Three years ago, Xano started as an internal tool at a software agency. We were rebuilding the same backend infrastructure for every client—auth, databases, APIs, file storage, scaling, security—over and over again.
To save time, the team built a platform that could generate production-grade backends in days instead of months. That internal tool evolved into Xano, which now powers products used by both startups and large enterprises.
Companies like Deriv, an online trading broker with over 3 million customers, use Xano to ship features faster without managing infrastructure. Independent builders use it to launch products without hiring backend engineers. And now, with Xano 2.0, we’re extending that same capability to AI developers and teams adopting an AI-first SDLC.
The Problem AI tools like Cursor and Claude Code make it easy to generate working prototypes in minutes. But getting a production-ready backend—auth, schemas, server-side logic, scaling, monitoring, microservices—still takes weeks. You’re stuck choosing between: Fast but fragile: AI-generated code that only works in demos
Slow but solid: Traditional backend setups that stall iteration
We wanted a third option: a production-grade backend that could support large scale apps, but built in minutes instead of weeks
What’s new in Xano 2.0 1. XanoScript — Your backend infrastructure and logic as code—with a visual layer that makes it easy to verify what’s being built A scripting language that mirrors our visual function builder. Developers can work entirely in code or visually—changes stay synchronized. AI tools can generate and modify backend logic because it’s expressed as real code, not configuration. 2. MCP Server — AI has context and can build and modify your backend Xano 2.0 exposes a Model Context Protocol (MCP) server so tools like Claude Code and Cursor can securely connect, inspect schemas, and push updates directly to production. 3. VS Code Extension — Native IDE integration Browse, edit, and version-control Xano resources in VS Code with linting, autocomplete, and one-click deploys.
Under the hood Every Xano instance runs on Google Cloud (BYOC for Enterprise), provisioned with Docker and orchestrated by Kubernetes — standardized with best practices so you don’t have to. Security, scaling, and compliance are already done-for-you, letting you focus on building the logic that drives value without having to manage DevOps and string together multiple tools.
Who it’s for - AI developers who want to ship prototypes that can scale to production - Teams that want backend speed without compromising reliability - Non-backend engineers who want to build production systems with help from AI tools
We’d love feedback from HN on the architecture, the AI integration model, and where this approach might fall short. I’ll be around in the comments to discuss!
No comments yet.
