# Could Companies Be Overpaying for AI by 20–40%? The Role of Model Assignment Author: Pal Sinha, Barnali Author URL: https://financedigest.com/author/pal-sinha-barnali Published: 2026-09-09 Category: TECHNOLOGY Category URL: https://financedigest.com/category/technology URL: https://financedigest.com/could-companies-be-overpaying-for-ai-by-20-40percent-the-role-of-model-assignment ![MixRoute Finance Digest](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/mixroute-finance-digest-1788962487009-compressed.jpg) Ask a finance team how it plans to reduce AI AI costs and you will usually hear two answers: negotiate with the provider or wait for prices to fall. Both can help. Neither addresses the quieter source of overspend sending every request to the same model, even when the work does not need it. That distinction matters because an AI workload is rarely uniform. A support bot may handle thousands of simple returns and opening-hours questions, then occasionally face a complex policy explanation or a difficult troubleshooting case. Charging the same frontier-model rate for all of those requests is like using a helicopter for every delivery because some deliveries are urgent. The practical question is not whether a smaller model is always better. It is whether the application has a way to send simple work to a lighter model and reserve the expensive model for the requests that actually need it. ### The 20-40% figure needs a proper label MixRoute's [Smart Routing explanation](https://mixroute.ai/smart-routing/) reports average savings of 20-40% across 10,000+ real-world runs. That is useful product evidence, not a universal industry benchmark. The result depends on the model pool, the mix of simple and difficult requests, token volume, and the quality threshold a team is willing to accept. ### What model routing actually changes A routing layer classifies the incoming request and selects a model from a set the team has already approved. Simple work can go to a lighter model; complex work can escalate. The application can keep the same request format while the model assignment changes behind the API key. That boundary is important. A router should not be a black box that quietly changes the product's quality standard. The safer setup is a controlled model pool, a direct key for endpoints that must always use one model, and a dashboard that lets finance compare routed traffic with the unrouted baseline. For a company spending $20,000 a month on model calls, a 20-40% saving would imply $48,000-$96,000 a year before any routing fee. That is arithmetic based on an assumed range, not a promise of what every workload will save. A uniform workload with mostly difficult requests will land below it; a workload dominated by short, repetitive questions may do better. ### The evidence a finance team should ask for A meaningful assessment of potential savings requires more than a headline percentage. Ask for the baseline definition, the traffic window, the model pool, the treatment of retries and failed requests, and the way quality was checked. Then run the same questions against your own traffic. The most useful first test is a real slice of production-shaped traffic: record the current cost, latency, success rate, and model mix; route only through models your team has approved; then compare those numbers with the unrouted control. The goal is not to prove a vendor right. It is to find out whether the mechanism fits your workload. ### What it costs, and where the trade-off appears Routing adds a classification step, so it can add latency. [MixRoute's](https://mixroute.ai/) own documentation recommends using a regular key for latency-critical endpoints and routing only the traffic where the cost trade-off is acceptable. That is the right way to frame it: savings are useful only when the resulting response time and quality still fit the product. The fee also needs to be stated precisely. MixRoute's [pricing page](https://mixroute.ai/pricing/) says Smart Routing is free during its launch window and then carries a 5% service fee on requests that actually go through the router. Regular keys are not charged that routing fee. The relevant comparison is therefore not 'free versus paid'; it is the total cost, including the fee, against the cost and performance of the current model assignment. ### Routing is one lever, not the whole cost plan Prompt length, output limits, repeated context, and caching can all change token spend. They work alongside model assignment rather than replacing it. Provider pricing also separates input and output usage, so the finance owner should track both instead of treating every token as the same unit. The current [OpenAI API pricing](https://openai.com/api/pricing/) and Anthropic pricing pages are useful references for that distinction, but the application's own usage export remains the decision-grade evidence. For finance teams turning AI from an experiment into a managed cost, the first move is simple: measure the traffic mix, identify how much work is genuinely hard, and make the model choice visible. The saving may be 20%, 40%, or neither. What matters is replacing an eighteen-month-old default with a number your own team can audit. --- This blog is powered by Superblog. Visit https://superblog.ai to know more. ---