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.

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.

It's counterintuitive, but using a more expensive, intelligent model like Opus 4.5 can be cheaper than smaller models. Because the smarter model is more efficient and requires fewer interactions to solve a problem, it ends up using fewer tokens overall, offsetting its higher per-token price.

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.

The process of 'distillation' involves using a large, expensive LLM to perform a task repeatedly. The resulting prompts and responses then become the training data to create a smaller, specialized, and much cheaper Small Language Model (SLM) that can perform that specific task, potentially saving 90% on inference costs.

M0 organizes agent knowledge into two distinct layers: a high-level "Experience" summary outlining strategy and cautions, and a detailed "Skill" layer with structured operational steps. This allows an agent to load the compact strategy first and only retrieve operational details when necessary, keeping the active prompt lean and efficient.

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").

As enterprises scale AI, the high inference costs of frontier models become prohibitive. The strategic trend is to use large models for novel tasks, then shift 90% of recurring, common workloads to specialized, cost-effective Small Language Models (SLMs). This architectural shift dramatically improves both speed and cost.