# Modelbit Deploy ML models. Source: [Console review]() Website: [https://www.modelbit.com/]() Profile updated: 2026-10-16 Category: [Models & infrastructure]() ## Supported tech stack Research checked: 2026-10-11 ### Runs on - Linux (Operating system) - macOS (Operating system) ### SDK support - Python (Language) ### Integrates with - Jupyter (Integration) Source: [Research source]() Documented compatibility; coverage may be incomplete. Written in describes implementation, not user-project language support. Modelbit's published Python package identifies Modelbit as author and lists Jupyter, macOS and Linux compatibility. The product website could not be verified; these tags describe the published SDK. ## Console’s review — 2023-05-18 Reviewed: 2023-05-18 Author: [David Mytton]() Citation: [This review]() ### What we like Build & deploy models from Jupyter notebooks. Provides an inference endpoint accessible via a REST JSON API and from within data warehouses e.g. from SQL, Redshift, or Snowflake. Supports custom environments, packages, tests. Can also run training jobs from a notebook or from Git. Includes logs, monitoring & version control. ### Tradeoffs Everything is based around Python & Notebooks - this is the standard ML stack, but worth being aware of if you’re using something else. This review reflects Console’s assessment at publication on 2023-05-18. Features and limitations may have changed since the review date. ## Is Modelbit a fit? Based on [Console’s review published on 2023-05-18](). Summary updated: 2026-10-16 ### Who it’s for Machine learning developers using Python and Jupyter notebooks should consider this tool ### Why consider it It allows deploying ML models with a REST JSON API and custom environments ### Keep in mind It may not be suitable for developers using non-Python environments [Selection criteria]() · [About Console]() · [Browse tools]()