Should I use DALL·E 3 or Stable Diffusion?

DALL·E 3 via the OpenAI API is the fastest way to ship — no GPUs to manage and strong prompt adherence. Stable Diffusion is open and far cheaper per image at volume, but you must host inference on a GPU and tune it. Many teams launch on a hosted model, then add a self-hosted path once usage justifies the GPU cost.

How do I keep API keys safe in an image app?

Never call the image API directly from the browser with your secret key. Put the request behind your own backend route, store the key in a server-side environment variable, and have the frontend call your route instead. This also lets you add rate limiting and per-user quotas.

How much does AI image generation cost?

Hosted models charge per image, typically a few cents each depending on resolution and quality. Self-hosted Stable Diffusion costs the GPU rental time, which can be a fraction of a cent per image at high utilisation but requires you to pay for idle capacity too.

How do I store generated images?

The API returns either a temporary URL or base64 data. Download it immediately and upload it to durable object storage such as S3, R2, or a Vercel Blob bucket, then save the storage key plus the prompt and user ID in your database so you can show a gallery and re-serve images later.

How do I prevent unsafe or abusive image generation?

Run the user's prompt through a moderation endpoint before sending it to the model, reject disallowed categories, and screen generated outputs as well. Log prompts with user IDs so you can rate-limit and ban abusers, and surface clear usage rules in your UI.

How to Build an AI Image Generation App

Building an AI image generation app

An AI image generation app turns a text prompt into a rendered image using a diffusion model. The two practical paths are a hosted model such as DALL·E 3 through the OpenAI Images API, or a self-hosted Stable Diffusion model behind your own inference server. The core flow is the same in both cases: collect a prompt and options, send a request, wait for the render, store the result, and display it safely. This guide covers each step and the builder below assembles a real, correctly-shaped request you can adapt.

How the pipeline works

The app has three layers. The UI gathers a text prompt plus parameters like size, quality, style, and image count. The backend holds your API key, calls the image endpoint, and enforces rate limits — you must never expose a secret key to the browser. The storage layer persists each generated image to durable object storage and records the prompt, parameters, and user in a database so you can render a gallery and audit usage.

For DALL·E 3 you POST a JSON body with model, prompt, size, and n to the images endpoint and receive image URLs or base64 data. For Stable Diffusion you send the prompt, a negative prompt, sampling steps, and a seed to your own inference server. The builder below lets you set these fields and see the exact request payload for either provider.

Tips and safety notes

Always moderate the prompt before generation and the output before display — hosted providers offer a moderation endpoint, and self-hosted setups need a classifier. Save images to your own storage immediately, because provider URLs expire. Record a seed for Stable Diffusion so renders are reproducible. Add per-user quotas to control cost, and surface clear content rules in the UI so users know what is allowed before they spend a generation.