JSON is Making You Lose Money!!! Slash LLM Token Costs with TOON Format
~ cat "JSON is Making You Lose Mone"~ cat post <<
Let's be real: every time you shove a bloated JSON blob into an LLM prompt, you're literally burning cash. Those curly braces, endless quotes, and repeated keys? They're token vampires sucking your OpenAI/Anthropic/Cursor bill dry. I've been there – cramming user data, analytics, or repo stats into prompts, only to hit context limits or watch costs skyrocket.
But what if I told you there's a format that cuts tokens by up to 60%, boosts LLM accuracy, and was cleverly designed for exactly this problem? Meet TOON (Token-Oriented Object Notation), the brainchild of Johann Schopplich – a dev who's all about making AI engineering smarter and cheaper.
Johann nailed it with TOON over at his original TypeScript repo: github.com/johannschopplich/toon. It's not just another serialization format; it's a lifeline for anyone building AI apps at scale.
Why JSON is Robbing You Blind in LLM Prompts
JSON is great for APIs and config files. But for LLM context? It's a disaster:
- Verbose AF: Braces
{}, brackets[], quotes around every key and string – all eating tokens. - Repeated Keys: In arrays of objects, every row repeats the same field names. 100 users? That's 100x "id", "name", etc.
- No Built-in Smarts: LLMs have to parse all that noise, leading to higher error rates on retrieval tasks.
- Token Explosion at Scale: A modest dataset can balloon to thousands of unnecessary tokens.
Result? Higher costs, slower responses, and more "context too long" errors. If you're querying GPT-5-nano or Claude with tabular data, JSON is quietly making you poor.
