Fly.io vs Railway: Deploys, Idle Apps, Databases and Scaling Compared

You have a web process, a background worker and a Postgres database, and a shortlist that says Railway and Fly.io. Both show up on every list of Heroku alternatives, both deploy from git, and both will run the app on day one.

What separates them shows up after day one: where the configuration lives, how a release rolls out, what an idle service does, who looks after the database, and whether the platform can do more than host the app when the product asks for it. If Render is on the list as well, Fly.io vs Render covers that pair.

What Is the Difference Between Fly.io and Railway?

Railway is dashboard-first, with a CLI alongside, and organises work into projects: collections of services and databases laid out on a project canvas.^[1] Each service is a container deployed from a Docker image or a code repository.^[3] Railway builds with a Dockerfile when it finds one and with its Railpack builder otherwise. A railway.toml or railway.json file is optional; when present it overrides the dashboard for that deployment, and the dashboard is not updated to match.^[4]

Fly.io is built on Machines, fast-launching VMs that start and stop at subsecond speeds, controlled through a REST API or flyctl.^[5] Each Machine is a Firecracker microVM with hardware-virtualization isolation, the boundary Fly.io relies on to run different customers’ applications on shared hardware.^[2] fly launch scans the project, detects the framework and writes a fly.toml, plus a Dockerfile when the project needs one, and that file drives the first deploy and every one after it.^[6]

The practical difference is the source of truth. On Fly.io, the app’s configuration is one file in the repository, reviewed and versioned with the code. On Railway it lives in the dashboard, with a config file that can override it one deployment at a time.

From each platform’s own documentation. Checked 2026-09-14.^{[7][8][9][10][11][12][13][14]}

How Do Deploys Work on Fly.io and Railway?

Railway puts the deploy workflow in the dashboard. Its GitHub integration provisions an isolated preview environment for every pull request and offers instant rollbacks.^[1] PR environments are created when a pull request opens and deleted when it is merged or closed, and duplicating an environment copies its services, variables and configuration.^[13]

Fly.io puts it in fly.toml. A release_command runs in a temporary Machine built from the new image before any running Machine changes, which is where database migrations go, and a non-zero exit stops the deploy.^[15] The rollout strategy is set in the same file. Rolling, the default, replaces Machines one at a time; canary boots a single new Machine and checks its health before continuing; bluegreen boots a new Machine next to each running one and moves traffic once the new set passes health checks; immediate replaces everything at once. The deploy documentation describes the smoke checks that halt a release whose Machines keep restarting.^[16]

Deploying on every push is a GitHub Actions workflow that runs flyctl deploy with a FLY_API_TOKEN secret,^[17] and the superfly/fly-pr-review-apps action deploys a review app for each pull request.^[9] Because both the migration step and the rollout live in the repository, changing how the app ships goes through code review like everything else.

What Happens When Nobody Is Using the App?

Railway’s serverless setting sleeps a service that has gone quiet, and quiet means outbound. A service counts as inactive once it has sent no outbound packets for 5 minutes, where outbound includes network requests, database connections and even NTP, and in practice it sleeps 5 to 10 minutes after its last outbound traffic.^[11] It wakes on traffic from the internet or from another service in the same project. The first request to a slept service is delayed and may return a 502 Bad Gateway. A service that holds a connection pool open to its database, or ships telemetry, keeps sending packets and so does not count as idle.

Fly.io decides from the other direction. With autostop and autostart enabled, Fly Proxy measures each Machine’s load against its concurrency soft_limit, stops or suspends Machines when a region has more capacity than its requests need, and starts them again as requests arrive.^[7] The signal is the traffic coming in, not what the app sends out. A suspended Machine starts faster than a stopped one, min_machines_running keeps a floor in the primary region, and stopped or suspended Machines are not billed for CPU or RAM.

That behaviour is what makes quiet environments cheap to keep around: a staging copy, an internal tool, a review app per pull request. The serverless scaling explainer covers the general trade between stopping idle compute and the latency of the first request.

Does Fly.io Have Managed Databases Like Railway?

Yes. Fly.io runs Managed Postgres for the team, while Railway describes its own database templates as unmanaged. Railway provides templates for PostgreSQL, MySQL, MongoDB and Redis, each deployed as a service with a volume attached for its data.^[3] Its Postgres documentation says those templates “are considered unmanaged, meaning you have total control over their configuration and maintenance”.^[12] High availability is a conversion: an existing Postgres service becomes a cluster of Patroni-supervised nodes, an etcd cluster and HAProxy, with 2 to 7 streaming replicas, and the connection endpoints change in the process.^[18]

Fly.io Managed Postgres is a highly available cluster that Fly.io runs inside the organization’s private network. Fly.io handles automatic backups and recovery, high availability with automatic failover, performance monitoring and resource scaling, and every plan includes high availability, backups and connection pooling, with pgvector and PostGIS available.^[8] A database created with fly mpg create has failover from the start, with no conversion step and no connection strings to rewrite later. The Postgres hosting explainer sets out what else a production database should include.

Railway’s templates do cover more engines: a project that needs MySQL, MongoDB or Redis next to its app gets each one from a template.

How Do Workers and Scaling Work on Fly.io and Railway?

On Railway, scaling up is automatic and scaling out is manual. By default a service scales up to the vCPU and memory limits of its plan, and horizontal scaling means raising the replica count in the service settings, with public traffic distributed randomly across the replicas in a region.^[14]

On Fly.io, a worker is a process group in the same app as the web server:

[processes]
  web = "bin/rails fly:server"
  worker = "bundle exec sidekiq"

Each group runs on its own Machines, so fly scale count web=4 worker=2 sizes them independently.^[19] Machine count follows load in two ways. Autostop and autostart run a pool of Machines up and down with incoming requests, and fly-autoscaler reads a metric from Prometheus or Temporal every 15 seconds and creates, starts, stops or destroys Machines to match it, which suits queue workers whose load is not HTTP traffic.^[10] Every app in the organization also shares a private IPv6 network built from WireGuard tunnels, on by default and addressed by .internal names, so workers, internal APIs and databases reach each other without public endpoints.^[20]

What Happens When the App Needs More Than Hosting?

Most products eventually need compute on demand: an environment per customer, a job that runs a user’s code, an agent with a machine of its own.

On Fly.io it is the same Machines the app already runs on. Their count, lifecycle, resources and region are controllable through the REST API,^[5] so the app itself can start a Machine for a task and stop it afterwards. For multi-tenant products, Fly.io’s documentation recommends one app per customer, which keeps each customer’s secrets in their own app and lets a heavy customer scale without affecting the others.^[21]

For code nobody on the team wrote, Fly.io runs Sprites: persistent, hardware-isolated Linux environments for running arbitrary code, whose filesystem persists between runs and which incur no compute charges while idle.^[22] Sprites have been compared, workload by workload, with E2B, Modal Sandboxes, Daytona, Cloudflare’s Sandbox SDK and Vercel Sandbox.

How Does Pricing Work on Fly.io and Railway?

Railway charges a monthly plan fee that includes the same amount of usage credit, and bills resource consumption beyond it per second.^[23] Fly.io bills usage directly rather than through a plan:^[24] each Machine is billed per second for its CPU and memory while it runs, and a stopped Machine only for its root filesystem.^[25] Rates change, so the Fly.io calculator prices a specific configuration before deploying, and PaaS pricing compared runs one app through five platforms.

Which Should You Choose, Fly.io or Railway?

Choose Fly.io for most production web apps. The configuration is one file in the repository, releases run a migration step and a rollout strategy you choose, idle services stop based on the traffic they actually receive, the database is managed with failover from the smallest plan, and the same platform starts Machines and Sprites when the product needs compute of its own.

Fly.io is the better choice when:

• Deploy configuration should be versioned and reviewed with the code, in one place.
• Services keep database connections or telemetry running but should still stop when users leave.
• The database has to be managed, with backups and failover, rather than maintained by the team.
• Queue workers should scale on a metric, not only on HTTP requests.
• The product will run customer or agent workloads on demand.

Railway is the better fit when:

• The team wants to lay out its architecture visually on a project canvas.^[1]
• The app needs MySQL, MongoDB or Redis from a template next to its services.^[3]

Web Apps and Databases on Fly.io

A Railway project maps onto Fly.io service by service. Running fly launch in the project directory writes the fly.toml and deploys; services that belong together become process groups, and ones that stand alone become their own apps. Serverless sleep becomes auto_stop_machines, PR environments become the review apps action, and the database moves into Managed Postgres: create a cluster with fly mpg create, open a local connection with fly mpg proxy, and pipe pg_dump from the Railway database into psql.^[26] From there the app, its workers, its database and any Machines or Sprites it starts all run on one platform.

Frequently Asked Questions

Is Railway better than Fly.io?

No, not for most production web apps. Fly.io runs each process as a hardware-isolated Machine configured in one fly.toml, rolls out releases with a migration step and health-checked deploy strategies, and includes high availability and backups in every Managed Postgres plan. Railway suits teams that want to lay out services on a visual canvas or need MySQL, MongoDB or Redis templates.

Is Railway easier to use than Fly.io?

No. Neither needs hand-written configuration for a first deploy. Railway starts from its dashboard; on Fly.io, fly launch detects the framework, writes the fly.toml and a Dockerfile when one is needed, and deploys the app, and every later change ships with fly deploy.

Does a sleeping Railway service respond straight away?

No. The first request to a slept Railway service is delayed and may return a 502 Bad Gateway, and a service only sleeps 5 to 10 minutes after its last outbound traffic. Fly.io stops or suspends idle Machines based on incoming load and starts them as requests arrive, and a suspended Machine starts faster than a stopped one.

Are Railway databases managed?

No, by Railway’s own description: its database templates are unmanaged, and high availability is a conversion of an existing Postgres service. Fly.io Managed Postgres includes automatic backups and recovery, high availability with automatic failover, performance monitoring and connection pooling on every plan.

Does Fly.io have preview environments like Railway?

Yes. The superfly/fly-pr-review-apps GitHub Action deploys a Fly.io review app for each pull request, alongside a GitHub Actions workflow that runs flyctl deploy on every push.

Does Railway autoscale?

Vertically, yes: Railway scales a service up to its plan’s CPU and memory limits by default, while the replica count is set by hand. On Fly.io, autostop and autostart run a pool of Machines up and down with request load, and fly-autoscaler changes the Machine count from Prometheus or Temporal metrics.

How do I move an app from Railway to Fly.io?

Run fly launch in the project directory to write the fly.toml and deploy the app to Fly.io Machines, turning related services into process groups. For the database, create a Managed Postgres cluster with fly mpg create, open a local connection with fly mpg proxy, and pipe pg_dump from the Railway database into psql.

Sources

1. ^ Railway, “Railway vs Fly.io: Technical Comparison and Migration Guide”. docs.railway.com/platform/compare-to-fly. Checked 2026-09-14.
2. ^ Fly.io, “The Fly.io Architecture”. fly.io/docs/reference/architecture. Checked 2026-09-14.
3. ^ Railway, “Databases reference”. docs.railway.com/databases/reference. Checked 2026-09-14.
4. ^ Railway, “Config as Code”. docs.railway.com/config-as-code/reference. Checked 2026-09-14.
5. ^ Fly.io, “Fly Machines”. fly.io/docs/machines. Checked 2026-09-14.
6. ^ Fly.io, “Create an app with Fly Launch”. fly.io/docs/launch/create. Checked 2026-09-14.
7. ^ Fly.io, “Autostop/autostart Machines”. fly.io/docs/launch/autostop-autostart. Checked 2026-09-14.
8. ^ Fly.io, “Managed Postgres”. fly.io/docs/mpg. Checked 2026-09-14.
9. ^ Fly.io, “Git Branch Preview Environments on Github”. fly.io/docs/blueprints/review-apps-guide. Checked 2026-09-14.
10. ^ Fly.io, “Autoscale based on metrics”. fly.io/docs/launch/autoscale-by-metric. Checked 2026-09-14.
11. ^ Railway, “Serverless”. docs.railway.com/deployments/serverless. Checked 2026-09-14.
12. ^ Railway, “PostgreSQL”. docs.railway.com/databases/postgresql. Checked 2026-09-14.
13. ^ Railway, “Environments”. docs.railway.com/environments. Checked 2026-09-14.
14. ^ Railway, “Scaling”. docs.railway.com/deployments/scaling. Checked 2026-09-14.
15. ^ Fly.io, “App configuration (fly.toml)”. fly.io/docs/reference/configuration. Checked 2026-09-14.
16. ^ Fly.io, “Deploy an app”. fly.io/docs/launch/deploy. Checked 2026-09-14.
17. ^ Fly.io, “Continuous Deployment with Fly.io and GitHub Actions”. fly.io/docs/launch/continuous-deployment-with-github-actions. Checked 2026-09-14.
18. ^ Railway, “Upgrading PostgreSQL to High Availability”. docs.railway.com/databases/postgresql-ha. Checked 2026-09-14.
19. ^ Fly.io, “Run multiple process groups in an app”. fly.io/docs/launch/processes. Checked 2026-09-14.
20. ^ Fly.io, “Private Networking”. fly.io/docs/networking/private-networking. Checked 2026-09-14.
21. ^ Fly.io, “One App Per Customer - Why?”. fly.io/docs/machines/guides-examples/one-app-per-user-why. Checked 2026-09-14.
22. ^ Sprites, “Sprites Overview”. docs.sprites.dev. Checked 2026-09-14.
23. ^ Railway, “Pricing”. railway.com/pricing. Checked 2026-09-14.
24. ^ Fly.io, “Discontinued Plans”. fly.io/docs/about/discontinued-plans. Checked 2026-09-14.
25. ^ Fly.io, “Fly.io Resource Pricing”. fly.io/docs/about/pricing. Checked 2026-09-14.
26. ^ Fly.io, “Import data from another postgres cluster”. fly.io/docs/mpg/import. Checked 2026-09-14.