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.

To control spiraling AI costs, teams should first determine if a task can be solved with deterministic, rules-based logic. Using AI for problems that have a straightforward, non-AI solution is an inefficient use of resources and introduces unnecessary variability and expense.

Instead of running an LLM for recurring tasks, have the Hermes agent write the code once. Combine this with cost-effective models via OpenRouter to dramatically reduce token spend, in one case from $130 to $10 over five days.

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.

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.

Pega's CTO advises using the powerful reasoning of LLMs to design processes and marketing offers. However, at runtime, switch to faster, cheaper, and more consistent predictive models. This avoids the unpredictability, cost, and risk of calling expensive LLMs for every live customer interaction.

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.

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.

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.