AI Infrastructure: Gateways, Caching & Shipping LLMs in Production

The problem nobody demos

Demos call one model directly. Production serves thousands of users across models that go down, change price, rate-limit you, and leak latency into every click. AI infrastructure is the layer that absorbs all of that: gateways, caches, fallbacks, and guardrails between your app and the model.

The ideas that matter most

• AI gateway — one door for all model traffic: auth, logging, retries, and routing in one place instead of scattered SDK calls.
• LLM routing — sending easy queries to cheap models and hard ones to frontier models. The single biggest cost lever.
• Model fallback — when your primary model errors or times out, traffic shifts automatically. Outages become invisible.
• Prompt caching — reusing identical prompt prefixes (system prompts, long docs) instead of re-paying for them every call.
• Semantic caching — "what's the refund policy?" and "how do refunds work?" are the same question — answer once, serve twice.
• Rate limiting — protecting your budget and your provider quota from runaway loops (agents!) and abuse.
• Observability — tokens, latency, and quality per request. You can't optimize what you can't see.
• Red teaming — attacking your own system before strangers do: jailbreaks, injections, and data exfiltration probes.

A sane learning order

1. Gate everything (ai-gateway → llm-rate-limiting)
2. Spend less (llm-routing → prompt-caching → semantic-caching)
3. Survive outages (model-fallback)
4. See everything (ai-observability)
5. Attack yourself (ai-red-teaming)

Explore each layer in our AI Infrastructure course.

Mistakes beginners make

• Calling models directly from app code. No logging, no fallback, no cost control — and a provider outage becomes your outage.
• One model for everything. Summarization doesn't need your smartest model. Routing easy work down routinely cuts bills 50–80%.
• Caching nothing. Repeated system prompts and frequent questions are pure margin walking out the door.
• No per-request tracing. When quality dips, you need tokens, latency, model version, and prompt per request — yesterday's averages won't debug today's incident.

FAQ

What is an AI gateway? A proxy layer in front of model providers handling auth, routing, retries, fallbacks, caching, logging, and spend caps — so app code makes one clean call.

Prompt caching vs semantic caching? Prompt caching reuses identical text (same system prompt, same document) at the provider level. Semantic caching reuses similar meanings ("refund policy?" ≈ "how do refunds work?") at your layer. Use both.

How do I cut LLM costs without hurting quality? Route by difficulty, cache aggressively, compress context (see our RAG compression guide), and pick the smallest model that passes your evals per task.

How do I survive a provider outage? Fallback chains across providers with automatic failover, tested regularly — plus cached answers for your most frequent queries so degradation is graceful, not total.