Your LLM Bill Shouldn't Scale With Your Users

Your invoice from OpenAI, Google, or any other LLM provider shouldn't grow at the same rate as your user base or data volume. This is the first thing I tell everyone who consults with me about their AI infrastructure.

Yes, it's exciting to see the performance of the latest models like Claude Opus 4.5 or the new Gemini Pro. They're remarkable, cutting-edge, and they solve almost everything. But at the end of the month, the bill arrives — and it hurts.

The Temptation of Always Using the Best

It's the most natural thing in the world to want to use the most powerful models for every task in your pipeline. They're amazing, advanced, and they solve almost everything. But when you're working at scale, the real challenge isn't just intelligence — it's unit economics.

The Winning Architecture: A Tiered Approach

A smart, winning architecture today works completely differently. Here's a simplified example that can be easily refined for your specific needs:

Layer 1: Basic Filtering

Start with basic filtering using simple tools like Regex patterns or small, local models for initial noise cleanup. This layer catches obvious cases, spam, malformed inputs, and requests that don't need AI at all. Cost: essentially zero.

Layer 2: Fast & Lean Models

From there, move to a lean and fast model like Llama 4 or similar open-source alternatives. These models excel at making routing decisions and closing out a huge portion of simple tasks in a fraction of a second. They handle classification, simple Q&A, and structured data extraction brilliantly — at a fraction of the cost of frontier models.

Layer 3: Premium Models for Complex Tasks

Only the truly complex tasks reach the large, expensive models at the end of the pipeline. This is where you deploy Claude Opus, GPT-4, or Gemini Pro — for nuanced reasoning, complex analysis, or creative tasks that genuinely require frontier capabilities.

The Best of Both Worlds

This tiered combination is the only way to enjoy the best of both worlds: the accuracy and capability of SOTA (State of the Art) models and reasonable costs that allow your product to be profitable.

• The majority of requests can typically be handled by Layer 1 and 2
• Significant cost reduction compared to routing everything to premium models
• Faster response times for simple queries
• Better user experience through appropriate resource allocation

Signs Your Pipeline Needs Optimization

If your pipeline feels too expensive relative to the value it creates, it's probably time to bring some order to it. Ask yourself:

• Are you sending every request to the same model regardless of complexity?
• Is your AI cost per user increasing linearly with user growth?
• Do simple queries take the same time as complex ones?
• Are you paying premium prices for tasks that don't require premium intelligence?

The INUXO Approach

At INUXO, this is exactly what we specialize in. We build AI architectures that pay for intelligence and performance only where it's truly needed. We help companies audit their current AI spending, identify optimization opportunities, and implement tiered routing strategies that can dramatically reduce costs while maintaining or even improving output quality.