TokenPig VS WTM API

TokenPig's answer

TokenPig focuses specifically on token efficiency, not just format conversion. Alongside clean Markdown output, it shows an estimated token savings for every conversion, so users can see exactly how much context window they're recovering before pasting a document into ChatGPT, Claude or Gemini — something general-purpose converters don't surface.

WTM API's answer:

WTM API focuses on structure preservation, not just text extraction. Unlike most scrapers that return raw text, it converts HTML to proper Markdown with headings, tables, code blocks, and links intact. No headless browser needed — pure server-side parsing means sub-second responses at a fraction of the cost.

TokenPig's answer

TokenPig runs entirely in the browser — no Python setup, no libraries to install, no code to maintain. That makes it accessible to non-developers (consultants, researchers, students) while still offering batch processing and an API for teams that want to automate document ingestion at scale.

WTM API's answer:

Speed and simplicity. One POST call, any URL, clean Markdown back in under 1 second. No SDKs to install, no headless browser to maintain, no complex configuration. Generous free tier (50 calls/month) and straightforward pricing. The output is immediately usable for LLMs, RAG pipelines, or content migration.

TokenPig's answer

Two main groups: individuals who regularly feed documents into ChatGPT or Claude and want cleaner, cheaper context (researchers, consultants, students), and developers/teams building RAG pipelines who need reliable document-to-Markdown conversion via API.

WTM API's answer:

Developers building AI/LLM applications, RAG pipelines, content migration tools, or any system that needs web content in a structured, portable format. Also useful for teams moving content to Markdown-based platforms like Notion, Obsidian, or static site generators.

TokenPig's answer

TokenPig started from a recurring frustration: pasting PDF or Word exports into an LLM and watching layout noise — repeated headers, broken tables, stray whitespace — burn through the context window before the actual content even got read. TokenPig was built to solve that specific problem: clean, structured Markdown output plus visibility into the tokens saved.

WTM API's answer:

I was building a RAG pipeline and needed web content in Markdown format. Existing tools gave me either raw text (losing all structure) or required a headless browser (slow and expensive). I built WTM API to solve my own problem - a fast, simple API that preserves the structure of any webpage as clean Markdown. Shipped the MVP in a weekend using Next.js, Supabase, Stripe, and Vercel, all on free tiers.

TokenPig's answer

Built as a modern web application using Next.js and TypeScript, with a focus on fast, reliable document processing entirely server-side — no client installation required.

WTM API's answer:

Next.js 16 (App Router), Supabase (auth and PostgreSQL), Stripe (subscription billing), Vercel (hosting), and Cheerio (server-side HTML parsing). The entire stack runs on free tiers with zero monthly infrastructure cost.

TokenPig's answer

• Independent consultants and researchers preparing documents for LLM workflows
• Development teams building RAG pipelines

WTM API's answer:

• Early-stage AI startups building RAG pipelines
• Independent developers working on content tools
  
• Currently in public beta and growing the user base