top of page

What is LLM Routing? The Hidden Engine Behind Cheaper, Smarter AI

  • Writer: Rohnit Roy
    Rohnit Roy
  • Aug 13
  • 4 min read

In the world of Artificial Intelligence, Large Language Models (LLMs) are the stars. From GPT-4 to Claude, these models are powering everything from chatbots to code assistants to business intelligence agents. But here’s a secret the hype glosses over: not every question needs the most powerful model — and sending everything to your “biggest brain” is the fastest way to blow your AI budget.


What is LLM Routing? The Hidden Engine Behind Cheaper, Smarter AI

That’s where LLM Routing steps in. It’s not glamorous. It’s not headline-grabbing. But it’s quietly becoming the control tower of AI efficiency.


What Is LLM Routing?


Imagine you run a helpdesk with a mix of junior and senior support staff.


  • A simple password reset? Junior rep handles it.

  • A complex legal query? Straight to the senior expert.


How LLM Routing works?
LLM Routing workflow (Source)

LLM Routing works the same way — but instead of people, it manages different AI models.


It:

  1. Analyzes your query — figuring out the content, intent, and complexity.

  2. Selects the right model — based on capabilities, past performance, cost, and availability.

  3. Routes the request — to a single model or multiple models.

  4. Aggregates responses — if multiple models are used.

  5. Learns and improves — by tracking results over time.


The result? Faster answers, lower costs, and more consistent performance.


Why Is Routing Needed Now More Than Ever?


LLMs are not created equal.

  • Top-tier models (e.g., GPT-4, Claude Opus) give the best quality but are expensive and slower.

  • Smaller models (e.g., GPT-3.5, open-source LLaMA variants) are cheaper and faster but can miss nuance.


If you send every single request to your most capable (and most expensive) model, you’re overpaying for simple tasks like:

  • Converting CSV data to a table

  • Answering factual lookups

  • Generating simple summaries


Routing ensures the right model handles the right task — without you needing to micromanage the process.


RouteLLM: A Case Study in Affordable, Smart Routing


While there are multiple routing frameworks, RouteLLM stands out because it’s practical, affordable, and backed by solid performance data.


How It Works


RouteLLM trains its routing decisions using datasets like Chatbot Arena, where different models are compared on the same prompts. By learning which models perform better on which types of queries, it builds a “playbook” for future routing.


Key routing techniques include:

  • Similarity-Weighted (SW) Ranking – Finds which past query is most like the current one, then chooses the model that performed best.

  • Matrix Factorization – Learns a scoring function predicting a model’s success.

  • BERT Classifier – Uses embeddings to classify which model will likely give the best response.

  • Causal LLM Classifier – Similar purpose, but with a generative modeling approach.


Router performance on MT Bench (left) trained only on Arena data (right) trained on Arena data augmented using a LLM judge
Router performance on MT Bench (left) trained only on Arena data (right) trained on Arena data augmented using a LLM judge. (Source)

The Numbers That Matter

  • Without data augmentation:

    • Matrix factorization achieved 95% of GPT-4’s performance using GPT-4 only 26% of the time.

    • That’s 48% cheaper than sending everything to GPT-4.

  • With data augmentation:

    • Matrix factorization still led, but now needed GPT-4 only 14% of the time.

    • 75% cost reduction with similar performance quality.


RouteLLM is a framework for serving and evaluating LLM routers.
RouteLLM is a framework for serving and evaluating LLM routers.

For companies running thousands (or millions) of queries, those savings aren’t just nice — they’re survival-critical.


Challenges in LLM Routing


Before you think routing is a plug-and-play magic wand, here are the real hurdles:

  1. Inferring Query ComplexityMisjudge complexity, and you either waste resources or get a bad answer.

    • Example: “Capital of France?” → cheap model is fine.

    • “Draft a compliance policy for EU data transfers” → you’ll regret using the cheap one.

  2. Latency Trade-offsRouting adds decision-making overhead. Some models are fast, others slow — routing needs to balance quality without making users wait too long.

  3. Balancing Cost and QualityThe Holy Grail. Too much cost-cutting, and quality drops. Too much quality obsession, and your CFO starts calling.


How Routers Are Evaluated


Routing quality isn’t just “gut feel.” Benchmarks help keep routers honest:

  • GSM8K – Tests math reasoning.

  • MTBench – Measures multi-task performance.

  • MBPP – Evaluates code generation.


And now, ROUTERBENCH has emerged — a 405k-inference dataset designed to systematically test routing systems across diverse tasks.


Why This Matters for AI Builders and Businesses


If you’re:

  • Building AI agents that respond to mixed-complexity queries

  • Running internal AI assistants for teams

  • Offering customer-facing chatbots at scale

…then routing is the lever that lets you scale without runaway costs.


At AdoSolve, we use routing strategies like these to help clients build AI agents, optimize LLM usage, and manage costs intelligently. It’s part of the invisible infrastructure that makes AI not just powerful, but sustainable.


Final Thought


LLM Routing is not about replacing powerful models — it’s about using them wisely. The smartest AI companies in 2025 won’t be the ones who always use the biggest model.


They’ll be the ones who know when not to.

In a world obsessed with model size and capability, routing reminds us:

Intelligence is not just about knowing the answer.It’s about knowing who should answer.

Comments

bottom of page