tavin vs Railway

Last updated: 2026-05-04

Both Railway and tavin.cloud build from Git repos, run containers, expose domains, and now care about AI-assisted operations. The difference is no longer “which one has agents?” It is how narrow the agent’s authority is, how auditable the deployment handoff feels, and how much platform surface you want.

At a glance

tavin.cloudRailway
OriginNew (2026)Mature (since 2020)
Build sourceGitHub repo + Railpack, Dockerfile, or imageRailpack/build-system defaults; Docker supported
BillingPer-minute CPU + RAM, scale-to-zero pathBase subscription plus metered usage
RegionsSouth America-focused preview; US and South America workloads supported4 regions (us-east/west, EU, APAC)
DatabasesBring-your-own (managed coming)First-party Postgres, MySQL, Redis, Mongo
Agent controlScoped MCP tools, PATs, one-time deploy intents, audit logsRailway Agent, CLI agent, MCP, API
Templates / one-clicksNone yetLarge template library

When to pick Railway

When to pick tavin

Migration notes

A working Railway service moves to tavin by connecting the same GitHub repo and re-declaring environment variables. If the app already uses a Dockerfile, that contract is unchanged; otherwise Railpack handles the source build. Databases are the integration to replan — bring an external one (Neon, Supabase, etc.) until tavin’s managed databases land.

Current agent-context note

Railway now has public agent and MCP surfaces, so tavin should not be positioned as “Railway, but with MCP.” The sharper distinction is agent-safe deployment: explicit credentials, browser approval for one-time public repo deploys, and an audit trail built around every tool call.

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