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SPECIFICTION
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DataRobot vs FEDOT

DataRobot

datarobot.com

FEDOT

fedot.readthedocs.io

Side by side

FigureDataRobotFEDOT
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WebYes—
Windows—Yes
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published11
Spec score · AutoML Software7.9 (#11)8.7 (#4)
PurposeDataRobot provides a platform to develop, deliver, and govern AI solutions, including agentic, generative, and predictive AI.FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach.

Plans

DataRobot

  • Self-Service SaaS trialFree

DataRobot pricing →

FEDOT

  • FEDOTFree

FEDOT pricing →

In their own words

DataRobot

DataRobot is a platform for developing, delivering, and governing agentic, generative, and predictive AI solutions. Its Agent Workforce Platform supports building production agents, deploying them across infrastructure, and governing them organization-wide. Development tools include data and tool registries, a vector store, code-first workflows, and workflow evaluation and comparison. Integrations cover business applications, LLMs, APIs and frameworks, data platforms, and AI infrastructure, with documented connections to AWS, Azure, Google Cloud, and Snowflake. The platform can run on-premise, in a virtual private cloud, or as software as a service. DataRobot says it encrypts data in transit and at rest and supports SAML single sign-on, multifactor authentication, and LDAP for self-managed customers. It lists ISO 27001 and SOC 2 Type II certifications. Pricing is on request, with a 30-day trial. The trial costs nothing, allows up to five team members, and deletes data and assets 30 days after expiration unless the user contacts Sales.

Visit datarobot.com

FEDOT

FEDOT is a free, open-source framework for automating the creation of data-driven composite models. It supports classification, regression, clustering and time-series forecasting, with tabular, text and image inputs, including multimodal combinations. Its workflow covers preprocessing, model selection, tuning, cross-validation and serialization. Users can leave parameters unspecified for fuller automation or provide them to guide pipeline composition. FEDOT uses the GOLEM library to optimize graph-based pipelines with meta-heuristic methods. Presets include best_quality, fast_train, stable, auto, gpu, ts and automl; auto is the default. Data can come from CSV files, pandas DataFrames, NumPy arrays or time-series CSV data. It can be installed with pip, and the API can be called from a console without Python code, saving predictions as CSV files. GPU evaluation uses RAPIDS and supports a specified set of models. The project is distributed under the BSD 3-Clause license and supports Windows, Linux and macOS.

Visit fedot.readthedocs.io