Image and video decision
Deep Agency
A technical user can build a useful self-hosted replacement using open-source avatar and diffusion projects; no durable moats are evident on the product page, but expect multi-week work and ongoing ops.
Visit website↗Built by Danny Postma, who ships 3 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off120 h to build
$200/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
- User submits brief and selects a virtual model → system generates professional photos of the virtual model per brief → user reviews and downloads assets.
What it still won’t have
- Brand, product polish, and managed UX optimizations
- Proprietary model fine-tuning and any curated model library maintained by the vendor
- Ongoing marketing, community reach, and closed-beta support
- Legal/rights assurances and contract handling provided by the company
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Deep Agency 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
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
Build a minimal self-hosted virtual-model photo generator using Python (FastAPI) backend, React frontend, PostgreSQL for metadata, and S3-compatible object storage. Core features in scope: (1) brief submission form (style, poses, resolution), (2) server-side generation worker that calls an open-source image model (use Hugging Face Diffusers / local SDXL) plus support for loading LoRA weights, (3) job queue (Redis + RQ) and endpoints to poll status, (4) image storage and signed-download links, (5) web gallery to preview and select final images, (6) simple Stripe checkout for paid downloads, (7) automated email with asset links. Out of scope: training large foundation models, hosted multi-tenant billing, complex rights-management contracts, video-avatar lip-sync. Include input validation, retries for model inference failures, logging, basic unit tests for API endpoints, and containerized deployment (Docker + docker-compose).
How we checked
How the score was reached
- Partly verdict base52
- 3/3 assessment runs agreed+4
- Evidence score56
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 · 1
Every page the run actually retrieved.
- official productDeep Agency — AI Modelling Agency | Deep Agency
Integrity checks
What held up, and what did not.
