Marker Pricing & Plans 2026

Name: Marker
Brand: Marker
Price: 4 USD
Availability: InStock

Complete pricing guide for Marker. Compare all plans, analyze costs, and find the perfect tier for your needs.

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Marker is worth it →

🆓Free Tier Available

💎3 Paid Plans

⚡No Setup Fees

Choose Your Plan

Open-source local use

Free for permitted uses

Start Free →

Managed Datalab platform

$4 per 1,000 pages for Fast and Balanced mode; $6 per 1,000 pages for High Accuracy mode, structured extraction, track changes, and spreadsheets; $25 monthly credit included on the managed plan

Start Free Trial →

Batch processing service

Custom pricing

Start Free Trial →

Commercial self-hosting / on-prem

Custom pricing

Start Free Trial →

Pricing sourced from Marker · Last verified March 2026

Feature Comparison

Detailed feature comparison coming soon. Visit Marker's website for complete plan details.

View Full Features →

Is Marker Worth It?

✅ Why Choose Marker

• Supports multiple input types beyond PDF, including images, PPTX, DOCX, XLSX, HTML, and EPUB, which makes it useful for heterogeneous document collections.
• Outputs markdown, HTML, tree-structured JSON, and flattened chunks, giving teams practical formats for human review, downstream parsing, and RAG indexing.
• Optional LLM mode can improve hard cases such as cross-page tables, inline math, table formatting, and form value extraction, instead of relying only on OCR and layout models.
• Developer-friendly architecture exposes converters, processors, renderers, providers, schemas, and block objects, so teams can customize the pipeline rather than treat it as a black box.
• Includes table-only, OCR-only, and beta structured-extraction converters, which lets users run narrower pipelines when full-document conversion is unnecessary.
• Benchmark data in the README reports strong speed and accuracy versus Llamaparse, Mathpix, and Docling, including favorable overall PDF conversion scores and improved table results with --use_llm.

⚠️ Consider This

• Local setup requires Python 3.10+, PyTorch, and model dependencies; non-PDF formats require the fuller marker-pdf[full] installation.
• High-throughput local processing can be resource intensive: the README states Marker may use about 5GB VRAM per worker at peak and 3.5GB on average.
• The built-in FastAPI server is described by the project as simple and intended only for small-scale use, so production API deployments may need the hosted Datalab API or custom infrastructure.
• Known limitations remain for very complex layouts, especially nested tables and forms, and forms may not render well without extra OCR or LLM assistance.
• Commercial use is not a simple permissive open-source story: the code is GPL-3.0 and broader commercial licensing or removing GPL requirements requires paid licensing.

What Users Say About Marker

👍 What Users Love

✓Supports multiple input types beyond PDF, including images, PPTX, DOCX, XLSX, HTML, and EPUB, which makes it useful for heterogeneous document collections.
✓Outputs markdown, HTML, tree-structured JSON, and flattened chunks, giving teams practical formats for human review, downstream parsing, and RAG indexing.
✓Optional LLM mode can improve hard cases such as cross-page tables, inline math, table formatting, and form value extraction, instead of relying only on OCR and layout models.
✓Developer-friendly architecture exposes converters, processors, renderers, providers, schemas, and block objects, so teams can customize the pipeline rather than treat it as a black box.
✓Includes table-only, OCR-only, and beta structured-extraction converters, which lets users run narrower pipelines when full-document conversion is unnecessary.
✓Benchmark data in the README reports strong speed and accuracy versus Llamaparse, Mathpix, and Docling, including favorable overall PDF conversion scores and improved table results with --use_llm.

👎 Common Concerns

⚠Local setup requires Python 3.10+, PyTorch, and model dependencies; non-PDF formats require the fuller marker-pdf[full] installation.
⚠High-throughput local processing can be resource intensive: the README states Marker may use about 5GB VRAM per worker at peak and 3.5GB on average.
⚠The built-in FastAPI server is described by the project as simple and intended only for small-scale use, so production API deployments may need the hosted Datalab API or custom infrastructure.
⚠Known limitations remain for very complex layouts, especially nested tables and forms, and forms may not render well without extra OCR or LLM assistance.
⚠Commercial use is not a simple permissive open-source story: the code is GPL-3.0 and broader commercial licensing or removing GPL requirements requires paid licensing.

Pricing FAQ

What file types can Marker convert?

Marker supports PDF, image, PPTX, DOCX, XLSX, HTML, and EPUB files. The README notes that non-PDF document support requires installing additional dependencies with marker-pdf[full].

What output formats does Marker produce?

Marker can output markdown, HTML, JSON, and chunks. Markdown includes image links, formatted tables, LaTeX equations, fenced code blocks, and footnote superscripts; JSON exposes a tree-like block structure; chunks flatten top-level blocks for easier RAG indexing.

Does Marker use LLMs?

LLM use is optional. With --use_llm, Marker can improve accuracy for cases such as table merging across pages, inline math, table formatting, and extracting values from forms. The README lists Gemini, Google Vertex, Ollama, Claude, OpenAI-compatible endpoints, and Azure OpenAI as supported LLM services.

Can Marker run locally?

Yes. Marker can run locally through CLI commands such as marker_single and marker, through Python APIs, through a Streamlit GUI, or through a lightweight FastAPI server. It can run on GPU, CPU, or Apple MPS, with Torch device detection and override options.

Is Marker free for commercial use?

Not for all commercial situations. The repository states that the code is GPL-3.0 and the model weights use a modified AI Pubs Open Rail-M license that is free for research, personal use, and startups under $2M funding or revenue. Broader commercial licensing or removing GPL requirements requires Datalab’s commercial licensing.

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More about Marker

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Marker Pricing & Plans 2026

Complete pricing guide for Marker. Compare all plans, analyze costs, and find the perfect tier for your needs.

🆓Free Tier Available

💎3 Paid Plans

⚡No Setup Fees

Choose Your Plan

Open-source local use

Free for permitted uses

Start Free →

Managed Datalab platform

$4 per 1,000 pages for Fast and Balanced mode; $6 per 1,000 pages for High Accuracy mode, structured extraction, track changes, and spreadsheets; $25 monthly credit included on the managed plan

Start Free Trial →

Batch processing service

Custom pricing

Start Free Trial →

Commercial self-hosting / on-prem

Custom pricing

Start Free Trial →

Pricing sourced from Marker · Last verified March 2026

Is Marker Worth It?

✅ Why Choose Marker

• Supports multiple input types beyond PDF, including images, PPTX, DOCX, XLSX, HTML, and EPUB, which makes it useful for heterogeneous document collections.
• Outputs markdown, HTML, tree-structured JSON, and flattened chunks, giving teams practical formats for human review, downstream parsing, and RAG indexing.
• Optional LLM mode can improve hard cases such as cross-page tables, inline math, table formatting, and form value extraction, instead of relying only on OCR and layout models.
• Developer-friendly architecture exposes converters, processors, renderers, providers, schemas, and block objects, so teams can customize the pipeline rather than treat it as a black box.
• Includes table-only, OCR-only, and beta structured-extraction converters, which lets users run narrower pipelines when full-document conversion is unnecessary.
• Benchmark data in the README reports strong speed and accuracy versus Llamaparse, Mathpix, and Docling, including favorable overall PDF conversion scores and improved table results with --use_llm.

⚠️ Consider This

• Local setup requires Python 3.10+, PyTorch, and model dependencies; non-PDF formats require the fuller marker-pdf[full] installation.
• High-throughput local processing can be resource intensive: the README states Marker may use about 5GB VRAM per worker at peak and 3.5GB on average.
• The built-in FastAPI server is described by the project as simple and intended only for small-scale use, so production API deployments may need the hosted Datalab API or custom infrastructure.
• Known limitations remain for very complex layouts, especially nested tables and forms, and forms may not render well without extra OCR or LLM assistance.
• Commercial use is not a simple permissive open-source story: the code is GPL-3.0 and broader commercial licensing or removing GPL requirements requires paid licensing.

What Users Say About Marker

👍 What Users Love

✓Supports multiple input types beyond PDF, including images, PPTX, DOCX, XLSX, HTML, and EPUB, which makes it useful for heterogeneous document collections.
✓Outputs markdown, HTML, tree-structured JSON, and flattened chunks, giving teams practical formats for human review, downstream parsing, and RAG indexing.
✓Optional LLM mode can improve hard cases such as cross-page tables, inline math, table formatting, and form value extraction, instead of relying only on OCR and layout models.
✓Developer-friendly architecture exposes converters, processors, renderers, providers, schemas, and block objects, so teams can customize the pipeline rather than treat it as a black box.
✓Includes table-only, OCR-only, and beta structured-extraction converters, which lets users run narrower pipelines when full-document conversion is unnecessary.
✓Benchmark data in the README reports strong speed and accuracy versus Llamaparse, Mathpix, and Docling, including favorable overall PDF conversion scores and improved table results with --use_llm.

👎 Common Concerns

⚠Local setup requires Python 3.10+, PyTorch, and model dependencies; non-PDF formats require the fuller marker-pdf[full] installation.
⚠High-throughput local processing can be resource intensive: the README states Marker may use about 5GB VRAM per worker at peak and 3.5GB on average.
⚠The built-in FastAPI server is described by the project as simple and intended only for small-scale use, so production API deployments may need the hosted Datalab API or custom infrastructure.
⚠Known limitations remain for very complex layouts, especially nested tables and forms, and forms may not render well without extra OCR or LLM assistance.
⚠Commercial use is not a simple permissive open-source story: the code is GPL-3.0 and broader commercial licensing or removing GPL requirements requires paid licensing.

Pricing FAQ

What file types can Marker convert?

Marker supports PDF, image, PPTX, DOCX, XLSX, HTML, and EPUB files. The README notes that non-PDF document support requires installing additional dependencies with marker-pdf[full].