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Welcome to Labelbox Foundry, your integrated environment for leveraging foundation models and custom AI to accelerate your data labeling and model development workflows. Foundry is designed to help you move from raw, unlabeled data to high-quality training data with speed and efficiency. With Foundry, you can:
  • Generate high-quality predictions: Use state-of-the-art foundation models to automatically generate labels for your data.
  • Compare model performance: Run multiple models on the same data to evaluate and compare their performance, helping you choose the best model for your use case.
  • Automate labeling workflows: Set up Foundry Apps to create repeatable, automated labeling pipelines that can be triggered from the Catalog or via API.
  • Active learning workflows: Identify the most valuable data to label by finding where your model is least confident, and send those assets directly to a human labeling project.
  • Integrate custom models: Bring your own models into the Labelbox ecosystem to take advantage of Foundry’s powerful workflow tools.
Foundry helps you close the gap between your model’s capabilities and your production needs, streamlining the path to building better AI.

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Key concepts

ConceptDescription
Model runThe core process in Foundry. A model run is a single execution of a model on a selected batch of data rows. It generates predictions for each data row.
Foundry appA saved model run configuration. Foundry Apps allow you to save a specific model and its settings so you can easily and repeatedly run it on new batches of data, automating your workflows.
PredictionsThe output generated by a model run. Predictions are the labels or values that the model “predicts” for your data (e.g., bounding boxes, classifications).
OntologyThe schema that defines the features your model can predict. You will map the model’s output to your project’s ontology to ensure consistency.

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Before you begin

To ensure a smooth experience with Foundry, please complete the following setup steps:
  1. Connect your cloud data: Foundry works directly with your data stored in the cloud. Make sure you have successfully connected your AWS, GCP, or Azure cloud storage to Labelbox.
  2. Create a project: You’ll need a Labelbox project set up with a defined ontology. This project will be the destination for predictions that need human review.
  3. Select your data: In the Labelbox Catalog, identify and select the data rows you wish to use for your model run.

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Add Foundry to your workspace

Foundry is available for all plans except for Educational subscriptions.
  • Self-Service (Free, Starter): Go to your workspace settings to enable Foundry as an add-on. You will be prompted to agree to the terms and add a payment method if needed.
  • Enterprise: Please contact your account manager to enable Foundry.

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How billing works

Foundry billing has two components: Inference Costs and Labelbox Units (LBUs).
Cost typeDescription
Inference costsThis is a direct cost for using a Labelbox-hosted model, charged in USD. It varies by model and the amount of data processed. These fees are passed on to you and charged immediately after a model run.
Labelbox units (LBUs)Your Labelbox subscription includes a set number of LBUs. Foundry consumes LBUs when you run a model and when you send predictions to a labeling project. This usage is deducted from your subscription’s LBU balance.
For example:
  1. Suppose you import 1,000 images into Catalog; this consumes 17 LBUs.
  2. You select 500 images and use a Foundry model run to generate predictions. Based on the model you selected and the parameters of your model run, this generates a $2.00 inference fee.
  3. When the Foundry model run is complete, 500 images and their predictions are now available in Model. This consumes 100 LBUs.
  4. To verify the predictions, you send them to Annotate for human review. This consumes another 500 LBUs.
Overall, you’ve generated $2 in inference costs (Step 2); this is charged immediately. You’ve also used 600 LBUs as one time charges (Model and Annotate) and generate a 17 LBU charge each month your data remains in Catalog. The LBU consumption is charged against the terms of your subscription at the end of the current billing cycle. Compute fees depend on the specific model used, the amount of data processed, and other factors. For details, consult the model card. To learn more about LBUs, see Labelbox Units (LBUs). You can review the specific costs for any model by selecting it in the Model gallery and viewing the Pricing details on its overview tab. The total cost of any completed model run can be found in the run’s details.

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Remove Foundry from workspace

You can unsubscribe to Foundry and remove it from your Labelbox subscription at any time. To do so:
  1. Select Workspace Settings from the Labelbox main menu to open the Organization Settings.
  2. From the Billing tab, locate the Add-ons section and then select Remove.
  3. A confirmation prompt asks you to confirm your request. Select Unsubscribe to do so.
Removing Foundry from your subscription doesn’t affect your data. Any predictions created during the subscription remain.