Einstein Vision is retired. The steps below cover migrating an existing consumer to a modern path; do not build new integrations against the retired API.
- Inventory every Vision API call
Search the codebase for the Einstein Platform Services base URL or the predict, train, and dataset endpoints. List each consumer with the dataset it depends on and the downstream consumer of the prediction.
- Map each call to a use case category
Sort calls into damage assessment, document OCR, visual product matching, asset counting. The replacement depends on the category, not on which Vision endpoint was called.
- Pick the modern replacement
Field service image classification often moves to embedded Field Service intake. Document OCR often moves to Salesforce OCR services in Cloud or Files. Visual product matching often moves to Commerce Cloud Einstein. Truly custom needs go to BYOM in Einstein Studio.
- Train or register the replacement model
Embedded features need their own dataset configuration. BYOM needs an external training pipeline and registration in Einstein Studio with an exposed endpoint.
- Cut over with parallel-run validation
Run the new path alongside the old call for one to two weeks. Compare predictions on a hand-graded sample. Cut over when the new path meets or beats the old accuracy.
The replacement for technician photo classification in Field Service. Damage assessment lives in the work order workflow.
The replacement for visual product matching and visual search use cases in commerce contexts.
For visual search inside a Salesforce org, the Files feature offers indexed similarity search without separate API plumbing.
The fallback for custom vision needs. Train externally, register in Einstein Studio, call from flows or prompts.
Some Prompt Builder paths accept image inputs alongside text, letting a foundation model classify or describe images directly.
- Einstein Vision is retired. New connected apps cannot register. Existing apps should plan a migration even if calls currently succeed.
- BYOM requires owning the model lifecycle: training, retraining, monitoring, and refresh. The platform does not manage these for custom models.
- Embedded replacements carry their own data model. The output may need transformation to match what the original Einstein Vision integration consumed.
- Direct Apex callouts to external vision models bypass the Einstein Trust Layer. Wrap calls in prompt templates if Trust Layer governance is required.
- Older Trailhead modules and developer docs still reference Einstein Vision. Confirm against current release notes before quoting them as guidance.