Cloud Runtime and Simple Deployment Paths
Your deployment story should be explainable before it is automated.
Design a simple deployment path for an AI-native SaaS with separate public and app runtimes.
The lesson is public. The pressure loop lives inside the app where submissions, revision, and AI review happen.
A local stack blueprint and deployment hardening plan.
Each lesson contributes to a week-level artifact and eventually to the shipped AI-native SaaS.
Cloud Runtime and Simple Deployment Paths
This lesson focuses on deployment architecture as an explainable system, not a pile of platform defaults.
When deploy paths are unclear, incidents become harder to diagnose and recovery becomes improvisation. AI systems suffer more because they already have more moving parts than ordinary CRUD apps.
A good deployment path states what runs where, what environment each component needs, how changes are released, and how failures are rolled back.
What the machine covers in this lesson.
This lesson focuses on deployment architecture as an explainable system, not a pile of platform defaults.
When deploy paths are unclear, incidents become harder to diagnose and recovery becomes improvisation. AI systems suffer more because they already have more moving parts than ordinary CRUD apps.
A good deployment path states what runs where, what environment each component needs, how changes are released, and how failures are rolled back.
For this product, the public academy and the authenticated app have different concerns. Public pages want static speed and SEO. The app wants auth, APIs, and runtime logic. The lesson is not “use platform X.” The lesson is to make those constraints explicit and design the release path around them. Simplicity matters because every hidden edge becomes downtime later.
A Pages deployment serves the public academy while a Worker runtime serves auth and app routes. The deployment plan states which environment variables belong to each target and what happens when one deploy succeeds and the other fails.
Common failures include sharing secrets across surfaces unnecessarily, having no rollback note, and confusing build-time content generation with request-time runtime dependencies.
Further reading the machine expects you to use properly.
The full lesson is inside the app.
Submit the exercise, receive AI review, close the gaps the machine finds, and unlock the next lesson in the sequence.