Adios
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Platform comparison

Adios vs Railway: workspace or infrastructure platform?

Both run apps and data services. Adios also includes the source-backed AI development loop.

Start building with Adios →See the practical difference

Last reviewed: July 21, 2026

✦

Choose Adios when…

You want AI agents to edit and verify source before the same platform deploys the reviewed version.

◇

Choose Railway when…

You already have a coding workflow and want Railway's infrastructure, databases, environments, and observability.

How they work

The practical difference

Adios starts in the project workspace. Railway starts with infrastructure delivery through Git, CLI, images, or templates.

01

Primary workflow

Add the development workspace before infrastructure.

Both deploy applications and services. Adios also includes the environment where developers and AI change the source.

>_

Adios

AI coding workspace plus source-to-deploy flow

◇

Railway

Application and service infrastructure from GitHub, CLI, image, or template

02

Agent workflow

Let AI change and prove the source.

Railway gives external agents strong infrastructure tools. Adios puts the source-editing agent inside the project workspace.

>_

Adios

AI plans, project file edits, commands, previews, and approval-sensitive deploys inside the workspace

◇

Railway

Dashboard Agent, agent skills, Claude Code plugin, CLI, and local or hosted MCP

03

Operations

Trace production state back to the change.

Both provide deployment and runtime evidence. Adios keeps that evidence in the same flow as the source edit and preview.

>_

Adios

Build and runtime logs tied to source, version, replica, health, and route state

◇

Railway

Deployment actions, health checks, metrics, environment logs, deployment logs, and HTTP logs

Strong in different ways

Choose the workflow and product shape that match the application your team needs to ship.

>_

Where Adios shines

  • ✓
    The development loop is includedEdit files, run commands, inspect diffs, preview, and deploy in one context.
  • ✓
    Configuration travels with the sourceThe app declares build, health, secrets, dependencies, and routes in adios.yaml.
  • ✓
    Release evidence points back to the changeBuilds, logs, health, replicas, and routes stay tied to the source.
◇

Where Railway shines

  • ✓
    Infrastructure-first deliveryDeploy services from GitHub, the CLI, container images, or Railway templates.
  • ✓
    Databases and environmentsManage services, databases, volumes, variables, and deployment environments.
  • ✓
    Agent and operations toolingUse Railway's CLI, MCP servers, agent skills, and plugins to operate infrastructure.

The Adios workflow

Use a workspace or Git push, then release the version you reviewed.

01

Start with source

Connect Git, sync a local project, or use a template.

02

Build and preview

Work in a workspace or deploy from a Git push.

03

Review adios.yaml

Keep runtime, health, secrets, and resources in Git.

04

Release when healthy

Verify the version, then promote it to production.

Adios in practice

From AI conversation to running software.

Connect ChatGPT through MCP, keep deployment in Git, and automate backend work with visible workflows.

01

ChatGPT + MCP

Bring Adios tools into ChatGPT.

Connect ChatGPT to scoped Adios tools. It can work with project source, commands, previews, logs, and deployment context from the conversation.

Explore the MCP integration →
ChatGPT × Adios MCP demo
adios.yaml
name: node-api
region: de
replicas: 2

build_cmd: npm ci && npm run build
start_cmd: node dist/server.js

runtime:
  name: node@24
  port: 8080
  memory_mb: 1024
  health_path: /healthz

secrets:
  DATABASE_URL: secret://DATABASE_URL
02

Deployment as code

Production stays beside the source.

One readable file defines the build, start command, health check, memory, replicas, and secret references.

Review infrastructure changes with the code and reproduce the same deployment without rebuilding settings by hand.

Read the manifest docs →
03

Visible workflows

Automate work. See every run.

Start on a schedule or webhook, pass data through ordered steps, and keep status, logs, outputs, and errors attached to the run.

Trigger
Process
Publish
Explore workflows →Read the docs →
workflows/daily-market-brief.yaml
workflow_id: daily-market-brief
enabled: true

triggers:
  - type: cron
    cron: "0 13 * * 1-5"
  - type: webhook
    event: market.brief.requested

secrets:
  MARKET_DATA_API_KEY: secret://MARKET_DATA_API_KEY

steps:
  - step_id: fetch-prices
    kind: http
    command:
      method: GET
      url: https://api.example.com/quotes

  - step_id: select-close
    kind: data-json
    dependencies: [fetch-prices]
    command:
      from_step: fetch-prices
      path: ".quotes"

  - step_id: publish-brief
    kind: http
    dependencies: [select-close]
    command:
      method: POST
      url: https://dashboard.example.com/api/market-briefs
2 triggers3 visible stepsSecret-backed access

Adios-only capabilities

What Adios adds to the workflow

3 key differences
CapabilityIncluded with AdiosRailway approach
Built-in AI coding workspace
✓

AI edits project files, runs commands, opens previews, and leaves a reviewable diff inside Adios.

—

Railway equips external coding agents to deploy and operate infrastructure through CLI, MCP, skills, and plugins.

Workspace preview before deploy
✓

Run the changed application in a stable project workspace before creating a production deployment.

—

Railway provides persistent, staging, and temporary PR environments after infrastructure deployment.

One source-to-runtime context
✓

Source, commands, preview, manifest, deployment, replicas, health, and logs stay connected.

—

Railway focuses its project context on services, environments, deployments, metrics, and logs.

Pricing

Adios plans start at $10 per month

Choose the compute, memory, storage, bandwidth, and included AI usage that fit your applications.

Starter · Dev · Pro

Starter

For a first app, small API, or personal workspace.

$10/month

  • ✓1 vCPU and 1 GB RAM
  • ✓10 GB storage
  • ✓500K included AI tokens
  • ✓500 GB included bandwidth
Popular

Dev

For active development across several apps.

$50/month

  • ✓2 vCPU and 4 GB RAM
  • ✓50 GB storage
  • ✓2.5M included AI tokens
  • ✓2 TB included bandwidth

Pro

For production projects that need more headroom.

$99/month

  • ✓4 vCPU and 8 GB RAM
  • ✓100 GB storage
  • ✓5M included AI tokens
  • ✓5 TB included bandwidth

Custom domains and TLS are included. Final terms and availability are shown before checkout.

Compare all plan details →

Frequently asked questions

?What is the main difference between Adios and Railway?

Adios starts in the project workspace. Railway starts with infrastructure delivery through Git, CLI, images, or templates.

?When should I choose Adios over Railway?

You want AI agents to edit and verify source before the same platform deploys the reviewed version.

?When is Railway the better fit?

You already have a coding workflow and want Railway's infrastructure, databases, environments, and observability.

?Can I deploy an existing application with Adios?

Yes. Add adios.yaml to a repository or local project, then deploy from the CLI or a workspace.

Keep exploring

See how Adios works.

View all documentation →
>_

Workspaces

Source, commands, previews, and logs.

YML

adios.yaml

Build, runtime, health, and secrets.

→

Workflows

Triggers, steps, runs, and errors.

+

Templates

Apps and managed services, connected.

Build, preview, and release from one project.

Bring an existing repository or start from a template. Keep AI work, deployment, services, and operations connected.

Start building with Adios →