mdstill
mdstill vs LlamaParse

A LlamaParse Alternative With Zero Setup -- Markdown in the Browser, Not a Pipeline

mdstill wins when you want clean Markdown right now without writing code: it returns token-aware GitHub-Flavored Markdown in the browser or one simple API call, free and with no signup -- ideal for one-off conversions, moderate volume, and prototyping a RAG pipeline before you commit to infrastructure. LlamaParse wins once that pipeline is load-bearing: it is a usage-priced API built to be wired into a custom, high-volume ingestion workflow where you control parsing, chunking and metadata at scale. Many teams use both -- mdstill while prototyping and for ad-hoc files, LlamaParse when the pipeline graduates to production.

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mdstill vs LlamaParse, side by side

AspectmdstillLlamaParse
Primary purposeDocuments → Markdown, instantly, for LLMs and RAGDocument-parsing API for custom RAG ingestion at scale
SetupNone -- drop a file in the browser, or one API callAPI key + integration code + a Python pipeline
OutputToken-aware GFM, ready to paste into a prompt or storeParsed elements you assemble inside your own pipeline
CostFree tier, no signup; Pro for higher limitsUsage-priced; requires an account and API key
Best forOne-off conversions, moderate volume, prototypingHigh-volume, custom ingestion you build and control
Tables & headingsPreserved as GFM pipe tables + H1–H6 hierarchyStrong on complex layouts; you wire structure into chunks
PrivacyVolatile memory, deleted immediately, no retention or trainingProcessed on their servers under their own policy
Learning curveNone -- no code to writeYou write the integration code

Build vs buy is the real question

This is not a feature shootout; it is a build-vs-buy decision. LlamaParse is a serious parsing API -- it handles complex layouts well and slots natively into a LlamaIndex pipeline -- but you adopt it by writing code: an API key, an integration, and a Python environment to run it in. mdstill is the no-build option: you get token-aware GitHub-Flavored Markdown from a browser drop or a single multipart request, with nothing to wire up. If your goal today is a clean .md file rather than a production ingestion system, the no-build path is usually the one you actually want.

Where each one earns its place

Reach for mdstill when the job is bounded: a handful of documents, a moderate batch, or a prototype where you want to see Markdown quality before committing to infrastructure. Reach for LlamaParse when ingestion is the product -- thousands of documents flowing continuously, where fine-grained control over parsing, chunking and metadata extraction at scale pays for the integration work. These are different shapes of problem, and the honest answer is that plenty of teams run both: mdstill for ad-hoc and prototype work, LlamaParse once the pipeline is load-bearing.

Privacy is a real difference, not a footnote

A hosted parsing API uploads your documents to its servers and processes them there; what happens after is governed by its retention and privacy policy. mdstill processes files in volatile memory and deletes them immediately after the conversion returns -- no logging of document content, no retention, no use for training. If the documents you convert are sensitive and you only need Markdown, immediate deletion is a stronger default than a pipeline you would still have to harden yourself.

When you do want automation

mdstill is not only a browser tool -- it has a developer-first API that returns Markdown from a single multipart request, so a short curl-and-jq script can turn a directory of PDFs or Word files into clean .md. That is enough for moderate, scheduled, or prototype workloads without standing up a Python ingestion service. When volume and custom control outgrow that, LlamaParse is the natural next step rather than a replacement -- see the API docs for a ready-to-paste example.

When to use LlamaParse instead

No single tool wins every job. Reach for LlamaParse when:

Frequently asked questions

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