TOON: Slashing LLM Costs with Schema-Aware Token Optimization
By Bruno Couriol
Published on November 23, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on InfoQ.
Summary
TOON (Token-Oriented Object Notation) is a recently introduced, schema-aware data format positioned as an alternative to JSON, specifically engineered to significantly reduce token consumption when interacting with Large Language Models (LLMs). Benchmarks indicate that TOON can achieve token savings of up to 40% compared to JSON in certain data shapes, leading to the potential for substantial reductions in LLM and inference costs for AI applications.
Why It Matters
This development is a crucial indicator of the LLM industry's maturation, shifting focus from pure model capability to practical operational efficiency and cost-effectiveness. For professionals in the AI space, TOON represents a direct answer to the escalating costs associated with LLM inference, which can quickly become prohibitive for high-volume or real-time applications. By potentially cutting token consumption by a significant margin, TOON could unlock new economic viability for a wider range of LLM-powered services and products.
Beyond immediate cost savings, the emergence of schema-aware, token-optimized data formats like TOON signals an evolution in how we design and interact with AI systems. It highlights the growing importance of the entire LLM stack, from prompt engineering and model selection to data serialization and output parsing. AI architects and developers must now consider data format choices as a critical lever for performance and budget control, integrating such innovations into their system design. This isn't merely a technical tweak; it's a strategic move towards democratizing LLM access, making advanced AI more scalable and economically sustainable for enterprises navigating the complexities of large-scale AI deployment.