> Source: https://txtfetch.com/compare/libraries/marker > Plain-text twin — every page on txtfetch.com has one. https://txtfetch.com/text --- # txtfetch vs Marker Marker is an open-source library you run yourself. txtfetch is one HTTP call. Here's an honest look at the licence, the operational cost, and where each one wins. ## What Marker is Marker is built by Datalab. A layout- and VLM-driven converter with an optional LLM pass for the highest-fidelity tables and math. Marker's code and its weights carry two different licences. The code is plain Apache-2.0. The model weights use a modified Open RAIL-M licence. That licence is free for research, personal use, and startups under $5M in combined funding or revenue. Above that line, you need a paid commercial licence. Marker's terms have changed more than once, so re-check both links before you rely on this split. Licence checked 2026-09. See [the project's own LICENSE file](https://github.com/datalab-to/marker/blob/master/LICENSE). Checked against the project's own LICENSE file, not a summary of it. A licence can change between releases, so check the LICENSE of the exact version you pin, not this page. This is a factual summary, not legal advice. at a glance Marker **Code licence**: Apache-2.0 **Model-weights licence**: Modified AI Pubs Open RAIL-M, free for research, personal use, and startups under $5M in funding or revenue **Install**: pip install marker-pdf. Model weights download automatically on first run. **Model weights**: A layout-detection model, plus a heavier vision-language model for balanced and highest-fidelity modes. Both download on first use. **GPU**: Runs on GPU, CPU, or Apple MPS, per Marker's own README. GPU is the realistic choice for throughput once you enable the VLM or --use_llm paths. **Formats**: PDF, images, PPTX, DOCX, XLSX, HTML, and EPUB, in any language, per the project's README. **Output**: Markdown, JSON, HTML, and a chunked output mode. **OCR**: A bundled layout-and-OCR model stack: a lightweight detector for the fast, CPU-oriented mode, and a heavier vision-language pipeline for the balanced and highest-fidelity modes. An optional --use_llm flag routes a page through an external LLM (Gemini, Claude, OpenAI-compatible, and others) for extra table, math, and form fidelity. txtfetch **Code licence**: N/A — hosted API, no code to license **Model-weights licence**: N/A — no weights on your infrastructure **Install**: None. One HTTP call. **Model weights**: None on your side. Tika and Tesseract run on ours. **GPU**: None needed. quality=premium routes to a VLM on our infrastructure. **Formats**: Every format on the coverage list, one endpoint. **Output**: Text, Markdown, or element JSON. **OCR**: Standard tier: eng, osd. quality=premium for a hard scan. See /languages. capability table | Capability | Marker | txtfetch | | --- | --- | --- | | Hosting | self-hosted only | hosted API* | | Ships neural model weights | yes | no | | GPU required | optional | no* | | Table extraction | yes | yes | | OCR engine bundled | yes | yes* | | Structured or JSON output | yes | yes* | | Built-in chunking for RAG | yes | no* | | Single HTTP endpoint | partial | yes | \* txtfetch notes: One HTTP call. No install, no model download, no server to run and patch. The standard tier runs on Tika and Tesseract, CPU only. quality=premium routes a document to a vision-language model on our infrastructure, not yours. The standard OCR tier ships English trained data plus script detection only (TESSDATA\_LANGS=eng osd). See /languages for the per-language detail and quality=premium for a hard scan. ?format=markdown and ?format=json ship today. Schema-defined field extraction, pulling named fields to your own schema, is on the roadmap, not shipped yet. txtfetch returns text, Markdown, or element JSON. Chunking stays a caller-side step, the same reasoning behind every guide on /fixes. No accuracy or speed figure is measured against Marker on this page. See [/benchmarks](https://txtfetch.com/benchmarks) for txtfetch's own measured numbers and their caveats. where marker wins - Strong table, equation, and form fidelity, especially with the optional --use\_llm hybrid pass enabled. - Automatic removal of running headers, footers, and page-layout artifacts before they reach the output. - Three run tiers (fast, balanced, and the --use\_llm hybrid pass) that let you trade speed for fidelity per document, instead of one fixed setting. - Works entirely offline once its model weights are downloaded, with no required outbound call unless you turn on --use\_llm. the work you still own Marker is free to run. Running it in production is a separate job. This is the checklist that job includes. - Downloading, caching, and updating Marker's layout and vision-language model weights, and provisioning a GPU if your volume needs the throughput. - Reviewing the Open RAIL-M weights licence against your own funding and revenue, since it is not the same permissive terms as the Apache-2.0 code. - Configuring and paying for an external LLM provider for the --use\_llm hybrid accuracy pass. That call goes to a service you connect, not one Marker bundles. - Patching Marker itself and its model stack as new versions and CVEs land, and re-checking the licence split each time you upgrade. where txtfetch wins - One HTTP call. No install, no model download, no GPU to provision, and no server to patch. - The standard OCR tier ships English trained data plus script detection only (`TESSDATA_LANGS=eng osd`). See [/languages](https://txtfetch.com/languages) for the per-language detail, and request `quality=premium` for a hard non-English scan. - A durable free tier (500 documents/month, ongoing), so trying it costs nothing and no card. txtfetch strips HTML markup, not boilerplate, the same as /fixes describes it. Nav, footer, and cookie-banner text still comes back as ordinary text. Deciding what's chrome versus article stays a caller-side step. We also don't ship schema-defined field extraction yet. See the capability table above for the full list. the same job, one call This is the whole integration: no install, no model download, no GPU. ``` curl -s -X POST "https://api.txtfetch.com/v1/extract?format=markdown" \ -H "Authorization: Bearer $TXTFETCH_KEY" \ -F file=@document.pdf ``` frequently asked questions **Is Marker free to use commercially?**: The code is Apache-2.0, free for any use including commercial. The model weights are different: a modified Open RAIL-M licence, free for research, personal use, and startups under $5M in combined funding or revenue. Above that, Datalab sells a separate commercial licence for the weights. **Does Marker need a GPU?**: No, per its own README it runs on GPU, CPU, or Apple MPS. A GPU is the realistic choice once you enable the vision-language or --use_llm paths, where CPU-only throughput drops sharply. **What does --use_llm actually add?**: It routes a page through an external LLM you configure: Gemini, Claude, an OpenAI-compatible endpoint, or others. That pass improves table, inline-math, and form extraction beyond Marker's bundled models alone. sources - [Marker: LICENSE (Apache-2.0, code)](https://github.com/datalab-to/marker/blob/master/LICENSE) Accessed 2026-09 - [Marker: README (commercial usage, formats, GPU/CPU/MPS)](https://github.com/datalab-to/marker) Accessed 2026-09 - [Datalab: pricing (model-weights commercial licence)](https://www.datalab.to/pricing) Accessed 2026-09 - [marker-pdf on PyPI](https://pypi.org/project/marker-pdf/) Accessed 2026-09 Checked against the project's own LICENSE file, not a summary of it. A licence can change between releases, so check the LICENSE of the exact version you pin, not this page. This is a factual summary, not legal advice. other open-source parsers - [txtfetch vs Docling →](https://txtfetch.com/compare/libraries/docling) - [txtfetch vs MarkItDown →](https://txtfetch.com/compare/libraries/markitdown) - [txtfetch vs PyMuPDF4LLM →](https://txtfetch.com/compare/libraries/pymupdf4llm) - [txtfetch vs MinerU →](https://txtfetch.com/compare/libraries/mineru) - [All open-source parsers →](https://txtfetch.com/compare/libraries) ## Keep the library. Add the hard formats. Some teams run both. Send only the files Marker cannot read. [Read the quickstart →](https://txtfetch.com/docs) [Compare self-hosted Tika →](https://txtfetch.com/compare/self-hosted-tika)