Hi Product Hunt! I am the creator of ZEON. Building applications powered by Large Language Models (LLMs) is incredibly exciting, but as developers, we quickly run into a silent budget killer: JSON syntax. When feeding complex schemas or receiving structured outputs, we force LLMs to process thousands of brackets, curly braces, quotes, commas, and redundant keys. JSON was built for the web, not for AI context windows. Language models natively understand whitespace and indentation, yet we keep feeding them sparse matrices of characters. That frustration inspired me to build ZEON (Zero-overhead Encoding Object Notation). The goal was simple: create a data serialization format from the ground up that is perfectly structured for LLMs to read and write, while stripping away every single byte of unnecessary syntactic overhead. ZEON uses a suffix-driven tabular grammar and Pythonic indentation. The result? A massive reduction in token consumption. In our benchmarks, replacing a standard JSON schema with ZEON saves up to 56% in token costs. What this means for you: More room in your context window for the actual instructions. Smaller bills from the OpenAI/Anthropic/Google APIs. Faster structured generations (since the model has fewer syntax tokens to output). We already built open-source parsers for Python (PyPI) and JS/TS (NPM), and there is a fully featured VS Code Extension to give you syntax highlighting right out of the box. I would love for you to try our interactive token comparison on our website to see the token savings on your own schemas. I will be here all day to answer your questions and hear your feedback on the parser design and grammar rules. Let's build more efficient AI apps together!

ZEON Format

Stop wasting money on JSON. Save 50%+ tokens on your LLMs

Stop wasting money on JSON. Save 50%+ tokens on your LLMs