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Open-weight OCR got so cheap I had to share it

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Open-weight OCR got so cheap I had to share it

This was not supposed to become a product. When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn't one ready for production that I would trust. So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive. It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages. The nearest competitors are either much lower quality (Azure Read) or absurdly expensive (Extend or Reducto). Even Mistral OCR 4 which is really good and pretty cheap is still 4x more expensive. So I had to share it. I made the endpoitns public, and vibe-coded a simple dashboard. Let me know what you think!

Visit product View on Hacker NewsJul 24, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, code · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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