BigML
Predictive analytics software
Data science and machine learning platforms
AI data mining tools
Low-code machine learning platforms software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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$1,000 per month
Free TrialFree version
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Medium
Large
- Professional services (engineering, legal, consulting, etc.)
- Retail and wholesale
- Banking and insurance
What is BigML
BigML is a machine learning platform used to build, evaluate, and deploy predictive models from structured data. It targets data analysts, data scientists, and developers who need a managed environment for common ML workflows such as classification, regression, clustering, anomaly detection, and time series. The product provides a web UI and APIs to create datasets, engineer features, train models, and operationalize predictions. It emphasizes end-to-end ML workflow management with options for low-code usage as well as programmatic integration.
End-to-end ML workflow
BigML supports the full lifecycle from data ingestion and preparation through model training, evaluation, and deployment. It includes built-in algorithms and workflow objects (for example, datasets, models, ensembles, and evaluations) that help standardize repeatable processes. This reduces the need to assemble multiple separate tools for common predictive analytics tasks. It is well-suited to teams that want a single platform for building and serving models.
Low-code plus API access
The platform provides a browser-based interface for users who prefer guided, low-code model building. In parallel, it offers APIs and client libraries for integrating model training and prediction into applications and data pipelines. This combination supports collaboration between analysts and engineering teams. It also enables automation of recurring training and scoring jobs.
Broad set of ML methods
BigML includes multiple supervised and unsupervised learning approaches, enabling use cases beyond basic reporting. Users can apply different model families and compare results within the same environment. This helps teams iterate on model selection without switching products. The platform also supports exporting or operationalizing models for production scoring depending on the deployment approach.
Not a BI-first analytics suite
BigML focuses on machine learning workflows rather than enterprise BI dashboards and semantic-layer reporting. Organizations looking primarily for interactive visualization, governed metrics, and broad self-service analytics may need additional tools. This can increase the overall stack complexity for business-facing reporting. It is typically adopted for predictive modeling rather than as a central analytics front end.
Ecosystem depth varies
Compared with large cloud data platforms, BigML may require more integration work for organizations standardized on specific cloud-native services and data governance tooling. Data ingestion, orchestration, and monitoring often depend on external systems. Teams with complex MLOps requirements may need to complement BigML with additional components. Fit depends on existing infrastructure and preferred deployment patterns.
Advanced customization constraints
While BigML provides many built-in algorithms and configuration options, it is not designed as a fully flexible code-first environment for arbitrary custom modeling. Some advanced workflows (custom deep learning architectures, bespoke feature pipelines, or specialized training loops) may be better served by general-purpose ML frameworks. This can limit suitability for research-heavy teams. It is typically strongest for standardized predictive analytics use cases.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0 | Unlimited number of resources; max dataset size 16 MB per task; max 2 parallel tasks; 1 user included (can add users via an organization). (See BigML pricing page). |
| Standard | Not listed / Contact sales | Subscription limits: max task size 64 MB; max parallel tasks 2. (Price not published on pricing page; see support article for limits). |
| Boosted | Not listed / Contact sales | Subscription limits: max task size 1 GB; max parallel tasks 4. (Price not published). |
| Pro | Not listed / Contact sales | Subscription limits: max task size 4 GB; max parallel tasks 8. (Price not published). |
| Bronze Enterprise (Private Deployment) | $45,000/year + $10,000 setup fee | Up to 1 server / 8 cores; unlimited users and organizations; prioritized feature requests; auto-scaling; customized email/chat 24-hour max response. (Listed on pricing page). |
| BigML Lite (Private Deployment) | $10,000/year or $1,000/month | 5 users, 1 organization; 1 server (8 cores); standard 8x5 email/chat support with 48-hour max response. (Listed on pricing page). |
| Private Deployment — Single instance (example) | Total $55,000 (Customization & Configuration $10,000; License $30,000/year; Maintenance $6,000/year; Support $9,000/year) | Official example breakdown for a single-instance private deployment (support article). |
Notes: The public pricing page clearly lists Free tier and Private Deployment (Lite, Bronze) pricing. Prices for the hosted subscription tiers (Standard, Boosted, Pro, etc.) are not published on the pricing page; only their resource limits are documented in BigML support articles.
Seller details
BigML, Inc.
Corvallis, Oregon, USA
2011
Private
https://bigml.com/
https://x.com/bigmlcom
https://www.linkedin.com/company/bigml/
