I think one thing I'm struggling to understand is the underlying compute model and how it works. If you compare yourself to a cloud like AWS or a Neocloud platform like, say, Railway or Fly.io, what are you doing differently?
Originally Railcode started out as an AI-native Retool let's say. But while Retool added an MCP and now supports custom code they're still a reasonably rigid platform. I don't really believe a platform that's rigid works out today, and I'm also all-in on the user's own harness [1]. As a result we ended up building something that's a lot closer to Vercel as far as the flexibility and ownership you have, but with the governance layer of Retool.
I'd say we're technically a layer above plain compute.
In practice:
- Projects are just code. We can host static frontends and JavaScript serverless functions for the backend.
- When you deploy an app it's immediately behind company auth
- If you want to connect some data, you can e.g. connect an MCP and allow the Growth team to use that connection. Projects the Growth team builds can then use railcode.connector('posthog') and no API keys are ever shared
- If you connect a warehouse, you can create specific queries and assign them to users or roles to be able to build on top of, same model as connectors
- If you need to store some data, you can just do railcode.db.collections.create('orders') and it works. For an LLM it's railcode.llm.generate and admins can configure keys and manage spend centrally
- You can set a design system that agents follow when building apps in Railcode
Non-technical users don't need to manage infra or spin up a database (like they would on Vercel) and admins retain control and observability over everything that gets built.
Companies have started to build some version of this internally on top of Vercel, Railway, and Cloudflare, so I'd say we're somewhere in between an application layer thing and compute.
Genuinely curious: what would you call it if not a cloud?