PYCARET
- Company typePrivate
- Founded2019
- HeadquartersToronto, Canada
- Headcount1–10
- GTM typeB2B
- OfferingSoftware
PYCARET firmographics
Firmographics- Name
- PYCARET
- Legal name
- PyCaret contributors
- Website
- https://pycaret.org
- Company type
- Private
- Founded year
- 2019
- Operating status
- Operating
- Headcount range
- 1–10 employees
- Ownership category
- akta.pro rank
PYCARET industry classification
Industry- Product category
- Automated Machine Learning Software
- NAICS
- Computer Systems Design and Related Services (54151)
- SIC
- Services-Prepackaged Software (7372)
- akta.pro primary industry
- AutoML & Low-Code ML Platform Operations (HDAAABAL)
- akta.pro secondary industries
- End-to-End MLOps & ML Platform Suites (HDAAABAA), Model Development & Training Platforms (AutoML, Notebooks, Feature Stores) (HDAEANAB)
Keywords
Where PYCARET is headquartered
LocationHeadquarters
- HQ city
- Toronto
- HQ country
- Canada
- HQ region
- North America
Markets served
PYCARET business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Infrastructure, Operations, Others
Revenue model
- Open-source distribution: PyCaret is a fully open-source library distributed under the MIT license. There is no commercial product, subscription, or paid tier mentioned in the sources. The project appears to be community-maintained with no direct revenue generation.
Go-to-market motion1 record
Distribution channels3 records
Marketing channels4 records
PYCARET product offering
Product offeringCore offering
PyCaret is an open-source, MIT-licensed low-code machine learning library in Python that exposes a unified object-oriented API for five ML task types (classification, regression, clustering, anomaly detection, and time-series forecasting). Every fitted output is a native scikit-learn or sktime Pipeline, enabling the full AutoML loop — setup, comparison, tuning, ensembling, and deployment — in under twenty lines of code. An optional FastAPI backend (pycaret-server) and React dashboard (pycaret-ui) provide a self-hosted, multi-user enterprise ML control plane.
Product overview
PyCaret is an open-source low-code machine learning library for Python, distributed under the MIT license. The product consists of three main components: (1) the PyCaret Engine — the core Python library providing an OOP API for five ML task types (ClassificationExperiment, RegressionExperiment, ClusteringExperiment, AnomalyExperiment, TimeSeriesExperiment), all built natively on scikit-learn 1.7+ pipelines with typed result dataclasses; (2) the PyCaret Server (pycaret-server) — an optional FastAPI backend enabling multi-user platform features including workspace/project management, REST API, WebSocket live run streaming, pipeline registry, deployment serving, and 6 LLM-backed copilots; and (3) the PyCaret Dashboard — an optional React UI (Vite + TypeScript + Tailwind) providing experiment design, model comparison, diagnostic plots, and drift monitoring. Additionally, the PyCaret Plots Library offers 33 Plotly-native chart functions, and the pycaret.api introspection module provides typed APIs for UI/agent integration. The product supports installation via pip with optional extras for anomaly detection and time-series. 8M+ PyPI downloads recorded.
Differentiator
Problem solved
Functional benefit
Brands
- pycaret-server: FastAPI backend service for multi-user ML platform with workspaces, projects, experiments, runs, deployments, and LLM copilots.
- pycaret-ui
- PyCaret Dashboard
Products and services
- PyCaret Engine The core Python library providing a unified object-oriented API for five ML task types (classification, regression, clustering, anomaly detection, time-series forecasting) implemented natively on scikit-learn 1.7+ pipelines with typed result dataclasses and structured event-stream logging. Designed for data scientists and ML engineers building production machine learning workflows.
- PyCaret Server (pycaret-server) Optional FastAPI backend enabling multi-user platform capabilities including workspace and project management, run orchestration with thread-pool execution, pipeline registry, deployment serving layer, WebSocket live run streaming, audit logs, and LLM-backed advisory endpoints. Designed for data teams needing a shared ML control plane.
- PyCaret Dashboard (pycaret-ui) Optional React-based dashboard UI providing experiment design, run comparison, model cards with diagnostic plots, drift monitoring, and prediction explorer. Self-hosted and MIT-licensed, communicates with the backend via REST API and WebSocket event stream. Designed for analysts, product owners, and data teams wanting a UI on top of the engine.
- PyCaret LLM Copilots Six AI-powered advisory endpoints integrated into pycaret-server: Dataset Consultant (suggests task type), Experiment Designer (generates RunConfig from natural language), Run Explainer (narrates model results), Failure Debugger, Deployment Reviewer (pre-deploy safety check), and Drift Analyst (interprets DriftReport). Provider-agnostic via Anthropic + OpenAI router.
Quantifiable outcome
- 8M+ PyPI downloads indicating widespread adoption in the data science community
Companies that use PYCARET
Customer profileSegments2 records
Ideal customer profiles2 records
PYCARET technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
AI capability2 records
Feature8 records
PYCARET partnerships and signals
Strategic signalScale indicators1 record
Recent moves6 records
Expansion highlights5 records
PYCARET competitors and assessment
Company assessmentDirect peers
- Auto-sklearn: Open-source AutoML library built on scikit-learn that automates algorithm selection and hyperparameter tuning via Bayesian optimization. Direct technical peer to PyCaret in the open-source low-code AutoML category, targeting the same data scientist persona.
- TPOT: Open-source Python AutoML tool that uses genetic programming to optimize ML pipelines. Competes directly with PyCaret for data scientists seeking automated pipeline construction and sklearn-compatible outputs.
- FLAML: Microsoft's open-source AutoML library offering low-code ML and hyperparameter tuning across classification, regression, and forecasting. Directly comparable in API philosophy and target user, but with Microsoft institutional backing.
- EvalML: Alteryx's open-source AutoML library that automates feature engineering, model selection, and pipeline optimization. Competes with PyCaret for the same low-code AutoML workflow use cases among Python data scientists.
Broad incumbents
- H2O.ai: Established AutoML platform (H2O Driverless AI, h2o-3 open source) offering automated feature engineering, model tuning, and deployment at enterprise scale. Overlaps PyCaret's automation story but operates a much broader commercial product portfolio.
- DataRobot: Enterprise AutoML and MLOps platform with a managed control plane for model building, deployment, and monitoring. Competes for the same ML platform budget as PyCaret's enterprise dashboard vision but offers a fully managed commercial alternative.
- Amazon SageMaker (Autopilot): AWS-managed ML platform that includes SageMaker Autopilot for AutoML. Competes with PyCaret's self-hosted enterprise dashboard by offering a fully managed AutoML experience bundled into the AWS ecosystem.
- Google Vertex AI: Google Cloud's unified ML platform with AutoML capabilities, model registry, and MLOps tooling. Represents the cloud-vendor alternative to PyCaret's self-hosted ML control plane for enterprises already on GCP.
Emerging players
- MLflow: Open-source ML lifecycle platform from Linux Foundation for experiment tracking, model registry, and deployment. PyCaret 4.0 explicitly removed MLflow tracker adapters, making MLflow a complementary rather than overlapping tool in many data teams' stacks.
- Weights & Biases: ML experiment tracking and model management platform with strong adoption among ML practitioners. PyCaret 4.0 dropped W&B tracker adapters, positioning these tools as adjacent ecosystem players rather than direct competitors.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat5 records
Key risks6 records
Key highlights6 records
Customer concentration
PYCARET social profiles
Digital presencePYCARET financial estimates
Financial estimateRevenue estimate
Valuation estimate
PYCARET leadership team
Management profileNumber of profiles
PYCARET funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
PYCARET M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about PYCARET
What does PYCARET do?
PyCaret is an open-source, MIT-licensed low-code machine learning library in Python that exposes a unified object-oriented API for five ML task types (classification, regression, clustering, anomaly detection, and time-series forecasting). Every fitted output is a native scikit-learn or sktime Pipeline, enabling the full AutoML loop — setup, comparison, tuning, ensembling, and deployment — in under twenty lines of code. An optional FastAPI backend (pycaret-server) and React dashboard (pycaret-ui) provide a self-hosted, multi-user enterprise ML control plane.
Is PYCARET a public or private company?
PYCARET is a private company. It is classified as nonprofit foundation owned and is currently operating.
When was PYCARET founded?
PYCARET was founded in 2019. It employs 1 to 10 people.
Where is PYCARET based?
PYCARET is headquartered in Toronto, Canada, in the North America region.
How does PYCARET make money?
One revenue line is on record: open-source distribution.
Who are PYCARET's main competitors?
Direct peers on record are Auto-sklearn, TPOT, FLAML and EvalML. Broad incumbents are H2O.ai, DataRobot, Amazon SageMaker (Autopilot) and Google Vertex AI. Emerging players are MLflow and Weights & Biases.
Does PYCARET have an API?
Yes. PyCaret 4.0 provides a typed introspection API (`pycaret.api`) designed for the React UI and LLM agents. This API enables external processes (UI servers, LLM agents, docs generators) to query model registries, hyperparameters, setup parameters, and metrics without training models. Static functions include `list_models(TaskType.CLASSIFICATION)` returning ModelCard objects, `describe_model(task, id)` for hyperparameter details with ranges and defaults, `list_metrics(task)` for available metrics, and `describe_setup_params(task)` for React form rendering. Runtime-aware functions like `list_available_models(experiment)` detect installed optional packages. The API returns JSON-serializable dataclasses and is side-effect-free. Additionally, `pycaret-server` is a FastAPI backend that provides REST API endpoints for multi-user platform features including auth (JWT + API keys), workspace/project/experiment/run CRUD, pipeline registry, deployment serving, and LLM copilots.
What industry is PYCARET in?
PYCARET's product category is Automated Machine Learning Software. Its primary akta.pro industry code is HDAAABAL, AutoML & Low-Code ML Platform Operations, with a secondary code of HDAAABAA, End-to-End MLOps & ML Platform Suites. Its NAICS code is 54151 and its SIC code is 7372.