AI Workspace Loading

We’re preparing your intelligent learning experience. Our AI systems are processing content, optimizing resources, and setting everything up for you.

Preparing Learning Paths...
AI Processing
Smart Automation
Learning Engine
Good things take a moment.

LearnLess.ai

LEARN LESS. UNDERSTAND MORE.
Advanced45–60 min

LLM Gateway

Apply the gateway lessons to a working routing, retry, fallback, and cost-tracking layer between applications and model providers.

What You Will Build

An illustrative gateway that sits between application code and model providers, centralizing authentication, routing, retries, fallback, rate limiting, and cost tracking — so application code just makes a request and lets the gateway handle the rest. This project applies the LLM Gateway, AI Gateway Architecture, Rate Limiting, Cost Optimization, and Observability lessons.

Learning Objectives

  • Implement provider abstraction behind a single interface

  • Implement model routing based on task type and cost

  • Implement retry and fallback for a failed provider call

  • Add basic rate limiting and cost tracking

Prerequisites

Concepts Used

LLM Gateway
AI Gateway Architecture
Rate Limiting
Cost Optimization
Observability

Architecture

Application

Makes One Request

Lets the gateway handle the rest.

sends request to

LLM Gateway (Auth, Routing, Rate Limit, Retry, Fallback, Logging)

Centralizes Everything

One consistent interface behind every provider.

routes to

Provider A / Provider B

Interchangeable

Routed to and swapped without changing application code.

LLM Gateway

Step 1 — Abstract the Providers

What are we doing? Presenting one consistent interface to application code, regardless of which provider actually handles a request. Why? Without this, provider-specific logic ends up duplicated across every calling application.

provider_abstraction.py (illustrative pseudocode)
class Provider:
    def call(self, request): ...

class ProviderA(Provider):
    def call(self, request):
        return provider_a_sdk.generate(request.to_provider_a_format())

class ProviderB(Provider):
    def call(self, request):
        return provider_b_sdk.complete(request.to_provider_b_format())

Step 2 — Route by Task

What are we doing? Choosing which model handles a request, based on task type rather than a hardcoded choice in application code. Why? Simple tasks can use a cheaper, faster model; complex reasoning may need a stronger one — this is a cost and latency lever, not just a technical choice.

routing.py (illustrative pseudocode)
def select_provider(task_type):
    if task_type == "classification":
        return ProviderA()  # fast, cheap
    if task_type == "reasoning":
        return ProviderB()  # stronger, slower
    return ProviderA()

Step 3 — Add Retry and Fallback

What are we doing? Retrying a failed call, and falling back to an alternate provider if the primary one is unavailable. Why? Without a fallback, a single provider outage becomes a full outage for every application behind the gateway.

retry_fallback.py (illustrative pseudocode)
def handle_request(request):
    provider = select_provider(request.task_type)
    try:
        return call_with_retry(provider, request, max_retries=2, timeout=request.timeout)
    except ProviderUnavailable:
        fallback = get_fallback(provider)
        return call_with_retry(fallback, request, max_retries=1, timeout=request.timeout)
    except AllProvidersFailed:
        return degraded_response(request)

Step 4 — Add Rate Limiting and Cost Tracking

What are we doing? Bounding how much any one caller can use, and recording enough to understand spend. Why? Without limits, one misbehaving caller can exhaust shared provider quota; without tracking, cost problems are only discovered when the bill arrives.

Request

Incoming Call

From an application behind the gateway.

checked by

Rate Limiter

Bounds Usage

Stops one caller from exhausting shared quota.

allowed through to

Provider Call

Actual Request

Sent to the routed provider.

recorded as

Log Tokens + Cost + Latency

Spend Visibility

Caught early instead of at the bill.

Step 5 — Add Observability

What are we doing? Logging enough per request to debug and track cost, without capturing unnecessary sensitive content. Why? The gateway sees every request — it's the natural place to centralize this visibility, but also a common place for sensitive data to leak if logged indiscriminately.

  • No fallback when the primary provider fails

    A single provider outage becomes a full outage for every application behind the gateway.

  • No per-caller rate limiting

    One misbehaving caller can exhaust shared provider quota for everyone.

  • Logging full prompts and responses without redaction

    Gateway logs are a common place for sensitive content to leak.

  • Hardcoding model choice in application code

    This defeats the purpose of routing — the decision should live in the gateway.

  • Making the gateway itself a single point of failure

    It needs its own reliability story, since every request now depends on it.

Challenges

Extend the project yourself. No automated grading — use these to practice reasoning about the architecture.

Challenge 1: Add a third provider

Add a second fallback tier so the gateway tries a third provider if both primary and first fallback fail.

Challenge 2: Add per-tenant rate limits

Extend rate limiting so each tenant has its own independent quota.

Challenge 3: Add cost budgets

Add a per-tenant monthly cost budget that routes to a cheaper model once exceeded.

Design Review

Before moving on, think through these questions the way a reviewer would.

  • What happens to every application behind this gateway if the gateway itself goes down?

  • How would you decide when a request should retry versus immediately fall back?

  • Where would you add caching to reduce load on the providers?

  • How would you detect a sudden cost spike before the monthly bill arrives?

Interview Questions

How would you design an LLM Gateway, and when would you actually introduce one?

I'd introduce a gateway once multiple applications or models create real duplication in authentication, retries, and cost tracking. It should abstract provider differences behind one interface, route by task type, retry and fall back to an alternate provider on failure, apply rate limits, and log enough for cost tracking and debugging without capturing sensitive content indiscriminately. For a single small app calling one model, I'd skip it — it's an extra hop and operational component that isn't earning its cost yet.

  • Introduce once duplication is real
  • Provider abstraction, routing, retry/fallback
  • Careful, non-indiscriminate logging

How would you handle a model provider outage?

I'd have the gateway detect the failure — timeout or explicit error — and automatically route to a configured fallback provider or model, logging which path actually served the request. If all providers fail, I'd return a degraded response rather than letting the failure propagate as a hard error to every calling application.

  • Automatic fallback
  • Graceful degradation as last resort
  • Visibility into which path served the request

Why is model routing also a cost lever, not just a performance one?

Because routing simple, low-stakes requests to a smaller, cheaper model directly reduces spend, while reserving a stronger, more expensive model for tasks that actually need it. It's one of the biggest cost levers available, since it changes cost per request rather than just optimizing infrastructure.

  • Model choice drives cost directly
  • Bigger lever than infrastructure tuning

Explain It in 30 Seconds

This project applies the gateway lessons to a working layer between applications and model providers: provider abstraction behind one interface, routing by task type, retry and fallback on failure, rate limiting, and cost/latency logging. It's worth the added hop and operational overhead once multiple applications or models create real duplication — and it needs its own reliability story, since every request now depends on it.

On this page