Adding a Brain and an Engine Room to GAS: Vertex AI and Cloud Run

When GAS alone isn’t enough, add two things: a brain (Vertex AI) and an engine room (Cloud Run). Both are separate adoptions and cross the line from “fully free” into “pay for what you use” (zero at idle, with a free allowance). So adopt them only when the trigger is clear.

The three-layer shape

GAS        = front desk (Gmail, Sheets, Drive, screens, human approval)
Cloud Run  = engine room (heavy or long-running work, latest AI)
Vertex AI  = brain (classification, summary, drafts)

Vertex AI first (easy entry)

Call Gemini straight from GAS via the advanced service (no separate server). Adopt when you need “intelligence” inside the app: auto-triaging inquiries, summarizing sheets/docs, drafting a quote from a file.

The standard pattern inserts a human-approval gate, not auto-execution:

Workspace data → GAS → Vertex (Gemini) → structured JSON → validation
  → human approval → Workspace action → audit log

Force structured JSON output + validation, and never let the AI act without human review.

Cloud Run when you hit the wall

A small cloud server you own. Adopt on any one signal:

• A job exceeds the GAS 6-minute limit.
• An account quota conflict actually occurs.
• You need the latest Gemini features faster than the advanced service ships them.

Split roles clearly: GAS = UI, Workspace access, approval cards; Cloud Run = heavy logic, latest AI. Design Secret Manager, logging, and retries alongside adoption.

The honest cost

Both need a Google Cloud project with billing enabled. Cloud Run is scale-to-zero, so it’s free at idle but billed under traffic. Go custom (“build it well while you’re at it”); skip no-code paths, which are weak on quality and differentiation.

One line to keep: intelligence via Vertex inside GAS (approval gate required), heavy work via Cloud Run: only when the trigger is clear.