Foundry incorporates foundational models into your Labelbox workflow. You can use Foundry to:
  • Predict (infer) labels from your data
  • Compare the performance of different foundational models with your data and ontologies.
  • Prototype, diagnose, and refine a machine learning app to solve specific business needs.
  • Generate preference data to fine-tune large language models for RLHF
  • Evaluate multimodal LLM
Foundry creates model runs that predict data row annotations based on your input. Here, we introduce Foundry, show how to use it, and explain the associated charges.

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Before using Foundry

Before using Model Foundry, you should:

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Foundry workflow

To use Foundry, you:
  1. Select the data rows you want to annotate
  2. Select the foundational model
  3. Define, verify, and submit the model run
  4. Track progress

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Common tasks

Use the Notification Center to view and track model run progress . When model runs complete, you can:

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Foundry availability

Foundry is available to all subscription types except Educational. Free, Starter, and other self-service subscriptions need to enable Foundry as an add-on to their subscription. When use Foundry from a self-service subscription, a confirmation prompt verifies that you understand and agree to the terms and conditions.
If you do not currently have a payment method, you are prompted to add one.
For help, see Manage account and billing, which shows how to upgrade self-service plans to enable Foundry, how to update billing details, and other related tasks. Enterprise and other accounts should work with their account manager to enable Foundry.

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Foundry billing

There are two costs associated with Foundry model runs:
  • Each model run consumes LBUs based on the amount of data processed. LBUs are limited and charged according to your subscription terms.
  • When using models hosted by Labelbox, Foundry model runs also generate inference costs that vary according to the model, the amount of data processed, the complexity of the inference task, and other factors. These costs are passed onto you and are charged separately from your Labelbox subscription.
Here’s an 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).

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Review model costs

To view the costs associated with a foundational model hosted by Labelbox, view the model card in the Model gallery.
  1. Sign in to the Labelbox app and then select Model from the menu.
  2. Select the model from the list.
  3. Review the Pricing details displayed on the Overview tab.
Note that pricing details vary according to the model and its use.

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Review model run costs

To review the cost of a model run, open it in Model and then view its details.

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Foundry removal

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.
  1. 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.