TOML vs TONL: Feature Comparison for Modern AI Applications

TOML shines as a configuration format with its human-friendly syntax. TONL takes a different approach: token efficiency with enterprise features like query APIs, schema validation, and streaming. Let's compare these formats for modern AI-powered applications.

The Contenders

Query Capabilities

TOML has no built-in query language. TONL includes powerful querying out of the box.

TOML Data Access (manual traversal):

import toml

# Parse TOML
config = toml.load('config.toml')

# Manual traversal
users = config['users']
admins = [u for u in users if u['role'] == 'admin']
avg_age = sum(u['age'] for u in users) / len(users)

TONL Query API (built-in):

import { parse } from 'tonl';

const doc = parse(tonlString);

// JSONPath-like queries
const admins = doc.query('users[?(@.role == "admin")]');

// Built-in aggregation
const avgAge = doc.avg('users[*]', 'age');
const totalSales = doc.sum('orders[*]', 'amount');
const byRole = doc.groupBy('users[*]', 'role');

// Fuzzy search
import { fuzzySearch } from 'tonl/query';
const matches = fuzzySearch('Jon', doc.query('users[*].name'));

Schema Validation

TOML relies on external tools for validation. TONL has TSL built in.

TOML Validation (external library):

# Requires external tools like:
# - taplo (Rust-based TOML toolkit)
# - toml-validator
# - Custom validation code

# No standard schema format exists

TONL Schema (TSL - native):

@schema v1
@strict true

Server: obj
  host: str required
  port: u32 required min:1 max:65535

Database: obj
  connection: str required pattern:^postgres://
  pool_size: u32 default:10 min:1 max:100

Config: obj
  server: Server required
  database: Database required
  debug: bool default:false

Syntax Comparison

TOML (182 bytes, ~90 tokens):

[[users]]
id = 1
name = "Alice"
role = "admin"
age = 32

[[users]]
id = 2
name = "Bob"
role = "user"
age = 28

TONL (110 bytes, ~49 tokens - 46% reduction):

#version 1.0
users[2]{id:u32,name:str,role:str,age:u32}:
  1, Alice, admin, 32
  2, Bob, user, 28

Streaming Comparison

Optimization Strategies

TONL includes built-in optimization that TOML lacks:

Performance Benchmarks

Indexing Comparison

TONL supports indexes for fast lookups. TOML has no indexing capability.

// TONL indexing
const doc = parse(tonlString, {
  indexes: {
    byId: { type: 'hash', path: 'users[*].id' },
    byAge: { type: 'btree', path: 'users[*].age' }
  }
});

// O(1) lookup
const user = doc.getByIndex('byId', 123);

// Range query
const adults = doc.rangeByIndex('byAge', 18, 65);

When to Use Which?

Stick with TOML if:

• You're writing standard config files (pyproject.toml, Cargo.toml).
• Native datetime support is essential.
• Section headers improve your workflow.
• You're in a TOML-centric ecosystem (Rust, Python packaging).

Switch to TONL if:

• You need query capabilities without external tools.
• Schema validation is required.
• You're processing large datasets with streaming.
• Token efficiency matters for LLM applications.
• You want indexing for fast lookups.

Final Verdict

TOML remains the standard for application configuration with its clean section headers and datetime support. For data-intensive LLM applications requiring queries, validation, and streaming, TONL provides enterprise-grade capabilities with 46% token savings.

For simpler token optimization without advanced features, see our TOML vs TOON comparison. Learn more about TONL features or explore API cost optimization strategies.