Faced with rising costs from proprietary labs, sophisticated enterprise clients are building internal evaluation and routing systems. This allows them to use cheaper, open-source models for less complex tasks, optimizing for both cost and performance.

Enterprises are currently overspending on tokens by sending all queries to the most powerful LLMs. A new software category will emerge to intelligently route requests to smaller, cheaper models when possible, creating a critical efficiency and cost-saving layer between companies and foundational model providers.

Don't use your most powerful and expensive AI model for every task. A crucial skill is model triage: using cheaper models for simple, routine tasks like monitoring and scheduling, while saving premium models for complex reasoning, judgment, and creative work.

Contrary to the belief that enterprises have unlimited budgets, they are focused on the ROI of their AI spend. As agentic workflows cause token bills to skyrocket, orchestration tools that intelligently route queries to the most cost-effective model for a given task are becoming essential infrastructure.

Advanced AI architectures will use small, fast, and cheap local models to act as intelligent routers. These models will first analyze a complex request, formulate a plan, and then delegate different sub-tasks to a fleet of more powerful or specialized models, optimizing for cost and performance.

The most sophisticated AI users aren't locking into one provider. Faced with a 13x annual increase in token costs, they leverage multiple models and routing platforms like OpenRouter to optimize for price and performance. This behavior suggests a future of model commoditization, not monopoly.

In response to budget blowouts from agentic AI, enterprises are moving beyond simple adoption to active cost management. A new "token efficiency" stack is emerging, featuring tactics like model routing to cheaper alternatives (e.g., DeepSeek) and custom post-trained models to reduce reliance on expensive foundation models.

Companies are building intelligent systems that analyze a user's prompt and automatically route it to the most cost-effective model that can handle the task. This avoids using expensive frontier models for simple requests, with some companies like Coinbase successfully keeping costs flat despite exponential usage growth.

State-of-the-art models like Claude Opus are often overkill and unnecessarily expensive for simple, routine tasks like summarizing emails. Using cheaper, less powerful models for these straightforward automations provides significant cost savings without sacrificing performance where it's not needed.