Enterprise AI adoption is hitting a wall: cost. As companies move from prototype to production, the bill for token consumption skyrockets. GPT-4o and Claude 3.5 Sonnet are powerful, but they charge by the token. Optimizing your prompt engineering is step one, but optimizing your data format is the hidden lever for massive savings.
The Hidden Tax of JSON
JSON is the standard for web APIs, so it became the default for LLM inputs. But JSON was designed for strict machine parsing, not token efficiency. It is verbose. Every quote mark, colon, and bracket is a token (or part of one). In a large dataset, these structural characters can account for over half of your total token count.
Case Study: E-Commerce Catalog
We analyzed a typical e-commerce product feed containing 10,000 items. In standard JSON, the file size was roughly 4.2MB, translating to approximately 1.1 million tokens. After converting to TOON format, the same data represented only 650,000 tokens.
At GPT-4o pricing (₹420/1M input tokens), this single optimization saves ₹189 per run. If you run this pipeline daily, that's over ₹67,200/year in savings for just one task.
Implementation Strategy
Integrating TOON is low-risk. Since the conversion is reversible, you can maintain your existing database in JSON or SQL and simply pass the data through a TOON converter before sending it to the LLM API. The model receives the denser format, processes it with the same accuracy, and you pay less.