𝗟𝗟𝗠 𝗚𝗮𝘁𝗲𝘄𝗮𝘆𝘀: 𝗥𝗼𝘂𝘁𝗶𝗻𝗴, 𝗙𝗮𝗹𝗹𝗯𝗮𝗰𝗸𝘀, 𝗔𝗻𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗖𝗮𝗰𝗵𝗶𝗻𝗴

One line of code can ruin your AI budget.

If you hardcode a single model provider in your app, you face three risks:

  • High costs for simple tasks.
  • Total outages when a provider goes down.
  • Paying for the same answer thousands of times.

An LLM gateway acts as a proxy between your app and your models. It handles three critical jobs: routing, fallbacks, and caching.

  1. Routing Most apps send every request to the most expensive model. This is wasteful. Use routing to send easy tasks to cheap models.
  • Static routing: Use rules based on user tiers or task types.
  • Cost/Latency routing: Pick the fastest or cheapest available model.
  • Difficulty routing: Use a small model to decide if a task needs a large model. Research shows smart routing can maintain high quality while cutting costs by over 80%.
  1. Fallbacks Providers fail. They hit rate limits or go offline. A gateway manages a fallback chain. If your primary model fails, the gateway automatically tries the next one in your list. To avoid making outages worse, use these patterns:
  • Exponential backoff: Space out retries to avoid overwhelming a struggling provider.
  • Circuit breaking: Stop sending traffic to a failing provider for a set period. This allows for instant failover instead of waiting for timeouts.
  1. Semantic Caching Standard caching looks for exact text matches. This fails for LLMs because users phrase questions differently. Semantic caching looks at meaning. It converts a prompt into a vector and checks if a similar question exists in your database.
  • The benefit: A cache hit takes 5ms and costs $0. A model call takes seconds and costs tokens.
  • The danger: Setting your similarity threshold too low causes wrong answers. If the threshold is too loose, a question about "resetting a password" might return an answer about "changing an email."

Build or Buy?

  • Build: Best for simple needs like basic fallbacks or exact-match caching.
  • Buy/Open Source: Use tools like LiteLLM or managed services when you need semantic caching, observability, and complex failover logic.

A gateway is infrastructure, not a feature. Stop scattering model calls throughout your codebase. Put a gate in front to control your costs and reliability.

Source: https://dev.to/nazar_boyko/llm-gateways-routing-fallbacks-and-semantic-caching-1n2b

Optional learning community: https://t.me/GyaanSetuAi