{"title":"Fly.io vs Railway","slug":"fly-io-vs-railway","tools":[{"name":"Fly.io","slug":"fly-io","category":"hosting","type":"cloud","website":"https://fly.io","pricing":"freemium","pricing_tiers":["Free (3 shared VMs)","Pay-as-you-go ($0.0015/hr per shared CPU)","$29/mo Launch","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript","go","rust","ruby","elixir"],"frameworks":[],"agent_features":{"serverless":false,"containers":true,"edge_compute":true,"ai_tooling":null,"mcp_hosting":false},"compliance":["soc2","gdpr"],"best_for":"Run containers close to users globally — best for latency-sensitive agent APIs and real-time agent interactions","limitations":"Steeper learning curve than Railway; pricing can be unpredictable with auto-scaling; smaller community; documentation is sometimes sparse","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://fly.io/docs","pricing":"https://fly.io/pricing"}},{"name":"Railway","slug":"railway","category":"hosting","type":"cloud","website":"https://railway.app","pricing":"freemium","pricing_tiers":["Free trial ($5 credit)","$5/mo Hobby","$20/seat/mo Pro","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript","go","rust","java","ruby"],"frameworks":[],"agent_features":{"serverless":false,"containers":true,"edge_compute":false,"ai_tooling":false,"mcp_hosting":false},"compliance":["soc2"],"best_for":"One-click deploy for any language/framework — best for long-running agent processes that don't fit serverless constraints","limitations":"Smaller ecosystem than Vercel/Netlify; less mature CDN and edge story; costs can surprise with always-on containers; no built-in AI tooling","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://docs.railway.app","pricing":"https://railway.app/pricing"}}],"category":"hosting-deployment","last_verified":"2026-05-09","body":"Fly.io and Railway both support persistent processes, private networking, and managed databases. They differ on VM model, networking scope, multi-region capability, and infrastructure control. Fly.io wins on networking, multi-region deployment, and VM control. Railway wins on deployment simplicity and database options.\n\n## Where Fly.io wins\n\n* **Private 6PN networking with .internal DNS, TCP/UDP, and WireGuard.** Fly.io's private IPv6 network (6PN) spans all Machines across an organization with automatic `.internal` DNS resolution, full TCP and UDP support, and WireGuard tunnels. Agent services discover each other via DNS names (`app-name.internal`) across regions. Railway provides WireGuard-encrypted private networking with internal DNS (`service.railway.internal:PORT`), but scopes it to a single project. Cross-project communication on Railway requires public endpoints. Fly.io's organization-wide network enables cross-app communication.\n\n* **Multi-region deployment with subsecond Machine start.** Fly Machines deploy to 18 regions with explicit per-region control. Machines start in subseconds, auto-start on incoming requests, and auto-stop when idle. This enables agents to spin up on demand in the region closest to each caller, avoiding idle replicas. Railway deploys to specific regions but doesn't support auto-start/auto-stop. Replicas stay on within their region. For global agents with latency-sensitive tool calls, Fly.io's multi-region model cuts latency without idle overhead.\n\n* **Firecracker microVMs with infrastructure-level control.** Fly Machines are Firecracker microVMs with full Linux environments, configurable CPU/RAM (shared-cpu-1x to performance-16x), and direct Machine lifecycle control via REST API. You control which Machines run where, with what resources, and when they start and stop. Railway abstracts container infrastructure—you deploy code or Docker images, and Railway manages the lifecycle. For agent architectures that need fine-grained control over resource allocation and placement (e.g., dedicating Machines to specific MCP tool servers), Fly.io provides it.\n\n## Where Railway wins\n\n* **Managed Postgres, MySQL, Redis, and MongoDB as first-class project services.** Railway deploys managed databases as services within the same project, accessible over the private network with no configuration. Postgres, MySQL, Redis, and MongoDB are all available. An agent's orchestrator and its databases share a project environment with automatic connection string injection. Fly.io offers managed Postgres and partners with Upstash for Redis, but MySQL and MongoDB require self-managed containers. Railway's database options are wider with less setup.\n\n* **Simpler deployment model with Git-push and automatic Docker builds.** Railway deploys from Git push with automatic Nixpacks or Docker builds, environment detection, and a dashboard for service management. Adding a Postgres database is a one-click operation. Fly.io requires a `fly.toml` configuration file, explicit `fly deploy` commands, and manual Machine management. For teams that want to deploy an agent stack quickly without infrastructure management, Railway's model reduces overhead.\n\n* **Per-service environment variables with shared variables across services.** Railway supports per-service variables, shared variables across all services, and environment-scoped variables (production, staging, development). This maps directly to multi-service agents where the orchestrator, tool servers, and databases each need different credentials but share common configuration. Fly.io manages secrets per-app via `fly secrets set`, but cross-app sharing requires manual duplication or external management.\n\n## The agentic difference\n\nBoth platforms support the core agent deployment pattern: persistent containers, private networking, managed databases. The difference is control versus convenience. Fly.io provides VM-level control—multi-region placement, per-Machine lifecycle, organization-wide networking, TCP/UDP support—for architectures that need fine-grained deployment decisions. Railway provides PaaS convenience—Git-push deploys, one-click databases, automatic builds—for teams that want to focus on application logic.\n\nFor a simple agent stack (orchestrator + Postgres + Redis), Railway reaches production faster. For complex agent architecture (multi-region MCP servers, dedicated Machines per tool, demand-based scaling, cross-app communication), Fly.io provides the primitives. Neither platform offers GPU compute or managed AI inference—both require external providers for model calls.\n\n## When to pick which\n\n* **Pick Fly.io** when the agent needs multi-region deployment, fine-grained VM control, organization-wide private networking with TCP/UDP, auto-start/auto-stop for cost-efficient scaling, or infrastructure primitives your team manages directly.\n\n* **Pick Railway** when the agent team wants PaaS-level simplicity, needs managed Postgres, MySQL, Redis, or MongoDB with no configuration, benefits from Git-push deploys and automatic builds, and doesn't need multi-region or VM-level control."}