AI Tokenmaxxing: Token and Cost Optimisation
Every AI-driven workflow has a real, ongoing cost attached to it, paid in tokens. As businesses lean more heavily on LLMs for content, support and internal operations, that spend adds up quickly and often invisibly. Tokenmaxxing is about getting the most useful output for the lowest realistic spend.
What it typically includes
- Auditing current AI and LLM usage and where token spend is going
- Prompt and workflow restructuring to cut wasted tokens without cutting quality
- Choosing the right model tier for each task, rather than the most expensive one by default
- Caching, batching and context-management strategies to reduce repeated spend
Who this is for
Businesses running AI tools at scale internally or in client-facing products, where token spend has become a real budget line rather than a rounding error.
Part of our wider AI in Marketing offering.
