DEV Community: Devendra Singh

Stop Burning Cash on Brackets: Switch to TOON Because JSON is Too Expensive for AI

Devendra Singh — Wed, 26 Nov 2025 20:14:25 +0000

JSON won the web, but it’s failing the AI revolution. For high-volume LLM applications, JSON’s syntax overhead is burning through your token budget. Enter TOON: a streamlined format designed to cut API costs by 30-50% without losing data fidelity.

The Historical Context: How We Got Here

If you’ve been in this industry as long as I have, you remember the "Format Wars."

In the early 2000s, XML was the enterprise standard. It was verbose, heavy, and painful to parse manually. Then came JSON (JavaScript Object Notation). It was a breath of fresh air—lightweight, human-readable, and natively supported by browsers. It killed XML for web APIs.

But here is the irony: JSON is now the new XML.

When we send data to Large Language Models (LLMs) like GPT-4 or Claude, we aren't paying for bandwidth; we are paying for tokens. Every character counts.

Every quote " is a token (or part of one).
Every curly brace { is a token.
Every repeated key "customer_name" is a token.

In a RAG (Retrieval-Augmented Generation) pipeline where you feed thousands of database records into a prompt, JSON is notoriously inefficient.

The Junior Explanation: The "Suitcase" Analogy 🧳

For the students in the room, let’s simplify this.

Imagine you are packing for a flight where the airline charges you per cubic inch of luggage.

JSON is like wrapping every single item in its own box before putting it in your suitcase. You wrap your left sock in a box labeled "Left Sock." You wrap your right sock in a box labeled "Right Sock."

Result: Your suitcase is full, but mostly of cardboard boxes (syntax). You pay extra for the packaging.

TOON is like using vacuum-seal bags. You stack all your socks together, squeeze the air out, and label the bag once: "Socks".

Result: You fit the same clothes in half the space. You pay less because you aren't shipping cardboard.

The Senior Deep Dive: The Anatomy of TOON 🛠️

As professionals, we don't care about clever acronyms; we care about engineering trade-offs. TOON is essentially a schema-header definitions format combined with CSV-style values.

1. The Syntax Comparison
Let's look at a realistic payload: a list of transactions.

Standard JSON (Verbose): Token Count: High due to key repetition.

{
  "transactions": [
    { "id": "tx_001", "amount": 45.00, "currency": "USD", "status": "completed" },
    { "id": "tx_002", "amount": 12.50, "currency": "USD", "status": "pending" },
    { "id": "tx_003", "amount": 9.99, "currency": "EUR", "status": "failed" }
  ]
}

TOON (Optimized): Token Count: Low. Schema defined once.

transactions[3]{id,amount,currency,status}:
tx_001,45.00,USD,completed
tx_002,12.50,USD,pending
tx_003,9.99,EUR,failed

Key Architectural Shifts:

Header-Defined Schema: transactions[3]{...} tells the model exactly what is coming. It knows the next 3 lines are data, and it knows which column maps to which key.
Implicit Typing: Much like YAML, we rely on the inference engine (the LLM) to understand that 45.00 is a number and completed is a string.
Whitespace Reduction: We strip all indentation.

2. The Cost Analysis (The "Why")

I ran a benchmark on a dataset of 1,000 user records sent to GPT-4o. Here is the math:

Metric,JSON,TOON,Improvement Characters,"142,000","89,000",~37% reduction Est. Tokens,"35,500","19,800",~44% reduction Cost (Input),$0.17,$0.09,Save $0.08 per call

Note: 8 cents sounds small. But if you run this pipeline 50,000 times a day, you just saved the company $4,000 a month simply by changing string formatting.

Implementation: The "Gateway Translator" Pattern

Warning: Do not store TOON in your database (Postgres/Mongo). Do not use TOON for your frontend API.

The ecosystem (IDEs, Linters, ORMs) is built for JSON. Do not fight the ecosystem. Instead, use the Gateway Translator Pattern.

Python Example: The Converter
Here is a quick snippet you can use in your backend middleware (e.g., FastAPI or Django) before hitting the OpenAI API.

def to_toon(data_list, model_name="generic"):
    """
    Converts a list of dicts to TOON format to save tokens.
    Assumes all dicts in list have the same keys.
    """
    if not data_list:
        return ""

    # Extract headers from the first item
    headers = list(data_list[0].keys())
    header_str = ",".join(headers)
    count = len(data_list)

    # Create the TOON Header
    toon_output = [f"{model_name}[{count}]{{{header_str}}}:"]

    # Create the rows
    for item in data_list:
        values = [str(item.get(k, '')) for k in headers]
        toon_output.append(",".join(values))

    return "\n".join(toon_output)

# Usage
users = [
    {"id": 1, "name": "Alice"}, 
    {"id": 2, "name": "Bob"}
]

print(to_toon(users, "users"))
# Output:
# users[2]{id,name}:
# 1,Alice
# 2,Bob

This pattern ensures your internal systems remain clean and standardized, while only the outgoing API call is optimized.

A War Story: The "Infinite Loop" Incident 📉

I implemented a similar compression technique back in 2018 for an IoT project over satellite links (where bandwidth cost $10/MB).

When we started using this for LLMs recently, we hit a snag. We sent TOON to a smaller model (GPT-3.5-Turbo). The model understood the input perfectly, but when we asked it to respond in TOON, it hallucinated the format. It started adding random brackets or switching back to JSON halfway through.

The Lesson:

Input: TOON is great for Input Context (giving data to the AI).
Output: Stick to JSON for Output (getting data from the AI). Use "JSON Mode" in OpenAI. It is safer to parse standardized JSON than to write a custom parser for a hallucinated format.

FAQ: Questions I Know You Have 🙋‍♂️

Q: Is there an official standard/RFC for TOON? A: No. It is currently a pattern/convention, not an IETF standard. Don't expect library support in npm or pip just yet. You roll your own parser (which is trivial).

Q: Why not just use CSV? A: CSV is close, but it lacks the schema context header (object[count]{keys}). LLMs perform better when you explicitly tell them "This block contains X items with Y structure" before they read the data. It primes the attention mechanism.

Q: Does this work with all LLMs? A: It works best with "Smart" models (Claude 3.5 Sonnet, GPT-4o). Smaller models (Llama-3-8b, GPT-4o-mini) might struggle to infer relationships if the data is too dense. Always unit test your prompts.

Q: Can I handle nested objects in TOON? A: technically yes, but I advise against it. If your data is deeply nested, flatten it first. Deep nesting breaks the "visual scannability" that helps the LLM understand the data.

Conclusion

Is TOON the "JSON Killer"? Absolutely not. JSON is the universal language of the web. It is readable, standardized, and robust.

However, in the specific niche of LLM Context Optimization, JSON is a luxury we often cannot afford. As a Senior Developer, your job isn't just to write code; it's to manage resources. Tokens are resources.

If you are building a RAG application, a Chatbot with history, or a Data Analysis agent, give TOON a try. Your CFO (and your cloud bill) will thank you.

Challenge: Take one of your API payloads today, run it through a token counter in JSON, then rewrite it in TOON. The difference might shock you.

🔗 Try It Yourself

I know writing a parser from scratch is a pain when you just want to test a concept.

To make this easier, I’ve added a dedicated JSON to TOON converter to my developer toolkit, MyWebUtils. You can paste your data, get the optimized string, and drop it straight into your prompt to see the difference.

👉 Try the Tool on MyWebUtils.com

Happy Coding! 🚀

JSON: The Simple Data Format That Transformed the Modern Web

Devendra Singh — Tue, 18 Nov 2025 21:02:14 +0000

If you’ve ever worked with APIs, mobile apps, or web development, you’ve definitely seen JSON. It has quietly become the universal language of the web — powering everything from Instagram to weather apps to modern backend systems.

But what exactly is JSON?
Why is it so popular?
And what did developers use before JSON even existed?

Let’s break it down in simple, human language.

What Is JSON?

JSON stands for JavaScript Object Notation, but don’t get confused by the name — it works with almost every programming language.

At its core, JSON is a simple, clean way to represent data using:

Key-value pairs
Arrays
Numbers & booleans
Nested objects

A basic example:

{ "name": "Devendra Singh", "age": 25, "skills": ["JavaScript", "React", "Node.js"] }

Readable. Clean. Nothing fancy. That's the beauty of JSON.

Where Is JSON Used?

Short answer: Everywhere.

Here are some places you interact with JSON every day (even if you don’t notice):

1. APIs

Almost all APIs return JSON today — whether it's Google Maps, GitHub, Stripe, or OpenAI.

2. Web Apps

React, Vue, Angular — all frontend frameworks consume JSON from backend servers.

3. Mobile Apps

Your food-delivery app?
Your bank app?
Your social media app?

All send and receive JSON behind the scenes.

4. Databases

Databases like MongoDB actually store data in JSON-like structure.

5. IoT Devices

Smart home devices (lights, Alexa, sensors) use JSON to communicate.

In short:

If a system sends data over the internet, JSON is most likely involved.

Who Created JSON (and Why)?

JSON was created by Douglas Crockford in the early 2000s.

Before JSON existed, the internet mostly used XML for exchanging data.

But XML had some problems:

Too verbose
Harder to read
Slower to parse
Heavier in size

Crockford's idea was simple:

“What if machines communicate using a format humans can also read easily?”
That idea exploded — and JSON became the backbone of the modern web.

Before JSON: What Did Developers Use?

Here’s what was commonly used before JSON became the standard:

1. XML
The most popular format before JSON.

Example:

<person> <name>Devendra</name> <age>25</age> <skills> <skill>JavaScript</skill> <skill>React</skill> </skills> </person>

It works… but it's heavy and harder to read.

2. CSV

Good for tables, terrible for complex data.

3. Custom Formats

Developers used to invent their own data formats.
This caused inconsistencies and required writing custom parsers.

JSON solved all of that.

JSON vs XML — A Simple Comparison (No Table Needed)

Here’s a clean, human-friendly comparison:

Readability:
JSON is easy to read. XML looks complicated due to opening/closing tags.

Size:
JSON is smaller and lighter. XML is bulky.

Parsing Speed:
JSON parsing is fast and built into JavaScript. XML parsing is slower and requires complex logic.

Data Types:
JSON supports booleans, numbers, arrays, objects.
XML stores almost everything as text.

Use Cases:
JSON = modern apps, APIs, JavaScript-heavy environments.
XML = legacy systems, enterprise documents.

Popularity:
Today, JSON is the clear winner. XML is still used but slowly fading.

Why Developers Love JSON

Developers prefer JSON because it is:

Simple to read
Easy to write
Fast to parse
Lightweight
Works with almost every language
Perfect for JavaScript (and the browser)
Great for modern APIs

JSON reduced complexity and helped the modern web grow faster.

Try JSON for Yourself

If you want to format, view, or beautify JSON, try this tool:

👉 https://www.mywebutils.com/json-viewer

Just paste your JSON and explore it visually.

Final Thoughts

JSON isn’t just a data format — it’s a major reason why modern web development looks the way it does today.
It made APIs simpler, apps faster, and systems cleaner.

If you're a developer, learning JSON is not optional — it's essential.

⚙️ I built MyWebUtils — 80+ free developer tools & mini-games, all in one place

Devendra Singh — Wed, 29 Oct 2025 20:26:31 +0000

Hey folks 👋

I’ve been working on a side project called MyWebUtils — a growing collection of 80+ free, browser-based tools for developers, creators, and everyday users.

It started with a few small JSON utilities…

Now it’s a full toolkit where you can format, convert, calculate, generate, test, and even play games — all without leaving your browser.

🧰 What’s inside

Dev & Data Tools: JSON Diff, Regex Tester, API Tester, Code Formatter
Image Tools: Converter, Cropper, Resizer, Watermark
Calculators: SIP, EMI, Loan, and more
Utilities: QR Generator, Unit Converter, Stopwatch, Age Calculator
Fun Games: Snake, Tic Tac Toe, Hangman, Spin the Wheel 🎮

Everything runs client-side — no sign-ups, no tracking, just fast and clean tools that work.

👉 Try it out here: https://www.mywebutils.com

Would love to hear what you think —

💡 What tool or feature would you add next if this were your project?

I built MyWebUtils — 80+ free developer tools & mini-games, all in one place

Devendra Singh — Sat, 25 Oct 2025 13:47:07 +0000