PyCaret
Toronto, Canada
What it is
PyCaret is a free, open-source, low-code Python tool for automating machine-learning workflows. Its experiment APIs support classification, regression, clustering, anomaly detection, and time-series forecasting. Users can fit experiments, compare and tune models, make predictions, and save or load models. Fitted outputs can be used as portable scikit-learn pipelines, with forecasting pipelines for time-series work. The core engine installs from PyPI with `pip install pycaret`; the listed supported Python versions are 3.11, 3.12, and 3.13. Forecasting, explainability, and dashboard dependencies are optional extras. The default path runs on CPU, while GPU-accelerated estimators are available when the required libraries are installed. For multi-user or deployment workflows, an optional React dashboard and FastAPI backend can be self-hosted. Saved pipelines can be served behind FastAPI, and the optional PyCaret server offers an HTTP prediction endpoint. The backend can connect to existing authentication and is JWT-friendly. The FAQ directs questions to GitHub Discussions and bug reports to GitHub Issues.
For and against
For
- Supports five listed machine-learning task types
- Creates portable scikit-learn pipelines
- Supports Python 3.11, 3.12, and 3.13
- Optional self-hosted dashboard and FastAPI backend
- Free, MIT-licensed open-source tool
Against
- PyCaret 4.0 removes the 3.x module-level functional API
- GPU estimators require their relevant libraries
The verdict
PyCaret offers a free workflow engine with experiment APIs, reusable pipelines, and optional self-hosted deployment components. Existing code using the 3.x module-level functional API may need to stay on 3.x; the FAQ recommends 4.0 for new work.
Who it suits
PyCaret suits Python users who want to automate model experiments across supported machine-learning tasks. Teams seeking self-hosted dashboards or deployment workflows can use its optional React dashboard and FastAPI backend.
Plans and prices
PyCaret open-source
No paid tier · MIT open-source engine · self-hosted
Read 15 Oct 2026 · pycaret.org
PyCaret
MIT-licensed open source; no paid tier or enterprise plan stated
Read 7 Oct 2026 · pycaret.org
On the record
- Anomaly detection
- AnomalyExperiment uses pyod adapters including Isolation Forest, KNN, COPOD, ECOD, LOF, PCA, MCD, ABOD, CBLOF, HBOS, OCSVM, SOS, and SOD.pycaret.org · read 15 Oct 2026
- Backend API
- The optional pycaret-server backend is a FastAPI service with CRUD, run orchestration, WebSockets, pipeline deployment, and plot endpoints.pycaret.org · read 15 Oct 2026
- Classification
- ClassificationExperiment handles categorical targets and includes metrics such as accuracy, AUC, recall, precision, F1, kappa, and MCC.pycaret.org · read 15 Oct 2026
- Clustering
- ClusteringExperiment offers KMeans, affinity propagation, mean shift, spectral, hierarchical, DBSCAN, OPTICS, Birch, KMode, and HDBSCAN adapters.pycaret.org · read 15 Oct 2026
- Company description
- LinkedIn describes PyCaret as an open-source, low-code machine-learning library and a nonprofit software-development organization.linkedin.com · read 15 Oct 2026
- Company location
- LinkedIn lists Toronto, Canada as PyCaret's headquarters and primary location.linkedin.com · read 15 Oct 2026
- Company size
- LinkedIn lists PyCaret's company size as 11–50 employees.linkedin.com · read 15 Oct 2026
- Compatibility break
- Version 4.0 removes the 3.x module-level functional API and requires the new Experiment object-oriented API; mixing 3.x and 4.0 is unsupported.pycaret.org · read 15 Oct 2026
- Compatibility limit
- PyCaret 4.0 removes the 3.x module-level functional API, so the FAQ advises pinning 3.x for existing code and using 4.0 for new work.pycaret.org · read 7 Oct 2026
- Current maturity
- The GitHub repository labels the 4.0 line as work in progress and says the 3.x line is frozen at 3.4.0.github.com · read 15 Oct 2026
- Dashboard
- An optional React dashboard and FastAPI backend can be self-hosted for multi-user and deployment workflows.pycaret.org · read 7 Oct 2026
- Deployment
- Models can be saved as portable scikit-learn pipelines, served behind FastAPI, or deployed through the optional pycaret-server layer.pycaret.org · read 15 Oct 2026
- Documentation depth
- The official site includes concepts, tutorials, preprocessing references, function guides, FAQs, a cheat sheet, and generated API reference.pycaret.org · read 15 Oct 2026
- Explainability and plots
- The plotting modules expose diagnostic charts for classification, regression, features, clustering, anomalies, time series, and exploratory analysis.pycaret.org · read 15 Oct 2026
- Forecasting
- TimeSeriesExperiment is built on sktime and supports models such as ARIMA, AutoARIMA, exponential smoothing, ETS, Theta, STLF, Croston, and reduced-regressor adapters.pycaret.org · read 15 Oct 2026
- Hardware
- The default path runs on CPU, and GPU-accelerated estimators are available when the relevant libraries are installed.pycaret.org · read 7 Oct 2026
- Hyperparameter tuning
- tune_model performs configurable iterative hyperparameter searches and returns tuned pipelines and parameters.pycaret.org · read 15 Oct 2026
- Installation
- The documented installation is pip install pycaret, with dashboard, explainability, and forecasting extras available.pycaret.org · read 15 Oct 2026
