Agentic AI Tutorial 0/80 lessons ~6 min read Lesson 69
Enterprise AI Infrastructure
Enterprise AI infrastructure includes a model gateway, prompt registry, eval platform, observability, secret management, and data isolation per tenant.
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
Enterprise AI infrastructure includes a model gateway, prompt registry, eval platform, observability, secret management, and data isolation per tenant. Often built on top of clouds (AWS Bedrock, Azure OpenAI, GCP Vertex).
Beginner analogy: Kubernetes for the AI stack — abstractions for everything you'd otherwise reinvent.
Understanding the topic
Core concepts:
- Model gateway: unified API, retries, cost capture.
- Prompt registry: versioned, with evals.
- Vector store as a service.
- Secret manager + per-tenant keys.
- Compliance & audit by default.
Syntax reference
Visual workflow / architecture:
bash
Apps ─► Model Gateway ─► OpenAI / Anthropic / Bedrock / Vertex│▼Prompt Registry · Eval Platform · Observability
Real-world use
Stripe AI Gateway, Cloudflare AI Gateway, Portkey, Helicone — production-grade gateways.
Best practices
- Centralise gateway + secrets early.
- Per-tenant cost capture is mandatory.
Common mistakes
- Direct provider calls from app code — no central control.
Hands-on exercise
Interview preparation — practice these questions:
- Q1. Five pieces of enterprise AI infra.
- Q2. Why a model gateway?
- Q3. Scenario: legal wants per-tenant cost reports. Where do you implement?
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