Vercel Enterprise AI Platform
A platform handbook for designing enterprise AI products with AI SDK, AI Gateway, Workflow, Sandbox, and Queues.
Recently Updated Chapters
Centralize model routing, provider fallback, usage policy, and cost governance.
Use AI SDK as the application runtime layer for streaming, tools, agents, and telemetry.
Automate backoffice work with explicit approval events, identity, and side-effect controls.
Orchestrate repo-aware coding agents from issue intake to tests and pull request drafts.
Manage AI cost, latency, error budgets, rate limits, fallback, and workload classes.
Enterprise AI is not just attaching a model to an app. It is a platform design problem: separate control plane, runtime plane, data access, quality, security, and cost governance.
This handbook organizes Vercel's AI platform capabilities into an enterprise operating model for platform engineers, AI infrastructure leads, and Staff+ engineers.
Core View
Durable AI systems need a clear split between application experience, model routing, long-running work, isolated tools, async jobs, and governance.
Platform Model
Maturity Model
| Level | State | Signal | Promotion condition |
|---|---|---|---|
| L1 Prototype | Single model and prompt | Manual operation | Call logs and owners exist |
| L2 Controlled | Gateway and key separation | Policies are visible | Long work moves to Workflow |
| L3 Reliable | Fallback, retry, tracing, isolation | Errors and cost are reviewed | Quality dashboards stabilize |
| L4 Governed | Team-level accountability | Audit and risk reviews operate | Quarterly architecture review |