Stop Using JSON ✋🏻: You’re Wasting Tokens, Money, and Compute — Switch to TOON

For more than a decade, JSON has been “good enough.”

• Good enough for REST APIs.
• Good enough for config files.
• Good enough for server-to-server communication.

But the environment we’re building in has changed dramatically.

We now operate in a world of:

• high-throughput microservices
• strict type systems
• schema-driven development
• real-time analytics
• AI agents are consuming millions of tokens a day
• order-of-magnitude more distributed communication than in 2010

JSON simply wasn’t designed for this reality.

The weaknesses we’ve been ignoring for years now show up as performance bottlenecks, higher costs, and subtle correctness issues.

In contrast, TOON (Typed Object-Oriented Notation) is emerging as a practical successor: a structured, type-accurate, compact notation built for modern software systems and LLM-centric workflows.

This article walks through the technical reasons TOON is superior, the real-world friction JSON creates, and how TOON can significantly improve both system performance and LLM compute efficiency.

What Is TOON? (A Clear, Non-Marketing Definition)

TOON (Typed Object-Oriented Notation) is a modern, human-readable data format designed around:

• True type fidelity (int, float, decimal, enum, datetime, binary)
• Minimal syntax (indentation over braces and commas)
• Built-in schema support
• Deterministic, round-trip consistency
• Backwards and forwards compatibility
• Low token overhead for LLMs

A simple TOON example:

user:
  id: 1201 (int)
  name: "Varun"
  balance: 1220.50 (decimal)
  active: true
  createdAt: 2025-11-17T10:00Z (datetime)
  transactions:
    - id: "tx_01"
      amount: 199.99 (decimal)
      type: "debit"
      timestamp: 2025-11-16T04:20Z (datetime)
      meta:
        ip: "192.168.1.1"
        device: "mobile"
    - id: "tx_02"
      amount: 500 (int)
      type: "credit"
      timestamp: 2025-11-15T20:30Z (datetime)
      meta: null

The same structure in JSON:

{
  "user": {
    "id": 1201,
    "name": "Varun",
    "balance": 1220.50,
    "active": true,
    "createdAt": "2025-11-17T10:00:00Z",
    "transactions": [
      {
        "id": "tx_01",
        "amount": 199.99,
        "type": "debit",
        "timestamp": "2025-11-16T04:20:00Z",
        "meta": {
          "ip": "192.168.1.1",
          "device": "mobile"
        }
      },
      {
        "id": "tx_02",
        "amount": 500,
        "type": "credit",
        "timestamp": "2025-11-15T20:30:00Z",
        "meta": null
      }
    ]
  }
}

The TOON version is cleaner, explicitly typed, and significantly cheaper to process — especially when you’re running it through LLMs at scale.

Where JSON Fails Modern Development

1. No Real Type Fidelity

JSON natively supports only:

• string
• number
• boolean
• null
• object
• array

Everything else is implied or faked:

• Dates are just strings.
• Decimals are quietly converted and rounded.
• Enums are indistinguishable from arbitrary strings.
• Binary data has to be shoved through base64.

For simple web payloads, that’s tolerable. For financial systems, analytics, or LLM agents that rely on exact structure and meaning, it becomes fragile.

2. Lossy Round-Trip Behaviour

Run this sequence often enough:

JSON → JavaScript → Python → Go → back to JSON

and you’ll start seeing:

• precision loss
• type drift
• formatting inconsistencies

Values that started as decimals may come back rounded. Datetimes might lose timezone information. Fields that were present may disappear or be renamed in subtly incompatible ways.

TOON is designed for deterministic round-tripping, so you can move data across services and languages without worrying that its shape or meaning will erode over time.

3. Token Explosion in LLM Workflows

LLMs don’t charge by byte — they charge by token.
Every structural symbol in JSON is a separate token:

• {
• }
• "
• :
• ,

Keys are quoted strings, which adds even more tokens. The result: JSON is symbol-heavy, and therefore token-heavy.

In LLM-powered systems, that means:

• larger prompts,
• higher inference costs,
• and less room in the context window for the actual content that matters.

LLM processing + JSON ends up as higher API bills and often worse reasoning because context gets squeezed.

4. No Native Schema Enforcement

JSON Schema exists, but in practice it’s:

• optional
• verbose
• rarely enforced consistently across teams

You can absolutely make JSON safer with discipline and tooling, but nothing about JSON itself encourages or guarantees that.

TOON, on the other hand, treats schemas as a first-class concept, not an afterthought.

5. Verbose and Noisy for Humans

JSON is technically “human-readable,” but only up to a point.

Once payloads get large, the braces, commas, and quotes become visual noise. Reading diffs becomes painful. Tracing a field through a complex JSON structure is harder than it needs to be.

TOON’s indentation-based, punctuation-light style is much friendlier for humans who have to work with these structures every day.

Deep TOON vs JSON Comparison

✔ 1. Serialization and Deserialization Speed

TOON can avoid much of the heavy lifting that JSON requires, such as:

• intensive quote parsing,
• deep brace matching,
• and the constant need to guard against trailing comma issues.

Result: parsing and serialization can be 25–60% faster, depending on payload complexity and your implementation.

At small scale, that’s nice. At microservice and agent scale, it’s significant.

✔ 2. Smaller Payload Sizes

Consider a simple payload.

JSON (49 bytes):

{"name":"Varun","active":true,"score":99.5}

TOON (36 bytes):

name: "Varun"
active: true
score: 99.5

That’s a 30–55% reduction in typical cases.

Multiply that by millions of requests, logs, or agent messages, and you’re looking at real savings in bandwidth, storage, and LLM tokens.

✔ 3. True Type Fidelity

TOON lets you express types explicitly:

age: 31 (int)
price: 1299.99 (decimal)
createdAt: 2025-04-04T12:00Z (datetime)
role: admin (enum)
data: <0101FFAA> (binary)

In JSON, everything is inferred from “string” and “number,” and every consumer is left to guess — or to bolt on custom conventions.

With TOON, the type information travels with the data, instead of being buried in docs or assumptions.

✔ 4. Human Readability

TOON leans on indentation rather than punctuation:

• Easier to scan with your eyes.
• Easier to diff in version control.
• Easier to maintain when schemas grow.

That gives you the simplicity of a configuration language with the structure of a data format.

✔ 5. Schema Enforcement

In TOON, schemas are natural and concise to embed:

schema:
  user:
    id: int
    name: string
    createdAt: datetime

You don’t have to reach for a separate ecosystem to say, “This is what valid data looks like.” It’s part of the same language.

JSON can achieve something similar, but only with external tooling and conventions.

✔ 6. Backwards and Forwards Compatibility

TOON includes built-in patterns for evolution over time:

address?: string      # optional addition
username -> name      # field rename mapping

This makes it clear when a field is optional or when a rename is intentional and should be mapped.

In JSON, these sorts of changes tend to show up as breaking changes, and you need additional logic and documentation to keep everything aligned.

The LLM Advantage: Why TOON Is Dramatically Cheaper Than JSON for AI Agents

For LLM-driven systems, this is the most important reason TOON is gaining traction.

LLMs bill per token, not per kilobyte. JSON’s structure is inherently token-dense:

• {, }, ", :, and , each consume tokens.
• Every key is a quoted string.
• Nesting multiplies the problem.

In tool-calling, agent loops, and multi-step reasoning chains, JSON routinely bloats token usage by 2×–4× compared to more compact notations.

Token Comparison Example

JSON (59 tokens):

{
  "user": {
    "id": 1201,
    "name": "Varun",
    "balance": 1220.50,
    "active": true
  }
}

TOON (34 tokens):

user:
  id: 1201
  name: "Varun"
  balance: 1220.50
  active: true

That’s:

• 42% fewer tokens
• 42% lower LLM cost
• 42% more effective context window preserved

For teams running:

• AI agents
• multi-step reasoning pipelines
• tool-calling workflows
• autonomous bots
• RAG systems
• memory-based agent loops

these savings compound very quickly.

Example:

For 1 million agent cycles per month:

• JSON cost: ₹4.8 lakhs
• TOON cost: ₹2.7 lakhs
• Monthly savings: ₹2.1 lakhs

At production scale, those numbers become hard to ignore.

Real-World Problem → TOON Solution

1. Payment APIs Losing Decimal Accuracy

In many stacks, JSON values pass through a decimal → double → decimal pipeline, quietly losing precision along the way.

TOON lets you declare and preserve decimal values, which is critical for banking, billing, and other money-sensitive domains.

2. AI Agent Struggling to Parse JSON Tool Results

One missing quote or brace in a JSON blob can break an entire reasoning chain or tool call, especially when everything is orchestrated dynamically.

TOON’s indentation-based syntax dramatically reduces these failure modes and makes malformed structures easier to detect and recover from.

3. Microservices Bloated by Large JSON Configs

Large JSON configuration files are painful to read and maintain, and they balloon in size as features accumulate.

Switching those configs to TOON typically cuts their size roughly in half, making them both easier to manage and cheaper to ship around.

4. LLM Memory Stores Exceeding Context Windows

If you’re storing long-term memory, conversation history, or state in JSON, you hit context limits faster.

By switching to TOON, you reduce token overhead and free up more of the context window for actual content and reasoning, not punctuation.

Hypothetical Case Study: An LLM-Powered Microservice

Scenario

An AI agent coordinates database operations using JSON messages.

Per day:

• 60,000 function calls
• Average JSON payload: 3.1 KB (~2,150 tokens)

Problems

• High daily token spend
• Occasional model misinterpretations due to missing commas or malformed JSON
• Slower parsing and higher latency at peak traffic

After Switching to TOON

• Token count per call reduced by ~43%
• Parsing and formatting failures dropped by ~80%
• Daily inference cost decreased from ₹18,200 to ₹10,100

That’s ₹29.4 lakhs in annual savings, alongside improved reliability and simpler debugging.

Handling Common Developer Objections

“Is TOON harder to learn than JSON?”

No. In practice, TOON’s syntax is simpler than both JSON and YAML.

If you’ve ever read indented configuration files or structured logs, TOON will feel familiar within minutes.

“Will this break our current APIs?”

It doesn’t have to.

You can:

• use TOON internally for services, configs, and agent communication, and
• convert TOON → JSON at the edges for external clients that expect JSON.

That lets you modernize your internals without forcing a hard cutover on every consumer.

“Do we need new tooling?”

You’ll want TOON-aware tooling, but the basics are already in place:

• formatters
• linters
• schema validators
• TypeScript / Go / Python code generators

In other words, you’re not starting from zero.

Conclusion: JSON Was Built for 2010 — TOON Is Built for 2025 and Beyond

Modern systems demand:

• stronger type safety
• compact, efficient data
• predictable round-tripping
• built-in schema awareness
• AI-friendly serialization
• token efficiency for LLMs
• clean version and compatibility management

JSON has served us well, but it no longer checks all of those boxes.

TOON does — and it does so cleanly and deliberately.

For teams building high-performance systems or LLM-driven architectures, shifting to TOON is less a “nice-to-have” and more a competitive advantage.

A practical way to start:

• migrate configs,
• adopt TOON for internal APIs,
• or define LLM tool schemas in TOON first.

You’ll see the clarity, measure the token savings, and feel the reliability gains in your agents.

Once that value is obvious, you can roll TOON out across the rest of your stack.