datarobot-predict
DataRobot Prediction Library
Decision gist · record as of 2026-08-14
Yes, if you are already committed to DataRobot and want to simplify prediction code with a unified interface. The permissive license, low install friction, and lack of known vulnerabilities make it safe to use. However, the aging maintenance status means you should verify that the library supports your specific prediction methods and be prepared for slower issue resolution if problems arise.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; also depends on datarobot package and its transitive dependencies (pandas, requests, py4j, etc.).
- Low install friction with a pure-Python wheel.
- Maintenance status is aging (233 days since last release), so expect slower response to issues, though the package remains functional for its core use case.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you can use this library in commercial and proprietary projects with minimal restrictions, provided you include a copy of the license.
last release 2025-12-24 (233 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 147,203 downloads/mo, #11,074 on PyPI
Alternatives
Verify before relying
pip install datarobot-predict
import datarobot_predict
# Use the library's prediction interface with your DataRobot models- Whether the library works with all DataRobot model types or only a subset of prediction methods.
- Whether py4j dependency is required for all use cases or only for specific prediction backends.
- Current state of the official documentation and whether it covers all supported prediction methods.
What it is and what it does
DataRobot Prediction Library is a Python wrapper that standardizes how you call predictions across different DataRobot prediction methods. Instead of learning multiple APIs for different backends, you get one common interface that lets you swap implementations without changing your calling code. It sits on top of the datarobot package and related dependencies like pandas, requests, and py4j.
The library is intended for teams already using DataRobot who want to simplify their inference code. It abstracts the complexity of multiple prediction backends into a single entry point. The package is aging (last release 233 days ago), so while it remains functional, updates and bug fixes may come slowly.
Use it for
- Standardize prediction calls across multiple DataRobot model types in a single application.
- Swap out prediction backends without rewriting inference code.
- Build a prediction service layer that abstracts DataRobot implementation details from application logic.
- Integrate DataRobot predictions into a larger ML pipeline with a consistent interface.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already committed to DataRobot and want to simplify prediction code with a unified interface.
The permissive license, low install friction, and lack of known vulnerabilities make it safe to use. However, the aging maintenance status means you should verify that the library supports your specific prediction methods and be prepared for slower issue resolution if problems arise.
Install
datarobot-predict on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance status is aging (233 days since last release), so expect slower response to issues, though the package remains functional for its core use case.
Requires Python 3.8 or later; also depends on datarobot package and its transitive dependencies (pandas, requests, py4j, etc.).
License in practice
Apache-2.0 is permissive; you can use this library in commercial and proprietary projects with minimal restrictions, provided you include a copy of the license.
Quickstart
pip install datarobot-predict
import datarobot_predict
# Use the library's prediction interface with your DataRobot modelsVerify before relying
- Whether the library works with all DataRobot model types or only a subset of prediction methods.
- Whether py4j dependency is required for all use cases or only for specific prediction backends.
- Current state of the official documentation and whether it covers all supported prediction methods.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagespandasclickpy4jdataroboturllib3requestsrequests-toolbelt |
| Maintenance | Aging 233 days since the last release |
| First released | |
| Downloads | 147,203 / month, #11,074 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: datarobot_predict-1.13.5-py3-none-any.whl
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