Image and video decision

Immopix

A competent engineer can build a narrower self-hosted replacement for core editing via open-source models and the documented API flow, but matching Immopix's product polish, proprietary model quality, EU compliance assurances, integrations and support would be costly and time-consuming.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off68 h to build

$300/mo6 h/mo upkeep

No published price to break even against.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Upload photo -> enqueue edit job to local model pipeline -> run inpainting/adjustment models -> store result and revisions -> return result URL and notify caller

What it still won’t have

  • Proprietary trained model quality and fine-tuning used by Immopix
  • Polish of the web product, Chrome extension and marketplace integrations
  • Established EU-hosting, documented GDPR/AI-Act compliance statements
  • Personal support and operational SLA

What remains hard

  • Compliance and regulationImmopix ist für deutsche Makler gebaut: datenschutzkonform, rechtssicher und ehrlich zu Ihren Interessenten.
  • Compliance and regulationEU AI Act? Schon erledigt.
Read the build prompt

First-year cost

No published price

Immopix 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
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

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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 self-hosted Immopix-like service using: FastAPI (Python) for the API; PostgreSQL for metadata; Redis for job queue; S3-compatible storage (MinIO or AWS S3); and a GPU worker using Hugging Face Diffusers + an inpainting model and open-source enhancement models for exposure/denoising. Core features in scope: token-based API to upload images and create edits, background worker to run inpainting/sky-replace/exposure pipelines, job status endpoint and HMAC-signed webhooks, signed short-lived result URLs, a simple React web UI for drag-drop uploads and previewing revisions, and a credit-pack purchase mock (no payment gateway required for MVP). Out of scope: building or fine-tuning proprietary models, Chrome extension, full EU legal audit, and multi-style virtual staging assets. Include error handling, idempotent job creation, rate-limiting, automated tests for API and worker, and Docker-based deployment manifests for local GPU and for a single-node cloud deployment.
How we checked2 sources · 3/3 runs agreed · evidence score 54

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score54

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 · 2

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! 2 moats quoted from the page