Roboflow + Zapier
Run Roboflow models inside Zaps with the Roboflow app on Zapier, or send Workflow results to a Zapier webhook to act on them in thousands of apps.
How to connect Zapier to Roboflow
4 stepsAdd a trigger that provides an image
Start a Zap with a trigger such as a new email attachment, and store the file somewhere that gives a public link, like Google Drive with link sharing on.
Add a Roboflow action
Choose the Roboflow app and an action such as Infer Classification or Infer Object Detection, then connect your account with your Roboflow API key.
Set the model and image
Pick the model and version, set a confidence threshold and map the image URL from the earlier step.
Act on the predictions
Add filters or paths on the returned class and confidence, then send emails, add spreadsheet rows or update other apps.
What teams build with Zapier
Route incoming files by what they show
Classify email attachments and forward each one to the right team.
Log detections to a spreadsheet
Send each prediction to a Zapier webhook and add it as a row in Google Sheets.
Notify people when a Workflow finds something
Post Workflow results to a Zapier Catch Hook and trigger an email or message.
Questions
The Roboflow app on Zapier has actions but no triggers. To start a Zap from Roboflow, send results to a Webhooks by Zapier Catch Hook, for example with a Workflow's Webhook Sink block.
The Roboflow app has actions for object detection, classification, instance segmentation and CLIP similarity, using models in your workspace.
No for the Roboflow app on Zapier. The webhook route needs a Workflow with a Webhook Sink or a short script that posts results.
Why are companies choosing Roboflow?
The end-to-end platform for vision AI: create datasets, train models and deploy to production.
Flexible deployment modes
Move from a scalable cloud API to edge hardware like NVIDIA Jetson without managing complex dependencies.
Integrated workflow builder
Combine multiple AI models, custom business logic and third-party integrations into one production-ready pipeline in a low-code editor.
Model monitoring and alerts
Dashboards track confidence, inference time and request volume for your deployed models, and email alerts tell your team when confidence suddenly drops.
