Rainfrog

Rainfrog replaces text prompts with a node-based AI workflow on a canvas, where you connect assets like products, models, styles, and environments to generator nodes that the platform traverses to assemble the prompt itself. No prompt syntax. No guesswork. It's the core of the product and the reason you don't need prompt-writing skills.

The backend writes the prompts for you, with what the company calls taste already embedded, so you point a workflow at your assets and hit generate without touching a prompt field. That matters if your team has creative chops but nobody who wants to learn prompt syntax, because the workflow itself carries the direction instead of a written brief.

Generator nodes handle both stills and video, and you can set normal or pro quality per node. That range covers everything from fast concept checks to final campaign assets in one place, so you're not juggling separate tools per output type. Video costs more credits than stills, which is the tradeoff for keeping both in one studio.

Every public example on the site can be cloned into your own projects. You click an image, see the workflow behind it, and start from there. It's a fast way to learn the node model and to keep a proven setup for repeat campaigns. Less setup, more output.

Generated results save to a project library inside the studio. With a paid plan, storage and credit balances sit alongside those assets, so a team can find past outputs instead of digging through local folders. It's a small thing, but on a long campaign it saves real time. Every shot stays one click away.

Every paid plan includes a monthly credit allotment, and unused credits roll over when your subscription renews. One-time top-up credits never expire. It gives you a predictable pool to plan production runs around. Watch usage on heavy months, because credits burn fast once video enters a workflow.