Capabilities and workflow
| Criterion | StayCharted AI Model Trainer | Google Teachable Machine |
|---|---|---|
| Who uses it | Business and operations teams running recurring classification workflows; developers using the API.StayCharted documentation | People exploring machine learning without coding; developers can integrate exported models into applications.Intended users · checked October 8, 2026 |
| Supported tasks | Text classification and image classification with Vision. No audio or pose models.StayCharted documentation | Image, sound and pose classification.Supported project types · checked October 8, 2026 |
| Training location | Training is processed by StayCharted’s hosted service.StayCharted documentation | Training happens in the browser.Browser training · checked October 8, 2026 |
| Training inputs | Labeled text files or categorized pictures.StayCharted documentation | Uploaded images or examples captured with a webcam or microphone.Gathering examples · checked October 8, 2026 |
| Using the model | Process new files in the application or integrate through the API on Essentials and up.StayCharted documentation | Test in the browser; export in TensorFlow.js, TensorFlow or TensorFlow Lite formats as supported by the project type.Testing and export · checked October 8, 2026 |
| Data handling | Workspace permissions and hosted retention policies apply. Assistant connections can share requested data with the assistant provider.StayCharted documentation | Google describes examples staying on-device unless the project is saved to Google Drive. Check data flow again when integrating an exported model.Example data handling · checked October 8, 2026 |
Fit by requirement
StayCharted
Recurring text or picture classification where a team wants hosted file processing, training reports, review and published versions.
Teachable Machine
Browser-based experimentation, sound or pose projects, or an exported model that will be integrated into a separate application.
Before choosing
Test both workflows on the same separate examples. Compare category errors, review effort, integration and total operating cost.
StayCharted plans and workflow
Free covers text and includes up to one AI Classifier Model. Essentials is $49/month and Business is $99/month per workspace. Pictures require Vision: +$49/month on Essentials or +$99/month on Business. Dedicated AI Models require Business. Prices are USD.
- Every training run reports accuracy on examples not used to train the model, overall and by category. Answers include confidence.
- Data-quality checks and personal-information masking before text training are available on every plan.
- On Essentials and Business, answers below your review cutoff go to a Review Queue. Grouping unlabeled examples into suggested categories is also included; pictures require Vision.
- Picture checks flag near-duplicates and potentially mislabeled examples. Location metadata is stripped from the copies used by the model; original uploads are retained unchanged until deletion.
StayCharted is a hosted service, with no on-premises application deployment or trained-model import. Standard workspace exports do not contain runnable models. Under Terms §6.3, an Owner can download a Dedicated model adapter after moving to a plan that no longer trains Dedicated models; running it elsewhere requires the exact base model and your own inference software.
Compare plans and allowances · Export conditions · Data handling
Common questions
Can Teachable Machine classify text spreadsheets?
Its documented project types are images, sounds and poses. For text rows with category labels, evaluate a text classification product such as StayCharted.
Does Teachable Machine send training examples to a server?
Google describes training as happening in the browser, with examples staying on-device unless saved to Google Drive. A separate application using an exported model can have a different data flow.
Can I import a Teachable Machine model into StayCharted?
No. StayCharted does not import trained model files. Prepare your labeled examples, train a new model and evaluate it on a separate test set.
Which image classifier is more accurate?
This page contains no head-to-head test. Use the same labels and separate test pictures, including changes in lighting, viewpoint and background, to evaluate both.