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fly-io

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Use when deploying or operating an app on Fly.io — writing fly.toml, placing Machines in regions near users, attaching Volumes, managing secrets, or picking a scaling lever (autostop/autostart, scale count, fly-replay). NOT choosing which host to deploy on (that is `deployment`), NOT a git-push PaaS with no regions model (that is `railway`).

skills/fly-io/SKILL.md

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# Deploy on Fly.io

You are deploying an app to Fly.io: a `fly.toml`, Machines (Firecracker microVMs) placed in regions close to users, optional region-pinned Volumes, secrets, and the right scaling lever. Get the mental model right first, then the config follows. If none of that placement control matters, ../railway/SKILL.md is the git-push PaaS with no Machines/regions model.

## Mental model

- **App** is the logical unit. It owns a name, a `primary_region`, and config in `fly.toml`. Why: every command targets an app.
- **Process groups** (`[processes]`, e.g. `web`, `worker`) split one image into roles. Why: a web group takes traffic, a worker group does not — they bind services and VMs separately.
- **Machines** are Firecracker microVMs running your image. Each runs in exactly one region. Why: latency and volumes are per-Machine, so placement is the whole game.
- **Fly Proxy** is the anycast front door. It routes a request to the nearest running Machine, can start a stopped one, and obeys `fly-replay` headers. Why: it is what makes "global" cheap — you do not run a load balancer.
- **Volumes** are local NVMe disks pinned to one Machine in one region. No replication. Why: this single fact dictates every stateful architecture decision below.

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