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TokenPig VS s3-lambda

Compare TokenPig VS s3-lambda and see what are their differences

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Contents:
  1. » Base Details
  2. » Reviews
  3. » Alternatives

TokenPig logo TokenPig

Upload a document and turn it into clean, token-efficient Markdown for ChatGPT, Claude, Gemini, Cursor and RAG workflows.

s3-lambda logo s3-lambda

Lambda functions over S3 objects: each, map, reduce, filter
  • TokenPig
    Image date //
    2026-08-06
  • TokenPig
    Image date //
    2026-08-06
  • TokenPig
    Image date //
    2026-08-06

TokenPig converts documents into clean, structured Markdown built specifically for LLM and RAG workflows — ChatGPT, Claude, Gemini, and retrieval pipelines.

The problem

Raw PDF, Word, PowerPoint and Excel exports carry a lot of formatting noise — repeated headers, broken tables, inconsistent whitespace — that eats into an LLM's context window without adding useful information. Cleaning that up manually is tedious, and general-purpose converters weren't built with token efficiency in mind.

What TokenPig does

  • Converts PDF, DOCX, PPTX, XLSX, HTML, CSV, JSON, XML, TXT and MD into clean Markdown
  • Estimates how many tokens each conversion saves versus the raw document
  • Runs entirely in the browser — no installation, no code, no configuration
  • Offers three output modes (Clean, Compact, Max Savings) depending on how aggressively you want to strip formatting

Plans

  • Free — try it with no signup
  • Personal — for individuals doing regular one-off conversions
  • Pro / Enterprise — batch processing, ZIP export, and a conversion API for teams automating document ingestion into RAG pipelines

Who it's for

Researchers, consultants, and students who regularly paste documents into ChatGPT or Claude and want cleaner, cheaper context — plus developers and teams building retrieval-augmented generation pipelines who need reliable document-to-Markdown conversion, with an API for automation.

Compared to open-source libraries like MarkItDown or Docling, TokenPig is built for people who want a ready-to-use tool with no setup, along with visibility into token savings — while still offering an API for teams that want to integrate it into their own pipeline.

  • s3-lambda Landing page
    Landing page //
    2022-11-04

TokenPig features and specs

  • Supported Formats
    PDF, DOCX, PPTX, XLSX, HTML, CSV, JSON, XML, TXT, MD
  • Token savings estimate
    Shows tokens saved vs. raw document for each conversion
  • Batch processing & API
    Pro/Enterprise plans include batch conversion, ZIP export and a conversion API

s3-lambda features and specs

  • Batch processing of S3 objects
    s3-lambda provides a straightforward way to perform batch operations on large numbers of S3 objects, enabling map, filter, and reduce-style processing over entire S3 buckets or prefixes without writing boilerplate code.
  • Familiar functional API
    The library uses a functional programming paradigm with operations like map, filter, and reduce, making it intuitive for JavaScript developers to process S3 objects using patterns they already know.
  • Built-in concurrency control
    s3-lambda handles parallel processing of S3 objects with configurable concurrency, allowing users to control how many operations run simultaneously and avoid overwhelming AWS resources or hitting rate limits.
  • Context-aware operations
    The library provides a context object within each operation that includes useful metadata about the current object being processed, simplifying access to S3 object properties during transformations.
  • Easy integration with Lambda
    Designed to work seamlessly within AWS Lambda functions, making it straightforward to set up event-driven, serverless pipelines for processing large volumes of S3 data without managing infrastructure.

Possible disadvantages of s3-lambda

  • Unmaintained project
    The repository appears to be no longer actively maintained, with limited recent commits and unresolved issues, which raises concerns about long-term reliability, security patches, and compatibility with newer AWS SDK versions.
  • Limited documentation
    The project's documentation is relatively sparse, lacking comprehensive examples, edge case handling guidance, and detailed API references, which can make it challenging for new users to adopt effectively.
  • AWS SDK version dependency
    The library depends on an older version of the AWS SDK for JavaScript, which may conflict with projects using the newer AWS SDK v3 and could miss out on performance improvements and features in updated SDKs.
  • Limited error handling flexibility
    The built-in error handling mechanisms are relatively basic, and handling partial failures or implementing sophisticated retry logic for individual object operations requires additional custom code from the developer.
  • Narrow scope of functionality
    The library is tightly focused on S3 object processing and does not integrate with other AWS services or provide utilities beyond basic map/filter/reduce operations, limiting its usefulness in more complex data pipeline scenarios.

Analysis of s3-lambda

Overall verdict

  • s3-lambda is a useful Node.js library for performing operations like map, reduce, and filter directly on S3 objects using Lambda, making it good for developers who need efficient, serverless-based batch processing of S3 data without managing infrastructure. It is well suited for smaller to medium projects but may not be actively maintained for enterprise-scale needs.

Why this product is good

  • Simplifies common S3 batch operations (map, filter, reduce) with a clean, functional API
  • Leverages AWS Lambda for scalable, serverless parallel processing of S3 objects
  • Reduces boilerplate code for iterating over and transforming large numbers of S3 objects
  • Open-source and free to use, allowing customization for specific workflows
  • Integrates well with existing AWS infrastructure and Node.js applications

Recommended for

  • Developers building serverless data pipelines on AWS
  • Teams needing to process or transform large sets of S3 objects without provisioning servers
  • Node.js developers looking for a functional programming approach to S3 operations
  • Projects with batch processing needs that fit within Lambda's execution limits
  • Prototyping or small-to-medium scale ETL tasks involving S3 data

Category Popularity

0-100% (relative to TokenPig and s3-lambda)

TokenPig

s3-lambda

Markdown Converter
100 100%
Markdown Converter
0% 0
Relational Databases
0 0%
Relational Databases
100% 100
Document Management
100 100%
Document Management
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

Questions & Answers

As answered by people managing TokenPig and s3-lambda.

What's the story behind your product?

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.

How would you describe the primary audience of your product?

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.

What makes your product unique?

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.

Why should a person choose your product over its competitors?

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.

Who are some of the biggest customers of your product?

TokenPig's answer

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

Which are the primary technologies used for building your product?

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.

User comments

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What are some alternatives?

When comparing TokenPig and s3-lambda, you can also consider the following products

Markitdown Online - Markitdown Online - Convert DOCX, PDF, PPT to Markdown for Your AI

Doc2Markdown - Convert PDF, Word, PowerPoint, Excel and more to clean Markdown

Docling - Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.

MarkItDownai.org - Use MarkItDown Online to convert PDF, Word, PowerPoint, Excel, HTML, CSV, JSON, and XML into clean Markdown locally in your browser.

Markdown.free - Convert Markdown to PDF, Word, EPUB, HTML and TXT in your browser — no signup, no watermark, files never stored

MarkItDown - The MarkItDown library is a utility tool for converting various files to Markdown (e.g., for indexing, text analysis, etc.).

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