AI Platforms & Infrastructure
Serving, scaling, evaluating, and governing models as production infrastructure.
By the end of this lesson, describe the platform concerns that turn a model into a reliable, governed production service.
Running one model call is easy. Running AI as production infrastructure — many models, many teams, real traffic, real risk — is a platform problem. An AI platform provides the shared serving, routing, safety, evaluation, and governance layers so individual apps don't each reinvent them.
First, see the whole system. Then we’ll open it up.
AI gateway in front of models
- An AI gateway fronts multiple model providers, handling routing and fallback, rate limits and budgets, guardrails, and logging/evals for all apps and agents.
flowchart LR apps[Apps / agents] --> gw[AI gateway] gw --> route[Route + fallback] gw --> limit[Rate limit + budget] gw --> guard[Guardrails] gw --> obs[Logging + evals] route --> m1[Model A] route --> m2[Model B]
Core concerns: serving (GPU efficiency via batching and KV-cache reuse — see the inference lesson), a gateway that unifies providers with routing, fallback, rate limits, and cost controls, guardrails (input/output filtering, PII redaction, injection defense), and evals — the AI equivalent of tests, measuring quality on fixed datasets so you can catch regressions when prompts or models change.
This is where the abstraction starts leaking.
You can't unit-test a probabilistic system the way you test code. A prompt tweak that fixes one case silently breaks three others, and there's no compiler to catch it. Without systematic evals, 'it looks better' is the only signal — which is why eval infrastructure, not clever prompts, separates mature AI platforms.
A platform (gateway, guardrails, evals, governance) adds real infrastructure and a team to run it, in exchange for consistent safety, cost control, provider flexibility, and auditability across every AI feature. Letting each app call providers directly is faster to start and ungovernable at scale.
Why are evals essential on an AI platform?
Because model behavior is probabilistic and can't be unit-tested; evals measure quality on fixed datasets so prompt or model changes don't silently regress.