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Marc Newstead
Marc Newstead

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Stop Using One LLM for Everything: A Dev's Guide to Model Routing

#ai #llm #architecture #devops

The Problem With Your Current LLM Stack

If you're sending every prompt through GPT-4 or Claude Opus because "it's the best model", you're probably burning money on overkill. Classifying a support ticket's sentiment doesn't need the same horsepower as generating a product requirements document. Yet most codebases I see treat LLM calls like they're all created equal.

Model routing solves this. Instead of one model for everything, you dynamically select which model handles each task based on complexity, cost, and latency requirements. Think of it as load balancing, but for intelligence.

What Model Routing Actually Looks Like

At its core, a router is middleware between your app and your LLM providers. Here's the mental model:

def route_llm_request(task):
    complexity = analyse_task(task)

    if complexity == "simple":
        return call_model("gpt-3.5-turbo", task)
    elif complexity == "moderate":
        return call_model("claude-haiku", task)
    else:
        return call_model("gpt-4", task)
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Obviously production implementations get more sophisticated, but the principle holds: inspect the task, pick the cheapest model that can handle it reliably.

Mapping Tasks to Models

The hard part isn't the routing logic—it's building a sensible taxonomy of your tasks. Start by auditing what you're actually sending to LLMs:

  • Classification tasks: Intent detection, sentiment analysis, category assignment. These are often binary or multi-class decisions. GPT-3.5-turbo or even GPT-4o-mini handles these beautifully at a fraction of the cost.

  • Retrieval-augmented generation: Answering questions from your docs. Moderate complexity. Models like Claude Haiku or Gemini Flash offer solid performance without flagship pricing.

  • Content generation: Drafting emails, writing code, creating marketing copy. This is where you might actually need GPT-4 or Claude Opus—but only when the stakes justify it.

  • Structured extraction: Pulling entities from text, parsing invoices. If you can define a JSON schema, smaller models work fine, especially with function calling.

The key insight: most applications have a long tail of simple tasks subsidising a small number of complex ones. Route accordingly.

Tracking the Wins

You need telemetry. Log every routing decision with:

{
  taskId: uuid(),
  taskType: "classification",
  modelSelected: "gpt-3.5-turbo",
  tokens: 150,
  cost: 0.0003,
  latency: 420,
  timestamp: Date.now()
}
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After a week, aggregate this. You'll likely find:

  • 70%+ of requests are simple and could use cheaper models
  • Your highest costs come from 5-10% of requests
  • Latency improves because smaller models are faster

One team I worked with cut their monthly LLM bill by 60% just by routing classification and extraction tasks away from GPT-4. The business logic didn't change—just the infrastructure underneath.

Fallback Strategies and Provider Diversity

Routing also gives you resilience. If OpenAI's API goes down (and it will), your router can failover to Anthropic or Gemini. This requires:

  1. Normalised interfaces: Abstract provider-specific SDKs behind a common interface
  2. Retry logic: Catch rate limits and failures, try the next model in your tier
  3. Circuit breakers: Temporarily skip a provider if it's consistently failing 
def call_with_fallback(task, models_list):
    for model in models_list:
        try:
            return call_model(model, task)
        except ProviderError:
            continue
    raise AllProvidersFailed()
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This multi-provider approach also dodges vendor lock-in. When you're not married to a single API, you can negotiate better pricing and adopt new models faster.

Getting Started

You don't need to build a Netflix-scale routing system on day one. Start simple:

  1. Categorise your prompts. Spend an afternoon tagging a sample of requests by complexity.
  2. Benchmark models on each category. Test accuracy, cost, and latency.
  3. Implement a basic router. Even a hardcoded if/else saves money immediately.
  4. Instrument everything. You can't optimise what you don't measure.
  5. Iterate. Add more sophisticated routing rules as your usage patterns emerge.

For a deeper dive into the strategic thinking behind this approach, the team at AI automation and software development have a solid write-up on deploying LLMs at scale that's worth reading.

The Bottom Line

Using one model for everything is like running every database query against your production master. Sure, it works—but it's wasteful and fragile. Model routing gives you cost control, performance headroom, and architectural flexibility.

Start small, measure everything, and let the data guide your routing decisions. Your infrastructure budget will thank you.

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