In modern software development—and especially in projects that integrate Artificial Intelligence (AI)—, the way we structure the data matters more than ever.
For years, JSON It has been the undisputed standard for APIs, integrations, and storage. But with the arrival of Language Models (LLMs), a new format has emerged that promises to transform efficiency: TOON (Token-Oriented Object Notation).
And the question is not "which one is better?", but:
How can we leverage them together to achieve both efficiency and compatibility?
JSON (JavaScript Object Notation) It is the classic and universal format we use to structure information.
It is characterized by being:
✅ Easy to read and write.
✅ Ideal for APIs and integrations.
✅ Compatible with all languages and databases.
✅ Extensive support in technological ecosystems.
However, JSON has a weak point:
in AI environments, generates many repeated characters (keys, quotation marks, field names), which translates to more tokens, higher costs and less available context in language models.
TOON (Token-Oriented Object Notation) It arises to optimize data reading by AI models.
It is designed for:
Reduce tokens
Eliminate noise
To be more compact and efficient
Facilitate the processing of tabular or repetitive data
Traditional JSON:
{
«users»: [
{ "id": 1, "name": "Alice", "role": "admin" },
{ "id": 2, "name": "Bob", "role": "user" }
]
}
TOON:
users[2]{id,name,role}:
1,Alice,admin
2, Bob, user
Fewer quotation marks.
Fewer keys.
Fewer characters.
Fewer tokens.
In repetitive structures, TOON reduces between 30% and 60% token consumption versus JSON.
| Aspect | JSON | TOON |
|---|---|---|
| Syntax | Verbosity, with quotation marks and braces | Compact, tabular |
| Ecosystem | Ultra mature and universal | Growing |
| AI Optimization | Not optimized | Designed for LLMs |
| Ideal data type | Diverse and complex structures | Tabular and repetitive data |
| Read by humans | Very legible | More technical/compact |
| Recommended use | APIs, databases, storage | AI prompts, big data |
None of them is “better”, They fulfill different roles.
No. And you shouldn't.
JSON remains the most stable, compatible, and universal format in the technology ecosystem.
TOON does not compete with JSON, It complements it.
The optimal strategy is to intelligently combine both formats.
The recommended architecture for maximum efficiency is:
1️⃣ Internal systems operate with JSON
(Safe, compatible, standard)
2️⃣ Before sending data to the AI model, you convert JSON → TOON
(To reduce tokens and improve results)
3️⃣ Upon receiving the model's response, you convert TOON → JSON
(To integrate it into your existing systems)
✔️ Full compatibility
✔️ 30% – 60% less tokens
✔️ Much lower AI costs
✔️ Cleaner structures for LLMs
✔️ You don't need to change your entire architecture
This approach is already standard in companies that work with large volumes of data.
Use it when your data is:
✔️ Repetitive (same pattern)
✔️ Tabular
✔️ Bulky
✔️ Intended for an LLM (GPT, Claude, Gemini, etc.)
✔️ Part of a long or concatenated prompt
⛔ They are very varied structures
⛔ It's a public API (JSON is required)
⛔ The volume of data is so small that it's not worth converting it
TOON is not here to dethrone JSON.
He comes to strengthen it where JSON was not designed to operate:
the environments of intensive AI, where every token counts.
🔹 Reduce AI costs
🔹 Improve the quality of responses
🔹 Make better use of the context
🔹 Maintain compatibility with your current systems
🔹 Optimize architecture performance without redoing it
At The Cloud Group we firmly believe that:
The future is not JSON or TOON.
The future is JSON + TOON working as a single strategy.
In The Cloud Group We integrate hybrid JSON-TOON solutions designed for:
Token optimization
Better results with AI
Efficiency in advanced prompts
Production-ready integrations
Real reduction in LLM usage costs
📩 Request your free consultation and discover how to optimize your systems for the future of AI.
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