Quick Verdict: Fly.io vs Railway
Bottom Line
Fly.io gives you Firecracker microVMs across 35+ global regions with hardware-level isolation, GPU access, and fine-grained infrastructure control. Railway gives you the fastest path from git push to running service, with per-second billing and a visual canvas for managing multi-service projects. Choose Fly.io when latency, global distribution, or sandbox isolation matter. Choose Railway when deployment speed and simplicity matter more than infrastructure control.
Architecture: MicroVMs vs Containers
This is the core difference. Everything else flows from it.
Fly.io: Firecracker MicroVMs
Each app runs in its own Firecracker microVM with hardware-level KVM isolation. Boot time is under a second. Memory overhead is less than 5 MiB per VM. The same technology powers AWS Lambda. You package apps as Docker images, but execution happens inside real VMs, not shared-kernel containers. This means stronger isolation boundaries and the ability to run untrusted code safely.
Railway: Container PaaS
Railway builds and runs Docker containers. Push code, Nixpacks auto-detects the framework, builds an image, and deploys. No Dockerfile required for most languages. Services run as standard containers with shared kernel isolation. The project canvas gives you a visual graph of all services, databases, and their connections. Simpler model, fewer moving parts.
What the Architecture Difference Means in Practice
Containers share the host kernel. A kernel exploit in one container can theoretically compromise neighbors. Firecracker VMs each get their own guest kernel, so a compromise stays contained. For most web apps, this distinction is academic. For AI agent sandboxing, where you run arbitrary code from untrusted sources, it is the entire decision.
Fly.io's VM model also enables features Railway cannot offer: per-region placement, WireGuard private networking between VMs, volume mounts with cross-region replication, and GPU attachment. Railway's container model enables features Fly.io makes harder: instant zero-config deploys, automatic framework detection, and a visual multi-service topology.
Pricing
Both platforms bill per-second for compute. The cost difference depends on utilization patterns.
| Item | Fly.io | Railway |
|---|---|---|
| Base plan | Pay-as-you-go (no minimum) | Hobby $5/mo, Pro $20/mo |
| 1 vCPU / 2 GB always-on | ~$10.70/mo | ~$20/mo |
| 4 vCPU / 8 GB always-on | ~$42.79/mo | ~$80/mo |
| Idle workloads | Scale-to-zero (Machines API) | Per-second billing, near-zero when idle |
| Managed Postgres | Yes (Fly Postgres) | Yes (one-click provision) |
| Volume storage | $0.15/GB/mo | $0.25/GB/mo |
| Bandwidth | $0.02/GB egress | $0.05/GB egress |
| IPv4 address | $2/mo per app | Included |
| GPUs | A100s, L40S available | Not available |
The Utilization Factor
Fly.io is roughly 2x cheaper for always-on workloads at equivalent specs. But Railway's per-second billing means a service that runs 4 hours/day costs about 1/6 the monthly rate. For staging environments, cron workers, and dev/test services with intermittent usage, Railway's model can undercut Fly.io even at higher per-unit rates.
Fly.io also supports scale-to-zero via the Machines API, but configuring it requires more setup than Railway's automatic idle detection.
Developer Experience
Fly.io: CLI-First, Dockerfile-First
flyctl is the primary interface. Deploy with 'fly launch' and 'fly deploy'. Configuration lives in fly.toml. Fly.io expects a Dockerfile (or generates one). The CLI is powerful and scriptable, but there is a learning curve. You configure regions, scaling policies, health checks, and secrets through flyctl or the dashboard.
Railway: Git Push and Done
Connect a GitHub repo. Railway detects the framework, builds it, deploys it. Often under a minute from first push to live URL. The project canvas visualizes your entire stack: web server, workers, databases, cron jobs, and their environment variable connections. Railway's dashboard is widely regarded as one of the best in the PaaS space.
Where DX Diverges
Railway's setup time for a typical web app is measured in minutes. Connect repo, click deploy, get a URL. Database? Click to add Postgres. Environment variables? Set them in the canvas and they propagate automatically.
Fly.io's setup takes longer on first deploy but gives you more control. You choose regions, configure health checks, set up volumes, and define scaling rules. For teams that want infrastructure-as-code with deterministic deployments, fly.toml plus flyctl is closer to what they already know from Docker Compose or Kubernetes manifests.
Scaling and Global Distribution
This is where the platforms diverge most sharply.
| Capability | Fly.io | Railway |
|---|---|---|
| Regions | 35+ worldwide | Single region |
| Global routing | Anycast (auto-nearest region) | Not applicable |
| Auto-scaling | Per-region, configurable | Automatic (vertical) |
| Scale-to-zero | Yes (Machines API) | Yes (automatic idle) |
| Horizontal scaling | Multi-region replicas | Multiple instances (single region) |
| Private networking | WireGuard mesh (6PN) | Internal networking between services |
If your users are in one geography and your app is a standard web service, Railway's single-region model is fine. Most SaaS apps don't need multi-region. But if you serve users across continents, or if you need to place compute near data sources (IoT, edge processing), Fly.io's 35+ region mesh with Anycast routing is a capability Railway does not have.
Fly.io also connects all your VMs with a WireGuard-based private network (called 6PN), meaning services in different regions can communicate securely without exposing traffic to the public internet. Railway offers internal networking between services within a project, but only within the same region.
AI Agent Sandboxing
Coding agents need isolated environments to run generated code. The isolation model determines how much you can trust the sandbox.
Fly.io: Built for Sandboxing
Firecracker VMs provide hardware-level isolation via KVM. Each sandbox is a separate VM with its own kernel. In January 2026, Fly.io launched Sprites: purpose-built stateful sandboxes for AI agents. Sprites boot in seconds, persist filesystem state between sessions, auto-idle to stop billing, and support up to 100 GB storage. A Sprite-based agent can install packages once, run multiple tasks, and resume days later.
Railway: General-Purpose Containers
Railway runs standard containers with shared-kernel isolation. Suitable for trusted application code, but not designed for adversarial workloads like arbitrary AI-generated code. No sandbox-specific features like snapshot/restore, per-process logging, or dedicated isolation APIs. You can run an agent's code on Railway, but the isolation guarantees are weaker than VM-based alternatives.
Sprites vs Generic Fly Machines
Sprites are Fly.io's dedicated sandbox product, distinct from standard Fly Machines. Sprites add persistence (filesystem survives between sessions), automatic idle suspension (stop billing when inactive), and a simplified API for agent workflows. Standard Fly Machines require you to build these capabilities yourself with Docker images and the Machines API.
For teams building AI agents that execute untrusted code, the choice is clear: Fly.io's VM-based isolation is architecturally superior to Railway's container model. Sprites make it turnkey. Other dedicated sandbox platforms like E2B and Modal also use Firecracker, validating the microVM approach for this use case.
When Fly.io Wins
Global Distribution
35+ regions with Anycast routing. Users in Tokyo hit the Tokyo instance. Users in Frankfurt hit Frankfurt. No CDN hacks, no manual region management. If your app serves users across continents, Fly.io's architecture handles this natively.
AI and GPU Workloads
A100 and L40S GPUs available. Sprites for agent sandboxing. Hardware-isolated VMs for running untrusted code. If you're building AI infrastructure, Fly.io has the compute types and isolation model you need.
Cost at Scale
Roughly 2x cheaper than Railway for always-on workloads at equivalent specs. A 4 vCPU / 8 GB instance runs ~$43/month on Fly.io vs ~$80/month on Railway. The gap widens with larger configurations.
Infrastructure Control
Region selection, WireGuard networking, volume mounts, health check tuning, custom scaling policies. Fly.io exposes the knobs. If you've outgrown 'deploy and forget' and need to optimize placement, networking, or resource allocation, Fly.io gives you the tools.
When Railway Wins
Time to First Deploy
Connect GitHub, push code, get a live URL. Often under a minute. Nixpacks auto-detects frameworks and builds without Dockerfiles. For prototypes, hackathons, and early-stage projects, nothing is faster.
Multi-Service Projects
The project canvas is genuinely useful. See your web server, worker, database, and Redis instance as a connected graph. Environment variables propagate between services. Adding a Postgres database is one click. For full-stack apps with multiple components, Railway's visual model reduces cognitive load.
Bursty Workloads
Per-second billing with automatic idle detection. A staging environment that runs 2 hours/day costs a fraction of its monthly rate. Dev environments, cron workers, and CI/CD services benefit from Railway's usage-based model.
Small Teams Without DevOps
No fly.toml to configure. No region decisions. No Dockerfile required. Railway removes infrastructure decisions and lets you focus on application code. For teams of 1-5 developers shipping a SaaS product, the reduced operational burden is worth the higher per-unit cost.
Frequently Asked Questions
Is Fly.io or Railway cheaper in 2026?
For always-on workloads, Fly.io is roughly 2x cheaper at equivalent specs. A 1 vCPU / 2 GB instance costs ~$10.70/month on Fly.io versus ~$20/month on Railway. For bursty workloads that idle frequently, Railway's per-second billing can be cheaper since you only pay for active compute time. Railway's Hobby plan starts at $5/month with $5 of included usage credits.
Does Fly.io use Docker containers?
Fly.io uses Docker images as the packaging format but runs them inside Firecracker microVMs. Each app gets hardware-level KVM isolation with its own guest kernel, not shared-kernel container isolation. Boot time is under a second with less than 5 MiB of memory overhead per VM. This is the same technology AWS uses for Lambda.
Can Railway deploy globally like Fly.io?
No. Railway deploys to a single region. Fly.io distributes apps across 35+ regions with Anycast routing to the nearest instance. If global latency matters, Fly.io has a structural advantage Railway cannot replicate without rearchitecting its platform.
Which platform is better for AI agent sandboxing?
Fly.io. Its Firecracker microVMs provide hardware-level isolation for running untrusted code. Sprites, launched in January 2026, are purpose-built stateful sandboxes with sub-second boot, persistent filesystems, and automatic idle billing suspension. Railway runs standard containers without equivalent isolation guarantees for adversarial workloads.
Does Railway have a free tier?
No. Railway removed its free tier in 2023. The Hobby plan costs $5/month with $5 of included resource credits. The Pro plan costs $20/month with $20 of included credits. Both plans bill per-second for compute, memory, and storage beyond the included amount.
Related Comparisons
For AI Agent Sandboxes, Try Morph
Morph provides fast, isolated sandboxes for AI coding agents. Hardware-level isolation, sub-second boot, and persistent state. Built for teams shipping agents that run untrusted code at scale.