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Slash your LLM token costs by 30-60% with TOON Format - a revolutionary serialization format designed specifically for AI applications. Learn how this JSON alternative can dramatically reduce your API expenses.
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Are you tired of expensive LLM API calls eating into your budget? If you're working with Large Language Models and dealing with structured data, you're likely overpaying for tokens due to JSON's verbose format. Token-Oriented Object Notation (TOON) is a revolutionary serialization format that can slash your token costs by 30-60% without sacrificing data integrity.
In this comprehensive guide, we'll explore what TOON format is, how it achieves dramatic token savings, and when you should use this game-changing technology for your AI applications.
TOON stands for Token-Oriented Object Notation - a compact, human-readable serialization format designed specifically for passing structured data to Large Language Models with significantly reduced token usage. Think of it as JSON's efficiency-focused cousin that was built from the ground up for the AI era.
Key Characteristics:
๐ก TOON's Sweet Spot: Uniform arrays of objects with multiple fields per row and consistent structure across items - think database query results, analytics data, and API responses.
As AI applications scale and context windows grow larger, developers are passing more data to LLMs. However, standard JSON is incredibly token-inefficient due to repetitive structure.
Consider this simple user data example:
{ "users": [ { "id": 1, "name": "Alice", "role": "admin" }, { "id": 2, "name": "Bob", "role": "user" }, { "id": 3, "name": "Charlie", "role": "user" } ] }
Token count: ~125 tokens (GPT-4 tokenizer)
Notice the problem? The keys "id", "name", and "role" appear three times - once for each user. This redundancy compounds exponentially with larger datasets.
TOON represents the same data with dramatic efficiency:
users[3]{id,name,role}: 1,Alice,admin 2,Bob,user 3,Charlie,user
Token count: ~54 tokens Savings: 57% fewer tokens!
When TOON encounters arrays of objects with:
It automatically converts to tabular format:
array_name[count]{field1,field2,field3}: value1,value2,value3 value1,value2,value3
This is where the magic happens - field names are declared once instead of repeating for every row.
For nested objects, TOON uses YAML-style indentation instead of curly braces:
TOON:
user: name: Alice profile: age: 30 city: New York
JSON equivalent:
{ "user": { "name": "Alice", "profile": { "age": 30, "city": "New York" } } }
TOON only quotes strings when absolutely necessary (when they contain delimiters, colons, or resemble numbers/booleans). This eliminates thousands of unnecessary quote characters.
Token counts measured using GPT-4 o200k_base tokenizer. All comparisons against formatted JSON with 2-space indentation.
โ ๏ธ Important: These benchmarks showcase datasets optimized for TOON's strengths. Real-world performance varies based on your data structure.
Use JSON in your application logic, then convert to TOON right before sending data to an LLM. This keeps your codebase maintainable while optimizing token usage where it matters most.
Try TOON instantly with the free online converter to see immediate efficiency gains with your data.
npm install @toon-format/toon
import { encode, decode } from '@toon-format/toon'; const data = { users: [ { id: 1, name: 'Alice', role: 'admin' }, { id: 2, name: 'Bob', role: 'user' } ] }; const toon = encode(data); console.log(toon); // Convert back to JSON const original = decode(toon); console.log(original);
pip install toon-format
from toon_format import encode, decode data = { 'users': [ {'id': 1, 'name': 'Alice', 'role': 'admin'}, {'id': 2, 'name': 'Bob', 'role': 'user'} ] } toon = encode(data) print(toon) # Convert back to original original = decode(toon) print(original)
Integrate TOON into your LLM prompts for immediate cost savings:
const prompt = `Analyze this user data: ${encode(userData)} Provide insights on user roles and activity patterns.`; // Send to OpenAI, Anthropic, etc. const response = await openai.chat.completions.create({ model: "gpt-4", messages: [{ role: "user", content: prompt }] });
The LLM receives the data in an optimized format, reducing your token costs while maintaining full data accessibility.
TOON Format represents a paradigm shift in how we approach data serialization for AI applications. By intelligently optimizing token usage without sacrificing readability or data integrity, TOON offers a practical solution to one of the most pressing cost challenges in modern AI development.
Whether you're building analytics dashboards, processing e-commerce data, or making frequent LLM API calls, TOON provides an immediate path to significant cost optimization. The 30-60% token savings can translate to substantial budget reductions at scale.
Ready to optimize your LLM costs? Start by testing your data with the TOON format converter and see the immediate impact on your token usage. Your budget will thank you.
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