"The first time your LLM bill crosses $10,000 in a month, you start paying attention. When it hits $50,000, you start making spreadsheets. Somewhere around $100,000, you realize that you might need a fundamentally different approach. Most teams reach this moment the same way. Something like this: they launched with a single model (usually the best one they could afford) and scaled it across every use case in their product. Customer support chatbot? GPT-4. Email summarization? GPT-4. Spam detection? Also GPT-4. The reasoning was simple: one integration, consistent quality, predictable behavior. Ship fast, optimize later."
"Routing is decision logic that directs incoming requests to appropriate models based on the request's characteristics. The sophistication isn't in the routing mechanism; it's in understanding your work well enough to categorize it meaningfully. Think of it like a hospital. When you walk into an emergency room, someone does triage. Chest pain? You see a cardiologist immediately. Sprained ankle? You wait for a general practitioner. Paperwork question? The administrative staff handles it. Nobody sends every patient to the most specialized, expensive surgeon. That would be wasteful and slow. The system directs patients to the most suitable level of care."
LLM costs escalate when teams use a single, high-end model across all use cases. Relying on one model creates rising bills, erratic latency, outage vulnerability, and poor unit economics. Routing solves this by directing requests to models matched to task complexity and characteristics. Effective routing depends on categorizing work accurately, not on complex routing systems. A triage-like approach sends simple queries to fast, inexpensive models and reserves expensive models for tasks requiring deep reasoning. Proper routing lowers costs, improves latency stability, and reduces the blast radius of provider outages while maintaining suitable quality for each task.
Read at LogRocket Blog
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