YAML vs TONL: Complete Feature Comparison for AI Development

YAML is beloved for DevOps configuration, but lacks the advanced data capabilities modern AI applications demand. TONL combines token efficiency with powerful query APIs, schema validation, and streaming. Let's compare these formats for enterprise LLM development.

The Contenders

Query Capabilities

YAML has no query language. TONL provides comprehensive querying out of the box.

YAML Data Access (manual or external tools):

import yaml

# Parse YAML
with open('data.yaml') as f:
    data = yaml.safe_load(f)

# Manual traversal
users = data['users']
admins = [u for u in users if u['role'] == 'admin']

# Or use external tools like yq
# yq '.users[] | select(.role == "admin")' data.yaml

TONL Query API (built-in):

import { parse } from 'tonl';

const doc = parse(tonlString);

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

// Aggregation - built-in
const avgAge = doc.avg('users[*]', 'age');
const totalRevenue = doc.sum('orders[*]', 'amount');
const byDepartment = doc.groupBy('employees[*]', 'dept');

// Advanced features
import { fuzzySearch, soundsLike } from 'tonl/query';
fuzzySearch('Jon', doc.query('users[*].name'));
soundsLike('Smith', 'Smyth'); // Phonetic matching

import { parseTemporalLiteral } from 'tonl/query';
parseTemporalLiteral('@now-7d'); // Temporal queries

Schema Validation

YAML relies on JSON Schema or external tools. TONL has TSL built in.

{
  "$schema": "...",
  "type": "object",
  "properties": {
    "users": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "integer"
          },
          "email": {
            "type": "string",
            "format": "email"
          }
        },
        "required": ["id"]
      }
    }
  }
}

@schema v1
@strict true

User: obj
  id: u32 required
  email: str required
    pattern:email
    lowercase:true
  age: u32? min:13 max:150
  roles: list<str>
    required min:1
    unique:true

users: list<User> required

TSL is more concise and includes 13 built-in constraints like pattern, min, max, unique, and lowercase.

Syntax Comparison

YAML (156 bytes, ~80 tokens):

users:
  - id: 1
    name: Alice
    role: admin
  - id: 2
    name: Bob
    role: user

TONL (105 bytes, ~49 tokens - 39% reduction):

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

Streaming Comparison

Indexing Capabilities

YAML has no indexing. TONL supports multiple index types:

const doc = parse(tonlString, {
  indexes: {
    // O(1) exact lookups
    byId: { type: 'hash', path: 'users[*].id' },

    // O(log n) range queries
    byAge: { type: 'btree', path: 'users[*].age' },

    // Multi-field lookups
    byRoleAndDept: {
      type: 'compound',
      paths: ['users[*].role', 'users[*].department']
    }
  }
});

// Fast indexed operations
const user = doc.getByIndex('byId', 12345);
const middleAged = doc.rangeByIndex('byAge', 30, 50);

Optimization Strategies

TONL includes optimization that YAML cannot match:

Performance Benchmarks

When to Use Which?

Stick with YAML if:

• You're managing Kubernetes, Docker, or CI/CD configurations.
• Comments are essential for documentation.
• You need anchors and aliases for DRY configuration.
• Multi-document support is required.
• Human editing is the primary use case.

Switch to TONL if:

• You need query capabilities without external tools.
• Schema validation is a requirement.
• You're processing large datasets with streaming.
• Token efficiency matters for LLM costs.
• Fast indexed lookups are needed.
• You want advanced optimization strategies.

Migration Path

import yaml from 'js-yaml';
import { encodeTONL } from 'tonl';

// Convert YAML to TONL
const yamlData = yaml.load(yamlString);
const tonlString = encodeTONL(yamlData);

// With optimization
const optimizedTonl = encodeTONL(yamlData, {
  optimize: true,
  strategies: ['dictionary', 'delta']
});

Final Verdict

YAML remains the standard for DevOps configuration where human editing, comments, and anchors matter. For data-intensive LLM applications requiring queries, validation, streaming, and optimization, TONL provides enterprise-grade capabilities with 39% token savings.

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