Image and video decision

Recraft

A capable developer can reproduce a useful subset (prompt→generate→store→basic editor→billing) by integrating external models and open-source editor components, but you cannot reproduce Recraft’s claimed proprietary model quality or built-in vector-generation fidelity without significant investment.

Visit website
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

$50one-off30 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

  • Accept text prompt + optional style images → generate raster or editable SVG vector → store result, present in web editor, allow edits/exports, bill for usage

What it still won’t have

  • Proprietary Recraft model quality and any unique art-direction baked into their models
  • Native editable-vector generation parity (quality and fidelity)
  • Enterprise SLA, managed hosting, and integrated product polish
  • Any private features tied to Recraft’s internal models or datasets

What remains hard

  • Proprietary modelsRecraft has released four generations of its own model:
  • Proprietary modelsMeet Recraft V4.1: Our most advanced model. Beautiful by nature.
Read the build prompt

First-year cost

No published price

Recraft 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
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

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

Not run yet
Build a minimal Recraft-like service using Next.js + React frontend, Node.js backend (Express), Postgres for metadata, S3-compatible storage for assets, and Redis+BullMQ for async jobs. Scope: (1) web UI to submit text prompts and upload reference images, (2) backend job worker that calls an external image model API (e.g., Stable Diffusion/Replicate) and a vectorization step to produce SVG, (3) store outputs and metadata, (4) simple web editor to crop/replace background and download PNG/SVG, (5) per-user credit counter and Stripe subscription for 1 seat. Out of scope: training new generative models, advanced art-direction models, enterprise SSO. Include error handling for failed model jobs, retries, and automated tests for API endpoints and worker jobs.
How we checked3 sources · 2/3 runs agreed · evidence score 52

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Hard moats found in the evidence-3
  • Evidence score52

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 · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 2 moats quoted from the page