Module 03: LLM Gateways
Every mature AI team eventually hits the same wall. They start with one LLM provider, one API key, and one integration. Then they add a second model for a different use case. Then a third. Then someone needs fallbacks when the primary provider goes down. Then finance asks where the $40k/month is going. Then a user hits rate limits and the whole feature stops working.
The answer to all of these problems is the same: a gateway.
An LLM gateway is the infrastructure layer that sits between your application code and every LLM provider you use. It gives you a single place to handle routing, fallbacks, caching, rate limiting, cost tracking, and observability - without rewriting application code every time you add a new model.
This module teaches you how to build, configure, and operate that layer.
What You Will Learn
Lessons in This Module
| # | Lesson | What You Learn |
|---|---|---|
| 01 | Why an LLM Gateway | The case for centralized routing and the 12k story |
| 02 | LiteLLM | Deploy a universal proxy; route 100+ providers through one endpoint |
| 03 | Portkey | Production gateway with tracing, virtual keys, and guardrails |
| 04 | Semantic Caching | Return cached responses for similar queries; cut costs 40–60% |
| 05 | Model Fallback and Retry | Build resilient LLM clients that survive provider failures |
| 06 | Load Balancing Across Providers | Distribute traffic by latency, cost, and health |
| 07 | Cost Management and Budget Alerts | Per-user spend tracking and Slack alerts before budgets blow |
| 08 | Rate Limiting and Quotas | Token buckets and sliding windows to prevent abuse |
Key Concepts
Unified endpoint - all LLM calls go through one URL; providers are swapped in config, not code.
Model routing - send different request types to different models based on cost, capability, or latency requirements.
Semantic cache - embed incoming queries and return cached responses when cosine similarity exceeds a threshold. The fastest LLM call is one you never make.
Fallback chain - if Claude fails, try GPT-4o; if that fails, try GPT-4o-mini. Configured once at the gateway, invisible to application code.
Circuit breaker - stop hammering a failing provider; open the circuit, let health checks restore it, close when healthy.
Token budget - enforce per-user, per-team, or per-feature spending limits with hard caps and soft alerts.
Prerequisites
- Familiarity with REST APIs and async Python
- Basic understanding of LLM providers (Anthropic, OpenAI, Google)
- Module 01 (LLMOps) and Module 02 (AI Observability) recommended
:::tip Real-world impact A gateway is not a nice-to-have. It is the difference between an AI platform that scales and one that becomes unmanageable the moment a second team starts shipping AI features. Build the gateway early. :::