Design and diagrams decision
Make Design
A capable developer can build a usable text-to-design generator and credit system in about a week, but reproducing the vendor polish, priority rendering, advanced reasoning, and any proprietary model tuning that justify paid tiers is non-trivial.
Visit website↗$9/mo
$108/yr
Read off the official pricing page.
$100one-off40 h to build
$3/mo3 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
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 enters a text prompt → call a generative image/layout model to produce designs → display results in a gallery → allow chat-style prompt edits to re-run renders → export selected assets.
What it still won’t have
- Vendor-run priority rendering queue and guaranteed low-latency performance
- Any proprietary or in-house model tuning and advanced reasoning features
- Polished UI/UX, built-in showcases, and experimental platform features
- Support and priority customer service
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 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 minimal AI design generator web app using React for the frontend, Node.js (Express) for the backend, PostgreSQL for storing users/credits, and AWS S3 for assets. Core features in scope: prompt input and gallery, call a hosted image-generation API to create designs, simple chat-style follow-up edits that re-run generation, export/download assets (PNG/PDF), and simple Stripe billing to charge a flat monthly plan and decrement credits. Out of scope: training or hosting custom generative models, priority rendering queues, long-term personalization memory. Include error handling for API failures, rate limits, and storage errors, and provide basic unit/integration tests for backend endpoints.
How we checked
How the score was reached
- Partly verdict base52
- 2 cited sources+1
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score60
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.
- official productmake.design
- official pricingmake.design pricing
Integrity checks
What held up, and what did not.