets DATABASE_URL environment variable automatically)
fly postgres attach my-api-db --app my-api

Fly runs Postgres as actual VMs with persistent storage, not as a managed service. This gives you full Postgres without a separate database bill, at the cost of managing backups yourself (or configuring Fly’s backup tooling). If you’d rather not run Postgres yourself at all, a serverless option like Neon’s branching model separates storage from compute and hands the operational work to the provider instead.

This self-managed model is worth weighing against a serverless Postgres option like Neon if branching and autoscaling matter more to you than co-location. For read-heavy workloads, you can add replicas:

fly postgres create --name my-api-db-replica --region sin
fly postgres attach my-api-db-replica --app my-api --fork

Railway

Railway takes a simpler approach than Fly. The central concept is a Project: a collection of services deployed together. You connect a GitHub repository and Railway builds and deploys on every push.

The setup is genuinely one of the faster deployment experiences available:

1. Create a project at railway.app
2. Connect your GitHub repo
3. Railway detects the runtime and deploys

No CLI required, though one exists. No Dockerfile required (Railway has Nixpacks for auto-detection). For a simple Express, FastAPI, or Django backend, it typically works without any configuration.

Railway vs Fly: The Key Differences

	Fly.io	Railway
Deployment model	VM-based (Firecracker)	Container-based
Setup complexity	Moderate (CLI-first)	Low (UI-first)
Networking	Anycast, multi-region by default	Single region per service
Databases	Self-managed Postgres on VMs	Managed Postgres, MySQL, Redis, MongoDB
Pricing model	Per-VM pricing	Usage-based (CPU + RAM + network)
Free tier	Hobby plan with usage limits	$5 credit/month
Best for	APIs needing low latency globally, WebSockets, stateful apps	Simple backends, staging environments, full-stack apps

Railway’s managed databases are a meaningful advantage for teams that don’t want to think about database operations at all. Railway provisions Postgres, handles backups, and provides a database URL. You point your app at it and forget about it, though once traffic grows it’s worth revisiting connection pooling on either platform, since exhausted connections tend to show up before compute does.

Railway Pricing in Practice

Railway charges for actual resource usage: CPU time, RAM, and egress. A simple Node.js API sitting mostly idle (0.2 vCPU, 256 MB RAM) might cost $5-10 per month. A backend with consistent traffic and a Postgres database runs $20-50 per month for modest load.

The usage-based model is honest (you pay for what runs), but it makes cost estimation harder than Fly’s per-VM pricing.

Deploying a Python API on Railway

No Railway-specific files needed for most projects. Add a railway.json if you need to customize:

{
  "$schema": "https://railway.app/railway.schema.json",
  "build": {
    "builder": "NIXPACKS"
  },
  "deploy": {
    "startCommand": "uvicorn main:app --host 0.0.0.0 --port $PORT",
    "healthcheckPath": "/health",
    "restartPolicyType": "ON_FAILURE",
    "restartPolicyMaxRetries": 10
  }
}

Environment variables, including database connection strings, are set in the Railway dashboard or via the CLI. Services in the same project can reference each other using Railway’s internal DNS. For anything more sensitive than a database URL, treat the platform’s secrets store as your baseline and read secrets management in production before you put API keys directly in dashboard env vars.

Where Kamal Fits

Kamal is a deployment tool rather than a hosting platform. It deploys Docker containers to servers you own or rent (a DigitalOcean droplet, a Hetzner VPS, your own hardware). It handles zero-downtime deploys, health checks, rollbacks, and TLS via Traefik, similar in spirit to the blue-green and canary deployment patterns larger teams run on Kubernetes.

If you’re already paying for a VPS or dedicated server and want a more structured deployment workflow than a bash script, Kamal is worth looking at. The setup takes a few hours; after that, deploys are one command.

# Install Kamal
gem install kamal

# Initialize in your project
kamal init

# Deploy
kamal deploy

Kamal doesn’t manage your infrastructure. You pick the server; Kamal handles the container lifecycle on it, including a graceful shutdown sequence so in-flight requests finish before a container is replaced.

Choosing Between Them

Use Fly.io when:

• Your API has WebSocket or long-lived connection requirements
• You need low latency for users in multiple regions
• You want to co-locate your database with your API on the same network
• You’re comfortable with a CLI-first workflow

Use Railway when:

• You want the fastest path from code to deployed backend
• You need managed databases without operational overhead
• You’re running staging environments or smaller production workloads
• You prefer a UI for visibility and management

Use Kamal (on a VPS) when:

• You have predictable traffic and want fixed infrastructure costs
• You’re running on hardware you already own or have budget commitments for
• You want full control over the runtime environment

Stay on Vercel when:

• Your backend work is primarily Next.js API routes or edge functions
• You don’t need persistent connections or long-running processes
• Static site generation or ISR is the main deployment pattern

If you’re unsure whether your workload even needs a server at all, edge functions vs. serverless is worth reading before you commit to Fly or Railway.

None of these platforms is a wrong choice for a standard backend service. The decision mostly comes down to how much control you want versus how much you want abstracted away. Fly gives you more knobs; Railway gives you fewer. Both are cheaper and faster to operate than rolling your own on AWS for teams under 20 people.