TOON (Token-Oriented Object Notation) is a data serialization format designed specifically for LLM prompts to be highly efficient. It dramatically reduces token usage by 30-60% compared to JSON while remaining structured and human-readable. By using a tabular format for arrays and minimal syntax for objects, TOON makes your data cheaper and faster to process with AI models.

What's the difference between TOON and JSON?

The key difference is token efficiency. JSON is verbose, with brackets, quotes, and commas that consume tokens. TOON is a more compact syntax designed for LLMs, representing arrays as tables with headers and using minimal punctuation. This efficiency directly translates to significant cost savings on your LLM API bills, especially for large or repeated datasets.

How much can I save with TOON?

You can typically expect to save 30-60% on LLM tokens compared to using JSON. For large datasets or frequent API calls, this translates directly into significant cost savings. Data with repeated structures, like API responses or database results, often sees savings at the higher end of this range (40-60%).

Is TOON compatible with all LLMs?

Yes. TOON is a simple text format that works flawlessly with all major large language models, including those from OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), and Meta (LLaMA). Since any LLM can process plain text, they can all be instructed to understand and parse the TOON format with a simple instruction in your prompt.

Can I convert TOON back to JSON?

Absolutely. TOON is fully and losslessly reversible. Our converter tool supports bidirectional conversion, meaning you can convert TOON back to the exact original JSON structure without any data loss. This allows you to use TOON for efficiency and then convert back to JSON for compatibility with other tools.

What types of data work best with TOON?

TOON can represent any valid JSON data, but it delivers the highest token savings (40-60%) on uniform tabular data. This includes database query results, API responses with lists of objects, analytics data, or product catalogs. While TOON fully supports nested objects and arrays, the token reduction is most dramatic with flatter, more repetitive data structures.

Is my data safe when using this converter?

100% safe. All conversion from JSON to TOON (and back) happens locally in your browser. Your data is never sent to any server, never stored, and never seen by us. The converter even works offline once the page has loaded, guaranteeing your information remains private.

Yes, completely free. Both this TOON converter and the underlying TOON format specification are open and free to use without any limits, file size restrictions, or premium features. It's an open-source effort to make working with LLMs more efficient for everyone.

The Ultimate Guide to Calculating API Savings with TOON

API Optimization

Cost Management

If you are running a production application powered by Large Language Models (LLMs), you already know the pain of the monthly invoice. Whether you are using OpenAI’s GPT-4, Anthropic’s Claude 3, or open-source models on hosted infrastructure, you are paying for every single token that passes through the wire.

We often focus on prompt engineering or model quantization to reduce costs, but there is a lower-hanging fruit that is strictly structural: the data format itself. Switching from the syntactically heavy JSON to the streamlined TOON format can yield massive savings. But as an engineer or CTO, you can't just operate on "hunches." You need hard data to justify the refactor.

Here is how to accurately calculate the financial impact of switching your API payloads to TOON, including the formulas you need to build your own calculator.

The Core Savings Logic

At its most basic level, the savings come from removing the syntactic sugar of JSON—the braces, the quotes, and the commas—that the LLM understands but doesn't actually need to process the semantic meaning of your data.

To get your baseline metrics, you need to look at the differential between your current state and the future state. Here are the fundamental formulas you will use for your analysis.

1. Calculating Token Reduction

First, you need to determine the efficiency gain. This isn't a guess; it's a precise measurement derived from a sample of your actual payloads.

2. Projecting Financial Impact

Once you have that percentage, the financial implication is calculated against your monthly burn rate. Note that for high-volume applications, even a small percentage point difference here scales into thousands of dollars.

Step-by-Step Execution Plan

You need a number you can take to your CFO or Engineering Lead. Here is the methodology to get it.

Step 1: Establish Your Baseline

Before writing code, audit your current usage. Open your billing dashboard and specific LLM provider logs to pull these four metrics:

Total Monthly Requests: The volume of calls.

Average Tokens per Request: Combine input and output tokens.

Cost per 1K Tokens: Specific to your model (e.g., GPT-4o vs. GPT-3.5).

Current Monthly Spend: The total dollar amount.

Step 2: The "Sampling Test"

Do not try to convert your entire database to calculate savings. You only need a representative sample. Take 10 to 20 of your most typical JSON payloads—the ones that represent the bulk of your traffic.

Let’s look at a real example of a User Profile object conversion to see the token difference:

Original JSON (35 Tokens):

TOON Format (18 Tokens):

In this specific instance, the token count dropped from 35 to 18. That is a 48.6% reduction. Repeat this process for your 20 samples to find your average reduction percentage.

Step 3: Calculate the ROI

Savings are great, but implementation isn't free. You need to calculate how fast the switch pays for itself to determine if the engineering effort is worth it.

Real-World Scenarios

To illustrate what these formulas look like in practice, let's run the numbers on three common business profiles based on typical market rates.

Scenario A: Mid-Size E-commerce Platform

Traffic: 1.5M requests/month

Model: GPT-4 Turbo

Current Spend: $30,000/month

TOON Impact: 52% token reduction (verified via sampling)

By applying the reduction formula, their projected monthly cost drops to roughly $14,400.

The Result:

Monthly Savings: $15,600

Annual Savings: $187,200

If it takes a senior developer a full week (40 hours at $100/hr) to update the prompts and parsers, the implementation cost is $4,000. The ROI timeline is 0.26 months—meaning the project pays for itself in about 8 days.

Scenario B: Enterprise AI Platform

Traffic: 6M requests/month

Model: Claude 3 Opus (High intelligence/High cost)

Current Spend: $472,500/month

TOON Impact: 58% token reduction

Because they are using a "smarter," more expensive model, the savings are exponential. A 58% reduction saves them $274,050 per month.

The Result:

Implementation: 160 hours (One month of dev time) = $24,000

ROI Timeline: 0.09 months (Less than 3 days)

Annual ROI: 13,602%

Scenario C: Small SaaS Wrapper

Traffic: 150k requests/month

Model: GPT-3.5 Turbo (Commodity pricing)

Current Spend: $90/month

TOON Impact: 48% reduction

Here, the savings are about $43/month. If the implementation costs $600, it will take 1.4 months to break even. While the dollar amount is lower, an 86% annual ROI is still technically a win, though it might be deprioritized in favor of shipping new features.

Advanced Factor: Variable Request Sizes

If your application has wild variance in request sizes (e.g., some requests are 100 tokens, others are 5,000), a simple average might mislead you. You should use a weighted average for accuracy.

The "Hidden" Multipliers

When calculating your savings, don't make the common mistake of looking only at the immediate API bill. There are technical efficiencies that compound the value of TOON:

Context Window Maximization: If TOON compresses your data by 50%, you effectively double your context window. This allows for few-shot prompting examples that weren't possible with JSON, potentially improving model accuracy without moving to a more expensive model tier.

Latency Reduction: Fewer tokens mean the LLM generates the response faster.

Infrastructure Load: Smaller payloads mean reduced bandwidth and slightly faster serialization/deserialization on your backend.

Conclusion

The math is simple: the syntax characters in JSON are expensive noise. By switching to TOON, you stop paying for the packaging and start paying only for the product.

Run the formulas above on your own data. If you see a reduction greater than 30% and your monthly bill exceeds $1,000, the ROI is almost certainly immediate.