Control Plane vs Fly.io

Both platforms take a standard container and get it running close to your users without you managing servers. They differ on functionality and portability. Control Plane is a multi-cloud runtime: it runs the same container on hardened, managed infrastructure by default, or in your own AWS, GCP, Azure, and on-prem accounts, stitched into one virtual cloud with geo-intelligent routing, full VMs, and platform-level compliance. You control where each workload runs and stay portable across the major clouds. Fly.io deploys your app as Fly Machines onto its own global edge network, tied to Fly's platform and region footprint.

The core difference: a portable multi-cloud runtime vs a single-vendor edge

Control Plane is a runtime that unifies clouds. By default it runs your standard containers on hardened, managed clusters in its own cloud accounts, in the regions you choose. It can also run them across any mix of clouds, regions, Kubernetes clusters, and on-prem servers in your own accounts, stitched into one virtual cloud, with placement, identity, networking, security, and scaling handled uniformly on top. Requests are steered by geo-intelligent DNS with health-based failover to the nearest healthy deployment, typically around 20 to 30 milliseconds away. You get edge-like proximity while keeping control over which cloud and region each workload runs in, plus full VMs and platform-level compliance.

Fly.io works differently: you hand it a container, pick some regions, and it runs your app as Fly Machines on its own edge infrastructure, working at the machine level for each instance. The infrastructure is Fly's own, so workloads run on Fly's network rather than in your cloud accounts, and your global footprint is limited to Fly's region list. The question is whether you want an app tied to a single vendor's edge or a compliance-grade layer that unifies clouds, runs VMs, and lets you own where each workload runs.

Why teams look past Fly.io

The reasons teams weigh Control Plane against Fly.io come down to functionality, control, and portability. The first is control over where workloads run: teams that need data, compute, and spend to sit inside their own AWS, GCP, or Azure agreements, or in specific regions, cannot commit everything to a third party's edge. The second is data residency and compliance, where you must be able to say exactly which cloud and region a workload runs in, backed by controls like PCI DSS Level 1, SOC 2 Type II, HIPAA, and GDPR. The third is portability beyond a single provider's footprint: if your users or your regulators require specific regions across the major clouds, running across those clouds beats being limited to one network's edge locations. The fourth is scale and workload mix, when you need containers, VMs, cron, and stateful services, plus Sandboxes for AI agents and untrusted code, under one model. Fly.io hosts an app on its own edge; Control Plane covers those functionality, control, and portability requirements that a single-vendor edge does not.

Which one should you pick?

Control Plane vs Fly.io, side by side

Which fits your scenario

Control Plane fits: a company running in regulated AWS and Azure accounts that is expanding into new regions. The same standard containers run across AWS, GCP, Azure, and on-prem in the company's own accounts under one identity and network model, with geo-routing to the nearest deployment and no cluster it has to operate. Fly.io would host that workload on its own edge instead of the company's regulated accounts, so data residency and cloud-agreement ownership become hard to guarantee. Control Plane also reaches native cloud services credential-free through Universal Cloud Identity, so a workload in one cloud uses another cloud's services on temporary, least-privilege session credentials.

What Fly.io is. Fly.io is a single-vendor edge platform: you hand it a container and it runs as Fly Machines on Fly's own global network, working at the machine level in Fly's regions. Control Plane delivers the same edge-like proximity through geo-intelligent routing to the nearest deployment, and also runs across AWS, GCP, Azure, and on-prem in your own accounts, adds full VMs, and carries PCI DSS Level 1, so you keep placement control and portability instead of committing everything to one network.

Why teams consolidate on Control Plane. The moment regulated data, virtual machines, or workloads spanning more than one cloud and on-prem enter the picture, a single-vendor edge stops being enough. Control Plane gives you the same edge-like proximity through geo-routing to the nearest deployment, and adds control over which cloud and region each workload runs in, platform-level compliance, full VMs, and one identity and network model across every cloud, so you do not outgrow it and end up running two platforms.

Frequently asked questions

• What is the difference between Control Plane and Fly.io?

  Fly.io hosts your app on its own global edge infrastructure, deploying containers as Fly Machines to Fly's regions with machine-level control. Control Plane is a multi-cloud runtime. By default it runs your standard containers on hardened, managed clusters in Control Plane's own cloud accounts, in the regions you choose. It can also run them in your own AWS, GCP, Azure, and on-prem accounts, all as one virtual cloud with geo-intelligent routing to the nearest deployment, no cluster you have to operate, scale-to-zero, and compliance. Short version: Fly.io ties you to its edge network; Control Plane gives you a portable multi-cloud runtime with control over where workloads run across the major clouds.

• Does Control Plane run on my own cloud accounts?

  It can. By default Control Plane runs your workloads on hardened, managed clusters in its own cloud accounts, in the regions you pick. As an option, through BYOK or managed Kubernetes, it runs them inside your own AWS, GCP, Azure, and on-prem accounts, so data, compute, and spend stay under your ownership and cloud agreements. Fly.io runs your app on Fly's own edge infrastructure and does not offer that choice.

• Can Control Plane route traffic globally like Fly.io?

  Yes. Control Plane uses geo-intelligent DNS with health-based failover to send each request to the nearest healthy deployment, typically around 20 to 30 milliseconds away. The difference is reach: instead of Fly's own edge regions, Control Plane routes across deployments you place in AWS, GCP, Azure, and on-prem regions, so your global footprint follows the major clouds rather than a single provider's network.

• Do I have to operate Kubernetes or machines with Control Plane?

  You do not have to. Control Plane is built on Kubernetes, and by default you never touch it: there is no cluster, node pool, or machine to configure, because the platform places, scales, and heals each workload for you. If you do want a cluster, you can run one through Managed Kubernetes (MK8s), where Control Plane operates the cluster on your behalf, or Bring Your Own Kubernetes (BYOK), where it runs inside a cluster you already have. On Fly.io you work at the machine level, configuring and sizing Fly Machines, which is more to manage than Control Plane's default model.

• Does Control Plane support scale-to-zero and right-sizing?

  Yes. Serverless workloads scale to zero when idle and back up on demand, and Standard and Stateful workloads can scale on demand through KEDA. Capacity AI right-sizes CPU and memory automatically and bills on consumed or reserved capacity, so you are not manually sizing machines to peak the way you typically do on Fly.io.