Palantir's modeling suite of products enables users to develop, manage, and operationalize models. This page compares different products to help you choose the right tool for your needs.
| Product | Details |
|---|---|
| Pipeline Builder | Large scale point-and-click data transformation |
| Code Workspaces | Interactive, pro-code data analysis and transformation in familiar environments such as JupyterLab® |
| Python Transforms | PySpark data pipeline development in Foundry's web-based IDE, Code Repositories |
No-code model training tools are available in Model Studio, providing a simple point-and-click interface for creating production-grade machine learning models.
The palantir_models library provides flexible tooling to publish and consume models within the Palantir platform, using the concept of model adapters. The foundry_ml library, its predecessor, has been formally deprecated as of October 2025.
| Product | Library support | Details |
|---|---|---|
| Code Workspaces | palantir_models | Interactive model development in Jupyter® notebooks |
| Code Repositories | palantir_models | Powerful web-based IDE with native CI/CD features and support for modeling workflows; less interactive than notebooks |
| Product | Details |
|---|---|
| Experiments | Framework for logging metrics and hyperparameters during a model training job |
Models can be used for running large scale batch inference pipelines across datasets.
| Product | Details | Caveats |
|---|---|---|
| Python transforms | Batch inference can be run directly in Python transforms. Supports pinning a specific model version. | Using the @lightweight decorator and model sidecars is recommended. |
| Modeling objective batch deployments | Modeling Objectives offers broader model management features such as model release management and evaluation. | Does not support multi-output and external models, models as sidecars, or deployment via Marketplace as detailed here. |
| Jupyter® Notebook | Users can create scheduled training and/or inference jobs directly from Code Workspaces. | Only supports running inference models created from the same notebook; use Python Transforms to orchestrate models created elsewhere. |
Models can be deployed in Foundry behind a REST API; deploying a model operationalizes the model for use both inside and outside of Foundry.
| Product | Details |
|---|---|
| Model direct deployments | Auto-upgrading model deployments; best for quick iteration and deployment. |
| Modeling objective live deployments | Production-grade modeling project management; modeling objectives provide tooling for model release management and evaluation. Does not support deployment via Marketplace as detailed here. |
Publishing models as functions makes it easy to use models for live inference in downstream Foundry applications, including Workshop, Slate, actions, and more.
| Product | Best for |
|---|---|
| Direct function publication | No-code function creation on models with live deployments, allowing integration with the Ontology. The same functionality is available in the Model and Modeling Objectives applications. |
| Importing model functions into Functions repositories | Import model functions into TypeScript v1, v2 or Python functions to further process predictions (for example, make ontology edits) with support for Model API type checking. |