What is Fly.io and how application deployment by regions works
Published on 2026-10-26 by frfrand
Fly.io and its distributed machines: latency, data, region, and comparison with VPS
There are projects where the user is close to your server, and others where you have them spread all over the world. Fly.io is interesting because it allows you to run applications in different regions. It sounds easy, but bringing the process closer does not mean the database is magically close to everyone.
I'm not going to sell you the idea that one platform is perfect for everyone. What's useful is understanding what work it saves you, what work remains yours, and how much it can cost to maintain it when the project stops being a test.
Fly.io: what it really offers you
Fly.io uses execution machines that you can deploy by region, with networking and storage according to the architecture. The strong point is geographical flexibility; the downside is having to design state, persistence, and communication between instances.
An example you could have tomorrow
A booking API receives visits from several countries. Replicating the frontend can reduce latency, but a booking needs to check available slots in a consistent database. If all regions write to a single remote PostgreSQL, the time for that write still exists and coordination matters.
How to get started without overcomplicating things
- Prepare the application to listen on the port and interface expected by its configuration.
- Install and authenticate the
flyctltool; initialize the deployment configuration. - Choose an initial region close to your database and your main users.
- Configure secrets and resources; verify that the service passes health checks.
- Add other regions only after measuring latency and understanding where writes occur.
The fifth step is often the one that gets skipped: opening the application from another device, performing a write operation, checking a log, and verifying it again after restarting. That is worth more than a screenshot where the panel says "deployed".
The mistake I would avoid
Do not copy volumes from one region to another assuming automatic synchronization. Design data consistency, failure recovery, and communication cost before scaling.
There is another simple rule: if the project has clients or payments, do not experiment with migrations directly on the only copy of the database. Test in a separate environment, keep a verifiable copy, and prepare how to roll back.
How would I solve the same in ArduMaker?
ArduMaker offers an alternative more focused on managing a VPS and its processes, databases, and domains. It is simpler to reason about for an application that can be concentrated in a single location; it does not replace Fly.io's multi-region deployment without additional architecture.
In a typical case, you could create the VPS in ArduMaker, connect the Git repository, install or manage PostgreSQL according to the application, define the execution process, and publish an HTTPS domain. If there is a worker that takes several minutes, separate it from the HTTP service. And if the project has several clients, organize them by Company to avoid mixed permissions and resources.
| Need | Fly.io | ArduMaker |
|---|---|---|
| Main model | Fly | VPS with management tools, Git, services, and MCP |
| System access | Depends on the platform or server where it is installed | VPS environment administration |
| Database and workers | Depend on the product and its configuration | Can run on the VPS according to available resources |
| Maintenance | Shared or assumed by you according to the service | Application and system require control and maintenance |
What I would look at before paying or migrating
I wouldn't choose based on a single figure on the pricing page. Note CPU, RAM, persistent storage, traffic, backups, support, and maintenance time. If you have to add a managed database, a worker service, or a licensed panel, include them. If you want redundancy between servers, it also needs to be designed: a VPS alone is not high availability.
So, which one would I use?
Fly.io stands out if you really need to bring execution closer to dispersed users. ArduMaker makes sense if you prefer a centralized environment with server control.
An honest test: publish a minimal API, connect it to a database, stop the process to see what the system logs, and try to recover it. The platform your team can repeat those steps on without suffering is usually the one that brings you the most value.
Sources and further reading
Final idea: first solve a real problem with the tool. Then compare how much effort it takes to maintain it. That's where the difference between a demo and an application you can deliver to a client appears.