A business that builds its AI strategy around a single model is building on the least stable part of the entire stack. Model prices fall sharply from one year to the next. The connectors, the permission engine and your organisation's context do not.
The price spread is not small
Published per-token prices for a provider's smallest model and its frontier model often differ by more than ten times, on that provider's own price list. Routing the requests a cheaper model handles well to that model, and keeping the premium one for the work that needs it, is where the saving comes from. In the RouteLLM research, that approach cut cost by 35% to over 85% depending on the workload, while keeping 95% of the stronger model's quality.
Why lock-in is the real cost
A business built directly against one provider's API inherits that provider's pricing decisions, outages and model deprecations as its own roadmap risk. In Andreessen Horowitz's 2025 survey of 100 enterprise CIOs, 37% were using five or more models, up from 29% the year before. Their main reason was that different models suit different jobs, with avoiding lock-in a factor too. Either way, model selection is becoming dynamic, not a single point of failure.
What is actually durable
The connectors into your systems. The permission engine that resolves who can see what. The accumulated context about how your business actually works. None of that depreciates when a new model ships, and none of it has to be rebuilt when a cheaper or better one does. That is the part worth investing in as a foundation, and the model is not it.