Stay free if you only need full conversion pipeline and gpu, cpu, and mps support. Upgrade if you need 99.99% uptime sla and ~15s for 250-page pdfs. Most solo builders can start free.
Why it matters: GPL license and model weight restrictions require commercial licensing for companies above $2M revenue
Available from: Managed API
Why it matters: GPU strongly recommended for batch processing — CPU-only deployment is impractical for production workloads
Available from: Managed API
Why it matters: No built-in REST API in the open-source version — requires wrapping in a web framework or using the managed API
Available from: Managed API
Why it matters: Get help when stuck. Can save hours of troubleshooting on critical projects.
Available from: Managed API
Why it matters: Advanced feature not available in free plan.
Available from: Managed API
Marker benchmarks favorably against both. Compared to LlamaParse, Marker is faster and open-source. Compared to Docling, Marker focuses on markdown/JSON output quality while Docling provides richer structured output with bounding boxes. Marker's LLM-enhanced mode often produces the highest overall accuracy.
Not technically — it runs on CPU and Apple MPS — but practically, yes for any batch workload. CPU processing is 4-10x slower. GPU processing achieves roughly 25 pages/second on H100 hardware in batch mode. For a handful of documents, CPU works fine.
Yes, through integrated Surya OCR supporting 90+ languages. Scanned documents at 300+ DPI produce good results. Use the --force_ocr flag to ensure all content goes through OCR. Lower-quality scans will have reduced accuracy.
The --use_llm flag pairs Marker's layout detection with an LLM (Gemini Flash by default) for post-processing. It improves table formatting, handles inline math, merges split tables, and extracts form values. Use it when document accuracy matters more than processing speed — it adds cost and latency but produces measurably better output.
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Last verified March 2026