How to Reduce API Inference Costs by Routing to Open Models

Many applications default to high-end, proprietary models for every task, leading to ballooning API bills. However, simple operations like classification, summarization, or entity extraction rarely require top-tier performance and can often be handled effectively by smaller, lower-cost, or open-source models.

This guide demonstrates how to implement intelligent model routing. By directing simpler tasks to a cost-effective endpoint while reserving high-performance models for complex reasoning, you can significantly slash your monthly inference costs without sacrificing the quality of your core user experience.

Video guide

Prerequisites

• An active subscription or API key for an LLM provider or proxy service.
• Python 3.10+ installed on your local development environment.
• The standard OpenAI Python SDK installed ('pip install openai').
• A basic understanding of your application's token usage patterns.

Steps

1. 1

   Analyze and Categorize Your Tasks

   Audit your existing API calls to distinguish between tasks that require complex reasoning versus those that are routine. Tag your requests into tiers, such as 'Light' for simple classification and 'Complex' for architectural or multi-step logic.

2. 2

   Configure the Client for Model Routing

   Instantiate your API client using the standard OpenAI SDK but pointing to your routing gateway. By setting a base_url, you can intercept requests and dynamically decide which model to invoke based on the task tag.

3. 3

   Implement the Router Logic

   Create a function that inspects the request parameters before dispatching. If the task is flagged as 'Light', route it to a smaller, open-source model; otherwise, proceed with your standard high-performance model.

4. 4

   Execute and Monitor Requests

   Use the 'smart-select' model endpoint provided by your gateway to handle the routing transparently. Ensure your code monitors the response metadata to verify that the correct model is being selected for the specific input type.

5. 5

   Fine-Tune Based on Latency and Quality

   Review the logs to ensure that your smaller models are maintaining the required quality threshold for your use case. If you notice a drop in performance, adjust your routing logic to promote those specific task types back to your primary model.

Code

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.select.ax/v1",
    api_key=os.environ.get("API_KEY")
)

def get_optimized_response(prompt, task_type):
    # Routing logic: use smart-select to automatically pick the cost-effective model
    model_name = "smart-select" if task_type == "light" else "claude-3-sonnet"
    
    response = client.chat.completions.create(
        model=model_name,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Example usage
result = get_optimized_response("Classify this sentiment.", "light")
print(result)

Pro tips

Visual guide

Route your models intelligently

Use one API key for routing, fallback, and cost control across model providers.