This template reads video prompts from Google Sheets on a schedule. ChatGPT writes the title, description, and tags.
Sora renders the actual clip. The file is saved to Google Drive, then uploaded to YouTube with that ChatGPT metadata. Connect Google Sheets, Google Drive, and YouTube. ChatGPT and Sora are Latenode AI nodes.
Latenode bills workflow runtime in CPU seconds. The first 10,000 CPU seconds are free each month, then usage follows pay-as-you-go CPU-second pricing. Plug-and-Play (PnP) AI nodes are billed separately—each PnP token is $1 USD, charged pay-as-you-go at vendor cost plus a small processing fee, with no API keys required.
Full pricing — how CPU seconds work →Workflow preview
The scenario starts on the cadence you set and reads pending prompt rows from Google Sheets.
ChatGPT returns title, description, and tags from the sheet prompt.
JavaScript shapes a render prompt from the row. Sora starts the video and returns the file when it is ready.
Google Drive stores the rendered file. YouTube uploads it and applies the ChatGPT title, description, and tags. The sheet row gets the YouTube URL.
Add Google Sheets, Google Drive, and YouTube connections. Use a sheet with columns for prompt, status, and YouTube URL. Pick the Drive folder that should keep rendered files.
Ask ChatGPT for title, description, and tags from the prompt row. Add Sora to start the render and fetch the finished clip. These are Latenode AI nodes.
Add a schedule for how often pending rows should run, such as once a day.
Read pending rows from the sheet, write metadata with ChatGPT, shape the Sora prompt in JavaScript, render with Sora, upload to Drive, publish on YouTube with the ChatGPT metadata, then write the YouTube URL back to the row.
FAQ
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