Image and video decision

Dezgo

A technical user can build a narrow self-hosted replacement (eg. text→image or a single text→video model) using open-source repos, but reproducing Dezgo's multi-model hosted catalog, scale, and polished UX is substantial and operationally costly.

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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-off80 h to build

$2,000/mo20 h/mo upkeep

No published price to break even against.

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. All Dezgo alternatives, with the arithmetic →

What a replacement has to do

  • Accept a text prompt (and optional image/seed), run a generative model, store and serve resulting image/video, let user preview and download

What it still won’t have

  • Hosted multi-model catalog and model-switching convenience
  • Scale and reliability of a production service
  • Polished web UI and user onboarding flows
  • Free hosted usage for casual users

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Dezgo 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 hosted AI image/video generation service using: Next.js (React) frontend, FastAPI backend, Postgres for jobs, Redis for queue, MinIO or S3 for storage, and PyTorch-serving workers running open-source models from open-mmlab/CogVideo. Scope: accept text prompts and optional seed images, enqueue jobs, run a text-to-image or text-to-video model to produce outputs (images or 3–15s video), generate thumbnails, persist outputs to object storage, provide job status API and a web UI to preview and download. Out of scope: training new models, multi-tenant billing, large model catalog, advanced editor features. Include logging, basic auth, retries for worker failures, end-to-end tests for API and a smoke test for a generation job, and Docker Compose + deployment instructions for one GPU instance.
How we checked5 sources · 3/3 runs agreed · evidence score 64

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 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 · 5

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