* * * LLAMAPARSE ALTERNATIVE * * *
A LlamaParse Alternative.
LlamaParse is a strong document parser with deep LlamaIndex integration. If you're outside that ecosystem — or tired of converting 'credits' into dollars — PennyOCR does the core job, documents to clean markdown with tables, at a flat $0.00075 per page with 100 free monthly.
START FREE — 100 PAGES
$0.75 / 1,000 PAGES
1,333 PAGES PER DOLLAR · NO CREDIT CARD
01 / TRY IT
ONE ENDPOINT.
POST a file, get JSON back — the extracted text, per page and joined. PDF, PNG, JPEG, WebP or TIFF.
curl https://api.pennyocr.com/v1/ocr \
-H "Authorization: Bearer $PENNYOCR_API_KEY" \
-F "file=@report.pdf"
# $0.75 per 1,000 pages, first 100 free02 / USE CASES
HOW WE COMPARE.
PRICING MODEL
Flat per-page price you can compute in your head vs credit packs with per-mode multipliers. Our /v1/estimate even quotes a URL's exact cost for free.
OUTPUT
Both produce LLM-ready markdown with tables. We add a plain-text mode and per-page results for citations.
PRIVACY
Zero retention by default — documents processed and discarded, storage strictly opt-in.
WHEN LLAMAPARSE WINS
Deep LlamaIndex pipelines, their premium parsing modes, or vendor consolidation if you're already paying LlamaCloud.
03 / PRICE CHECK
HALF THE PRICE OF THE BIG CLOUDS.
Per 1,000 pages, public list prices, first tier.
PENNYOCR$0.75
AWS TEXTRACT$1.50
GOOGLE CLOUD VISION$1.50
AZURE DOC INTELLIGENCE$1.50
YOU KEEP50%
DOES PENNYOCR INTEGRATE WITH LLAMAINDEX?
Yes — a ten-line reader (shown at pennyocr.com/llamaindex) turns our page_results into Documents with page metadata. An official reader package is on the roadmap.
HOW DO PRICES ACTUALLY COMPARE?
Check LlamaParse's current credit rates for your parsing mode and divide into dollars per 1,000 pages; ours is a constant $0.75. For most standard-mode workloads we come out meaningfully cheaper — verify with your own documents.
WHAT ABOUT COMPLEX LAYOUTS?
Tables, multi-column and skewed scans are exactly what the VLM handles. Run both on your hardest PDF — our free tier exists for that comparison.
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