Unlike companies that resell tokens for every query, Serval uses expensive models once to create a durable script. This automation is executed repeatedly at low cost. This "generate-once, run-many" approach dramatically improves unit economics and insulates the business from high token consumption.

An effective cost-saving strategy for agentic workflows is to use a powerful model like Claude Opus to perform a complex task once and generate a detailed 'skill.' This skill can then be reliably executed by a much cheaper and faster model like Sonnet for subsequent use.

To combat rising AI costs, firms are creating hybrid systems that use cheaper "worker" models for routine tasks while delegating complex problems to powerful "advisor" models. This approach, used by Harvey and explored by Microsoft, can outperform state-of-the-art models alone for a fraction of the cost.

Relying solely on premium models like Claude Opus can lead to unsustainable API costs ($1M/year projected). The solution is a hybrid approach: use powerful cloud models for complex tasks and cheaper, locally-hosted open-source models for routine operations.

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").

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.

While AI models have different behaviors, their core strength is instruction following. By creating thorough 'skills,' developers can achieve consistent outputs from different frontier models, effectively commoditizing the underlying model and reducing vendor lock-in.

A cost-effective AI strategy involves using a powerful, expensive model once to solve a complex task, then using a system like M0 to distill that solution into reusable "experience" and "skill" records. Cheaper models can then leverage this pre-packaged knowledge to execute the same task with higher success rates and significantly lower token costs.