ShipAI Production Playbook for AI SaaS Teams
The exact baseline we use to keep auth, billing, and AI workflows stable in production.
Contents
The quickest path to production is not speed alone, it is predictable execution.
This post documents the baseline playbook we use when launching a new ShipAI-based product. It is optimized for small teams that need to move fast without accumulating operational debt.
1. Lock the Delivery Boundary
Before writing new features, define what can and cannot change during the launch window.
- Stable: auth provider, billing provider, primary model vendor, database topology.
- Flexible: landing copy, onboarding UX, email templates, support workflows.
Use this split to avoid mid-launch architecture pivots.
2. Keep API Responsibilities Sharp
A simple rule: route handlers orchestrate, services decide, utilities transform.
That gives you:
- predictable test scope,
- easier incident triage,
- safer refactors when requirements shift.
3. Define Operational Guardrails Early
Set these before first paid traffic:
- request timeout budget per critical API path,
- retry policy for all network-bound jobs,
- fallback behavior for model/tool outages,
- explicit limits around credits, quotas, and concurrency.
Why this matters
Most production incidents happen in undefined edges: retries, limits, and third-party failures. Guardrails reduce those edges.
4. Treat Documentation as Product Surface
Your docs should answer:
- how to configure environments,
- how billing limits are enforced,
- what recovery looks like when webhooks fail,
- how to rotate secrets safely.
When docs are accurate, onboarding and support time drops immediately.
5. Ship Weekly, Not Randomly
Use a fixed release rhythm:
Mon: backlog cut + scope lock,Wed: QA + migration dry run,Thu: deploy,Fri: post-release review.
Consistency compounds. Teams that keep cadence improve faster than teams with occasional bursts.