Image and video decision
Leonardo AI
A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Leonardo AI, organize local or API-backed image-generation workflows and retain parameters. The hard boundary is proprietary models, hosted gpu capacity, training tools, and asset ecosystem, plus frontier models, compute, and data.
Visit website↗$12/mo
$144/yr
Read off the official pricing page.
$100one-off40 h to build
$0/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 1 seat.
The code exists. It is not what you are paying for.
This project is real, published, and does the core job — and this page still says keep paying. What the subscription buys is proprietary models and infrastructure at scale, and none of that ships in a repository. Fork it anyway if you want to. Go in knowing what it does not carry. What stays hard ↓ · All Leonardo AI alternatives, with the arithmetic →
What a replacement has to do
- Organize prompts and local or API-backed image-generation workflows, submit jobs to a user-owned model server, retain parameters, and keep outputs reproducible.
What it still won’t have
- proprietary models, hosted GPU capacity, training tools, and asset ecosystem
- frontier proprietary models
- hosted GPU capacity
- licensed training data
- moderation and fast global delivery
What remains hard
- Proprietary models
- Infrastructure at scale
First-year cost
The build hours below are a category default, not an estimate for this product. Change them to your own numbers and the comparison follows.
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 1 seat.
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 closest honest personal substitute for Leonardo AI in an empty repository. Use Python 3.12, FastAPI, SQLite, ComfyUI as a local worker, and a small React frontend; do not offer alternative stacks. The core loop is: organize prompts and local or API-backed image-generation workflows, submit jobs to a user-owned model server, retain parameters, and keep outputs reproducible. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Create prompt, negative-prompt, seed, dimensions, model, and workflow controls. Submit jobs only to the local ComfyUI endpoint configured in .env. Record exact generation parameters and workflow JSON beside every output. Build a searchable contact sheet with compare, favorite, annotate, and rerun actions. Support local image-to-image and mask inputs without uploading them elsewhere. Show estimated VRAM needs and fail clearly when a workflow or model is missing. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out training a new frontier model. Deliberately leave out copying a vendor's proprietary model or dataset. Deliberately leave out public generation hosting and moderation. Finish by running the tests and listing the exact commands used.
