The future of enterprise AI isn't choosing one provider. Instead, companies will use a "composable model" approach, routing queries to a combination of powerful frontier models and their own fine-tuned open-source models. This strategy, dubbed the "council of LLMs," optimizes for cost, performance, and specialization on proprietary data.

For typical enterprise tasks like code migration, using an optimized control plane with an open-source model can be over 16 times cheaper than using a frontier model like Claude Opus. While it may be slower, the massive cost savings make it a compelling business alternative.

Running premium AI models constantly is prohibitively expensive. A cost-effective strategy is to use a cheaper model as a "manager" to understand a high-level goal, break it down, and then delegate the execution of sub-tasks to multiple, short-lived "child" sessions running more powerful models.

Instead of relying on a single large AI model, companies are adopting "model orchestration" to control costs. This involves using a router to send prompts to the most appropriate model based on the task, often cascading from cheap, small models to more expensive ones only when necessary.

To optimize costs, users configure powerful models like Claude Opus as the 'brain' to strategize and delegate execution tasks (e.g. coding) to cheaper, specialized models like ChatGPT's Codec, treating them as muscles.

A hybrid approach to AI agent architecture is emerging. Use the most powerful, expensive cloud models like Claude for high-level reasoning and planning (the "CEO"). Then, delegate repetitive, high-volume execution tasks to cheaper, locally-run models (the "line workers").

The smartest 'AI-pilled' companies adopt a two-tiered model strategy. They use expensive, frontier models for internal, high-leverage tasks like creating new knowledge and optimizing processes. However, they use cheaper, open-weight models in the 'bill of materials' for the customer-facing product to manage costs effectively.

To optimize AI costs in development, use powerful, expensive models for creative and strategic tasks like architecture and research. Once a solid plan is established, delegate the step-by-step code execution to less powerful, more affordable models that excel at following instructions.

According to NVIDIA's VP, the modern approach to enterprise AI involves mixing models. Use expensive, powerful frontier models for complex, high-value tasks like agentic planning. For more trivial, high-volume tasks like document summarization, use cheaper, fine-tuned open-source models to optimize cost.