Image and video decision

ON1 Photo RAW

A capable technical user can implement a useful subset (RAW decode, non-destructive edits, basic AI denoise/upscale, and export) but reproducing ON1's full product polish, proprietary AI/restore models, plugin integrations, and cloud/market features would be costly and time-consuming.

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SubscriptionCustom pricing
Initial build80 hours
Monthly upkeep10 hours + $0
Evidence3/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.

What a replacement has to do

  • Import RAW photos → non-destructively adjust develop parameters and layers/masks → apply AI denoise/upscale/effects → export final images

What it still won’t have

  • ON1 proprietary AI/Restore models and any curated training data
  • Polished, production-quality UI and cross-platform installers
  • Integrated plugin-level compatibility with Photoshop/Lightroom as shipped
  • Cloud sync, ON1 Plus content, and official support/training materials

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

ON1 Photo RAW does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal cross-platform desktop RAW photo editor using Electron (UI) + a Rust or Go backend for image processing. In scope: 1) RAW decoding and preview using LibRaw bindings; 2) a local SQLite catalog to store file references and non-destructive edit JSON; 3) a pixel pipeline implementing exposure, white balance, curves, HSL, plus a single layer with mask support; 4) integrate an open-source denoise/upscale model runnable via ONNX/TorchScript (local GPU if available, fallback CPU); 5) batch export to JPEG/TIFF and a simple preset/save/load system; 6) basic automated tests for RAW decode, pipeline parameter changes, catalog read/write, and export. Out of scope: cloud sync, plugin hosting for Photoshop/Lightroom, advanced retouch/spot-heal, marketplace or licensing. Provide error handling for file I/O, model inference failures, and corrupt RAWs; include CI that runs unit tests and a simple end-to-end export regression test.
How we checked4 sources · 3/3 runs agreed · evidence score 64

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 4 cited sources+3
  • 3/3 assessment runs agreed+4
  • Evidence score64

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.

How scoring works →

Cited sources · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded