Image and video decision
Krea
You can self-host core image-generation features because Krea publishes an open-source repo, but the hosted product's value relies on proprietary models and inference infrastructure that are costly and hard to match; build for experimentation or narrow workflows, keep paying for full production scale and enterprise capabilities.
Visit website↗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
- Provide a web UI to submit text+image prompts, run the open-source Krea model to generate images, store and serve results, and an endpoint for upscaling/LoRA fine-tuning.
What it still won’t have
- Krea-hosted inference infrastructure and latency/scale optimizations
- Automatic model updates and access to their proprietary/commercial models and model catalog
- Enterprise features (SLA, audit logs, user seats, hosted compute packages)
- Convenience of integrated web app, workflow automation nodes, and managed LoRA training
What remains hard
- Proprietary models
Powerful proprietary models
- Proprietary models
Krea 2 is our first foundation image model built completely from scratch, focused on aesthetic diversity, style control, and expressive visual direction.
- Infrastructure at scale
We put a lot of effort into having one of the strongest inference infrastructures to make models like Flux or Krea-1 to work in just seconds.
- Infrastructure at scale
Industry-leading inference speed
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 223 seats.
Money you would actually spend
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
Build a self-hosted instance of Krea 2 using the official krea-2 GitHub repo on a single GPU VM. Stack: Ubuntu 22.04, Docker, Docker Compose, Postgres, MinIO (S3-compatible), Nginx, and PyTorch/TensorRT. In scope: clone https://github.com/krea-ai/krea-2, wire environment variables, install model weights, run the model server container on a GPU (NVIDIA drivers + Docker runtime), deploy the backend API, run the web frontend, configure persistence (Postgres + MinIO), implement job queuing, and expose a simple prompt+upload UI. Out of scope: reimplementing model architecture or building multi-region autoscaling. Deliverables must include automated start scripts (docker-compose or k8s manifests), basic unit/integration tests for API endpoints, error handling for model failures and OOMs, and documentation of GPU instance type and monthly cost assumptions.
How we checked
How the score was reached
- Self-host verdict base92
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- 1/1 assessment runs agreed+4
- Hard moats found in the evidence-6
- Evidence score99
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.
- official productKrea homepage (product + pricing)
- official docsKrea AI Image Generator feature page
- open sourceAnil-matcha/Open-Generative-AI
- open sourceStability-AI/StableStudio
Integrity checks
What held up, and what did not.
