Inference cost optimization
Inference is the cost of running an AI model every time it processes a request — every question your agent answers, every document it analyzes. These costs add up fast at scale. Optimization means using the right size model for each task: a small, fast model for routine ticket classification, a powerful model for complex analysis. Smart routing between models can cut your AI operating costs by 50-80% without sacrificing quality where it matters.
Go deeper
Your HVAC company runs an AI dispatch assistant that reads every incoming service request, classifies urgency, matches technician skills, and suggests scheduling. That's four inference calls per ticket. At 200 tickets a day, you're making 800 AI calls daily — and your vendor is charging you for every one at the same rate, whether it's a simple 'is this urgent?' yes/no or a complex multi-factor scheduling optimization.
The trap most companies fall into is using one model for everything. It's like sending a senior engineer to change an air filter. Route the simple classification to a small, cheap model and save the expensive model for the judgment calls. Ask your AI vendor if they support tiered model routing — if they only offer one model size, you're probably overpaying for 70% of your workload.
Questions to ask
- What percentage of our AI calls are routine classification versus complex reasoning?
- Does our vendor offer model tiering or do we pay the same rate for every request?
- What's our per-transaction AI cost today, and what would it be with right-sized models?