Insights on data formats, LLM optimization, and reducing API costs. Learn about TOON and TONL formats, or try our free converter.
TONL's optional compression layers push token savings toward 60%. A tour of Dictionary, Delta, RLE, Bit Packing, Column Reorder, and Quantizer—and when each one pays off.
TOON supports comma, tab, and pipe delimiters. Learn how each affects tokenization, quoting, and readability—and how to pick the right one for your data and model.
Token savings depend on the tokenizer. See how TOON's reductions hold across o200k_base, Claude, Gemini, and Llama vocabularies—and why you should count tokens on your own model.
Fine-tuning files stay JSONL, but the data inside each example doesn't have to be JSON. Learn how TOON-formatted content trims training tokens and teaches models to read tables.
In multi-agent systems every message is re-tokenized at each hop, so format overhead compounds. Learn why TOON makes a strong inter-agent wire format and where to keep JSON.
Conversation history grows every turn and you pay for it every request. Learn how TOON-encoding message arrays plus prompt caching keeps long chats inside budget.
Application logs are huge, uniform, and repetitive—exactly where JSON wastes the most tokens. Learn how TOON and TONL streaming make log triage with LLMs affordable.
How to chunk, retrieve, and format TOON context so a RAG pipeline keeps 99.6% field-retrieval accuracy while fitting far more evidence into the same context window.
Got rows of spreadsheet data to analyze with AI? Learn when to send it as CSV, TOON, or TONL, and how to convert Excel and Sheets exports into token-efficient prompts.
A SQL result set is a uniform table—TOON's best case. See how converting query rows to TOON cuts up to 58.8% of tokens while keeping field retrieval near-perfect.
The Batch API takes 50% off input and output tokens; TOON removes 40-60% of them first. Here's how to combine async batching and token-efficient formatting for bulk LLM jobs.
Prompt caching cuts the price per token up to 90%; TOON cuts the number of tokens up to 60%. Learn how to combine both levers for compounding savings on Claude and GPT.
Use JSON and constrained decoding for what the model writes; use TOON for what you feed in. A practical guide to splitting input and output formats in tool-calling pipelines.
A plain-English guide to Byte Pair Encoding, tiktoken, and the o200k_base vocabulary—and exactly why repeated JSON braces and quotes cost you tokens that TOON removes.
Parquet and Avro dominate big-data storage, but they're binary and built for disks, not prompts. Learn the store-in-Parquet, serialize-to-TOON pattern for AI pipelines.
MessagePack is about half the size of JSON on the wire—but binary formats Base64-bloat inside LLM prompts. Here's why TOON wins for prompts and MessagePack wins for transport.
Markdown tables look tabular but their pipes and dashes are pure token bloat. See how TOON keeps the table structure LLMs love—worth a 40% accuracy gain—without the alignment tax.
NDJSON (JSON Lines) is the streaming workhorse of data engineering. See how it compares to TOON on token efficiency, structure, and LLM readability.
A one-page TOON reference: objects, lists, tabular arrays, delimiters, quoting, nesting, and edge cases—each with a side-by-side JSON example.
A practical guide to TONL schemas: type hints (u32, str, bool), validation rules, and auto-generating TypeScript types—while staying ~32% smaller than JSON.
TONL ships a SQL-like query API with sub-0.1ms indexed lookups. Learn how to filter, aggregate, and join structured data without spinning up a database.
TONL streams 50GB+ files in under 100MB of memory. Learn how streaming, indexing, and sub-millisecond queries make huge datasets usable in AI pipelines.
How text-first formats like TOON and TONL handle vectors, images, and binary blobs versus bloated Base64-in-JSON, and when to keep data out of the prompt entirely.
Small local models like Llama and Phi have tiny context windows. Learn how TOON's compact tables stretch limited context for on-device and edge AI.
Does saving tokens cost you parsing speed? A look at the trade-offs between JSON.parse, CSV, and TOON encoding/decoding for high-throughput LLM applications.
Tokens are the unit of AI economics. Here's why the format you serialize data in—JSON, TOON, or TONL—quietly determines your LLM bill and context budget.
Install, encode, decode, and validate TOON in Python and JavaScript/TypeScript with copy-paste examples and a JSON-to-TOON migration checklist.
Custom output parsers and document formatting to feed TOON-encoded context into LangChain and LlamaIndex pipelines for materially lower token usage.
How to cut token costs in agent loops and Model Context Protocol servers by passing tool results as TOON instead of JSON, with concrete patterns and caveats.
TOON isn't always the cheapest option. Learn about the 'prompt tax', the data shapes where JSON or CSV win, and a framework for choosing an LLM data format.
A data-driven look at TOON vs JSON across 5,016 LLM calls: 39.9% fewer tokens at 76.4% retrieval accuracy, plus per-model and per-data-shape results.
Why JSON is costing you money and performance in AI applications, and how switching to TOON can reduce token usage by up to 60%.
Explore Warp, Ray, and HTTPie—three niche developer tools that can transform your workflow—and see how TONL provides the reliable data foundation they need.
Discover how Warp, Ray, and HTTPie can supercharge your development cycle, and learn how the TOON format makes sharing tool outputs with AI more efficient.
Explore why LLMs favor agreement over correctness due to reward hacking, and how using TOON in your evaluation pipeline can help detect sycophancy.
Understand the 'sycophancy' problem in LLMs and learn how the TONL data platform provides the ground truth needed to build assertive, reliable AI systems.
Explore the fundamental causes of LLM hallucinations and learn how the TOON format reduces noise to improve accuracy and reasoning in AI applications.
Learn how the TONL data platform provides a reliable, high-fidelity foundation to ground Large Language Models and minimize factually incorrect outputs.
Claude 3.5 and TOON: lower token costs with compact structured data for agentic workflows.
Explore the environmental impact of AI and how processing fewer tokens with efficient data formats like TOON contributes to sustainable coding practices.
A step-by-step tutorial on building a chatbot using Next.js, Vercel AI SDK, and TOON for efficient tool calling and data passing.
TOON best practices for naming, nesting, and troubleshooting strict-mode conversion errors.
A practical guide for developers migrating their data from JSON to TOON. Learn about common pitfalls, regex vs parsers, and validation strategies.
Explore TONL's architecture for schemas, validation, and streaming in an AI-native data platform.
Learn how replacing JSON with TOON in your RAG context chunks can significantly reduce token usage, lower latency, and cut API costs.
Compare Google's Protocol Buffers with TOON. Learn why binary formats struggle with LLMs and how TOON provides a token-optimized alternative.
A deep dive comparing Protobuf's binary serialization with TONL's AI-native data platform. Discover which typed format is right for your architecture.
Learn how to convert between JSON and Protobuf using our new tool. Discover the benefits of Protobuf's schema-driven approach and binary efficiency.
Deep dive into CSV format: RFC 4180 standard, common challenges, quoting rules, type handling, and best practices for data interchange.
Compare YAML vs TONL for LLM applications: advanced features, performance benchmarks, query capabilities, and when to use each format.
Compare YAML vs TOON for LLM prompts: token efficiency, readability, edge cases, and which format saves more on AI API costs.
Compare TOML vs TONL: query API, schema validation, streaming, and advanced optimization for LLM-powered applications.
Compare TOML vs TOON for LLM applications: token efficiency, nested structures, config use cases, and cost savings analysis.
Compare CSV vs TONL for LLM data: advanced features, indexing, nested data support, and enterprise-grade capabilities.
Compare CSV vs TOON for LLM prompts: flat vs structured data, type safety, token efficiency, and when to use each format.
Compare XML vs TONL for LLM applications: query capabilities, streaming, schema validation, and advanced optimization strategies.
Compare XML vs TOON for LLM prompts: token efficiency, verbosity analysis, and how TOON saves up to 69% on AI API costs.
Compare TOON vs TONL for LLMs: key differences, performance benchmarks, and when to use each format to optimize AI API costs.
Discover TONL: production-ready format saving 45% tokens vs JSON. Learn features, queries, and optimization strategies.
Discover the official TOON (Token-Oriented Object Notation) specification, ABNF grammar, data types, and key syntax rules of this modern serialization format.
Compare TOON against JSON, YAML, XML, TOML, and CSV to choose the best format for LLM applications.
Practical guide to reducing OpenAI GPT and Anthropic Claude API costs by 30-60% using TOON format. Includes code examples and implementation strategies.
An in-depth comparison of JSON and TOON data formats for LLM applications, analyzing token efficiency, performance, and when to use each format.
Discover TOON, a compact format that reduces LLM token usage and helps cut AI API costs.