TOON vs JSON: Data Serialization for the LLM Era

1. The problem you’re already paying for

You build systems. You hustle. You wire up backend APIs, data stores, UIs, and lately — you’re feeding data into large language models (LLMs). Here’s a truth you cannot ignore: every token you send an LLM costs you time, money, context window.And yet… most of us still use JSON, the decades-old go-to format, to funnel structured data into those models. But JSON wasn’t built for the “tokens = cost” world.

2. Enter TOON

Meet TOON (Token-Oriented Object Notation) — a serialization format optimized for LLMs. Its goal: same data, fewer tokens. Less syntactic noise. More efficient prompt/context design. According to recent write-ups, TOON can reduce token usage by 30-60% for the right kind of data. (DEV Community)

3. How TOON works (vs JSON)

JSON example

{ "users": [ { "id": 1, "name": "Alice", "role": "admin" }, { "id": 2, "name": "Bob", "role": "user" } ]}

TOON equivalent

users[2]{id,name,role}: 1,Alice,admin 2,Bob,user

What changed:

  • The schema (id,name,role) is declared once.
  • No quotes, braces, commas per object beyond the minimal row.
  • Less structural overhead. (FreeCodeCamp)
  • It’s optimized for uniform arrays of objects — that’s the sweet spot. (DEV Community)

4. When TOON shines — and when it doesn’t

Shines when:

  • You’re feeding an LLM large arrays of uniform objects (list of users, product items, events).
  • Token cost or context window matters.
  • The structure is flat or lightly nested.
  • You control the pipeline (can convert JSON → TOON) and don’t need full interchange with arbitrary external systems.

Struggles when:

  • Your data is deeply nested or objects have varying schema. TOON may lose its advantage or even cost more tokens. (DEV Community)
  • You need standard tooling, validators, interoperability across systems where JSON is expected.
  • Team lexicon is already full of JSON, tooling, pipelines — introducing a new format may add friction.

5. Engineering checklist for you (given your stack)

You have chops around full-stack, AI integrations, SaaS scale. Here’s how to approach TOON smartly:

  1. Benchmark first: Take a real use case where you send large structured data into an LLM. Measure token count via JSON. Then convert to TOON. Is the delta meaningful?
  2. Use TOON at the boundary: Keep your system internally JSON (because tooling, ecosystem, dev familiarity). Only convert at the point where it goes into the LLM.
  3. Provide fallback/escape hatch: If TOON becomes unwieldy due to schema drift, fallback to JSON.
  4. Document & standardize: Your future self—and team—should know when TOON is used, how conversion happens, and when it should not be used.
  5. Monitor cost vs complexity: The engineering overhead of supporting a new format might outweigh marginal savings—be ruthless with ROI.

6. Real-world architecture view

Imagine your SaaS product with a microservice that fetches events, user metadata, product logs, etc., then sends a chunk to an LLM for analysis. Without TOON: you send JSON, pay tokens. With TOON: you send a compact representation, reduce tokens, faster turnaround. You still store and process JSON internally, but transform to TOON at “LLM prompt generation”. That keeps your system clean and your token cost optimized.

7. Verdict

  • JSON remains your universal, interoperable standard. It’s proven, everywhere.
  • But if you’re in the AI/LLM space and token efficiency matters, TOON is worth trying.
  • Don’t rush to adopt it everywhere. Use it where it adds measurable value.
  • Make sure your team understands it, your tooling supports it, and you don’t trade readability or reliability for compactness without justification.