Production AI Infrastructure

Building the infrastructure required to make LLM-powered features reliable, observable, controllable, and production-ready.

API Call → Gateway → Validation → Provider → Audit → Client

01 — Overview

This case study covers the foundational AI infrastructure I've built to support multiple products. It focuses on the reality that calling an LLM API is easy, but building a reliable application boundary around probabilistic systems is extremely hard.

02 — Problem

When AI features move from prototype to production, you immediately hit issues: rate limits, provider outages, parsing errors from malformed JSON, un-auditable token costs, and security boundaries. You cannot ship a raw OpenAI or Anthropic API call directly inside a mission-critical feature.

03 — Why it was technically difficult

The challenge is building a deterministic system wrapped around a non-deterministic core. You need to handle streaming responses, enforce structured data schemas, route traffic dynamically based on model health, and track provenance—all with minimal latency overhead.

04 — Architecture

The core of the infrastructure is an AI Gateway (often utilizing tools like LiteLLM) that sits between the application logic and the model providers.

05 — Key engineering decisions

• Provider Abstraction: Code never references a specific provider API. It asks the gateway for an inference task, allowing seamless swapping between OpenAI, Anthropic, or local models.
• Dynamic Fallbacks: Configured automatic routing so that if a primary model times out or a provider goes down, the request instantly falls back to a secondary model.
• Structured Output Enforcement: Implemented strict validation layers (e.g., Pydantic). If an LLM returns invalid JSON, the gateway automatically retries with the error injected before failing back to the application.
• Unified Observability: Every request logs token usage, latency, cost, and the exact prompt/response to an audit ledger, essential for debugging hallucinations.

06 — AI architecture

App Request → Gateway → Cost/Auth Check → Primary Provider → (If Fail) → Fallback Provider → Schema Validation → Audit Log → App Response

07 — Product decisions

• Model Agnosticism: This infrastructure ensures the product team is never locked into a single vendor, allowing the business to negotiate or swap based on pricing.

08 — Outcome

Resulted in near 100% uptime for AI features despite multiple upstream provider outages, and reduced token costs by routing simpler tasks to cheaper models automatically.

09 — What I learned

I learned that production AI engineering is mostly just traditional systems engineering. The key to successful AI products is assuming the model will fail, hallucinate, or format things wrong, and building resilient guardrails to catch it.