BigML vs PyCaret
BigML
bigml.comPyCaret
pycaret.orgSide by side
| Figure | BigML | PyCaret |
|---|---|---|
| Price from | $833.33/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 7 | 2 |
| Spec score · Predictive Analytics Software | 9.2 (#1) | 8.4 (#5) |
| Company size | LinkedIn lists BigML, Inc. as a privately held software company with 11–50 employees. | LinkedIn lists PyCaret's company size as 11–50 employees. |
| Deployment | Private deployments can run on a preferred cloud provider, ISP, or on-premises behind a corporate firewall, as managed or self-managed deployments. | Models can be saved as portable scikit-learn pipelines, served behind FastAPI, or deployed through the optional pycaret-server layer. |
| Founded | 2011 | LinkedIn lists PyCaret as founded in 2020. |
| Headquarters | BigML lists Corvallis, Oregon as headquarters and Valencia, Spain as its European headquarters. | Toronto, Canada |
| Integrations | Official tools include Google Sheets, Zapier, Node-RED, Alexa, PredictServer and a native BigMLX macOS application. | PyCaret wraps or interoperates with scikit-learn, XGBoost, Microsoft LightGBM, spaCy, sktime, pyod, FastAPI, React, and Plotly. |
| Licence | The hosted BigML platform is proprietary under BigML terms, while selected bindings and libraries are open source. | PyCaret 4.0 licenses the engine under MIT and the control-plane components under BUSL-1.1 with a later Apache-2.0 change date and an additional-use grant. |
| Rating on G2 | 4.7 out of 5 from 24 reviews | 0.0 out of 5 from 0 ratings; the G2 category page provides no user-review information for PyCaret. |
| Security | BigML states that connections use HTTPS, resources are private by default, and staff cannot access customer data without explicit consent. | The overview says the self-hosted backend can be wired to existing authentication and is JWT-friendly. |
| Self-hosting | Private Deployments can run in a public cloud, private cloud or on-premises infrastructure and may be self-managed or fully managed. | The repository provides a Docker Compose installation with React web UI, FastAPI backend, SQLite data volume, and local model artifacts. |
| Support | BigML Lite includes standard 8x5 email and chat support with a 48-hour maximum response time; Enterprise lists customized email and chat with a 24-hour maximum response time. | The FAQ directs users to GitHub Discussions for questions and GitHub Issues for bug reports. |
| System requirements | Users must supply compatible hardware, software, connectivity and internet access; the service does not specify a client operating-system requirement. | PyCaret 4.0 supports Python 3.11, 3.12, and 3.13; CPU is sufficient, GPU acceleration is optional, and 4 GB memory is described as comfortable for tutorial data. |
| What it does | BigML provides cloud and deployable machine-learning workflows for classification, regression, forecasting, clustering, anomaly detection, association discovery and topic modeling. | PyCaret is a low-code Python AutoML library and self-hosted machine-learning platform. |
Plans
BigML
- FREEFree
- Basic BigML training$800, paid once
- BigML Lite$1,000 a month
- Extra Support$3,000 a month
- BigML Lite$10,000 a year
- BigML Lite (annual)$10,000 a year
- Bronze Enterprise$45,000 a year
In their own words
BigML
BigML is a machine-learning platform for classification, regression, forecasting, clustering, anomaly detection, association discovery, and topic modeling. It provides interactive model visualizations, prediction explanations, and field-importance views. Models can be exported in JSON PML or PMML formats for use in programming languages and web, mobile, or IoT applications. OptiML helps select and tune models; WhizzML automates workflows, and Scriptify turns workflows into reusable scripts. Users can work through the REST API and language bindings including Python, Node.js, Ruby, Java, and Swift. Listed integrations include Node-RED, Google Sheets, Zapier, Alexa, Docker, and macOS. BigML runs on web, macOS, API, and self-hosted platforms, with private deployments available on a cloud provider, ISP, or on-premises. The free plan is $0/month and includes up to 60 tasks, a 16 MB per-task dataset limit, and two parallel tasks. Paid plans start at $1,000/month; a three-day trial is available.