BigML

BigML is a machine-learning platform for analysts, developers, and scientists building predictive models and workflows. It is strongest for teams that need varied modeling methods, explainability, and deployment flexibility in one system. The $1,000/month paid entry point makes it a poor fit for buyers who need substantial capacity on a small budget.

Overview

Founded in 2011, BigML brings model building, evaluation, automation, and real-time deployment together, with access through a web platform and REST API. Its breadth can serve teams working on more than one kind of prediction; organizations that only need a single narrow tool may find the platform’s scope and paid entry cost difficult to justify.

Key features

BigML supports classification, regression, time-series forecasting, cluster analysis, anomaly detection, association discovery, and topic modeling. That range lets analysts and scientists address varied tasks within one platform. Interactive visualizations, prediction explanations, and field importances help users inspect model behavior rather than treating outputs as a black box.

Models can be exported in JSON PML and PMML for use in popular programming languages and web, mobile, or IoT applications. That portability matters when a model must move into another application; it is less useful to teams whose only requirement is in-platform analysis.

OptiML automates model selection and parameterization, while WhizzML automates workflows and Scriptify converts them into reusable scripts. These tools can reduce repetitive setup for recurring modeling work. Developers can also use the REST API and bindings for Python, Node.js, Ruby, Java, and Swift. Integrations include Node-RED, Google Sheets, Zapier, Alexa, Docker, and MacOS.

Private deployments can run on a preferred cloud provider, ISP, or behind a corporate firewall, as managed or self-managed installations. BigML says connections use HTTPS, resources are private, and its team cannot access user data without explicit consent. Resource and project permissions support access control, while immutable resources with unique IDs and creation parameters help preserve traceability.

Pricing

BigML has a free plan and a three-day trial. The free account allows up to 60 tasks, datasets up to 16 MB per task, and two parallel tasks; the trial provides up to 60 tasks or three days of full access without a credit card. Those caps make the free option suitable for limited evaluation, but not a generous route to sustained larger workloads.

PlanPriceWhat it includesFREEfree (billed $0/month)Unlimited tasks and storage, 16 MB maximum dataset size per task, two parallel tasks, one userBigML Lite$1,000 a month (billed $1,000/ month)Five users, one organization, one 8-core server, standard 8x5 email and chat support, 48-hour maximum response timeExtra Support$3,000 a month (billed $3,000/month)24x7 support, less than 8-hour response, private email and chat channel, and telephoneBronze Enterprise$45,000 a year (billed $45,000/ year + $10,000 setup fee)Up to one 8-core server, unlimited users, unlimited organizationsThe free plan’s unlimited task and storage wording does not remove its per-task dataset cap or two-task concurrency limit. BigML Lite is the paid entry point for small teams needing a server and standard support, but its five-seat and single-organization allowance is restrictive for broader deployments. Extra Support costs substantially more for round-the-clock help and faster response. Bronze Enterprise suits organizations needing unlimited users and organizations, with a sizable annual fee and setup charge. Enterprise support is customized email and chat with a 24-hour maximum response time; plans without a published price use custom pricing.

Platforms

BigML runs on the web and macOS, offers API access, and supports self-hosted deployment. The self-hosted and private deployment options are relevant to organizations that need to run workloads in their own environment rather than rely only on a hosted service.

Who it's for

BigML describes its users as analysts, software developers, and scientists, and says more than 245,000 users use the platform. It is a strong candidate for teams that need several modeling approaches, model explanations, automation, and a path to private deployment. The free account can support a small evaluation, but teams needing more capacity must weigh the $1,000/month starting price against their workload and support needs.

Pros and cons

Pros: Broad modeling coverage plus explanations and field importances helps teams build and interpret different kinds of models in one platform.

Pros: JSON PML and PMML export, API bindings, workflow automation, and private deployment give developers options for operationalizing models.

Pros: Permissions and immutable, traceable resources support governed collaboration.

Cons: The free account is constrained by 16 MB datasets, 60 tasks, and two parallel tasks, limiting its usefulness beyond evaluation.

Cons: Paid plans begin at $1,000/month, and BigML Lite allows only five users and one organization.

Alternatives

For a free plan and a self-hosted option, JADBio is worth comparing; its Basic plan includes one seat, three projects, 50 MB uploads, 500 MB storage, and one model export. Choose Amazon SageMaker Autopilot if pay-as-you-go pricing with no minimum fees or upfront commitments better fits your needs; it has no free plan. FEDOT is a free, open-source AutoML framework for buyers who want a BSD 3-Clause licensed option across Linux, macOS, Windows, API, and self-hosted environments. MLJAR AutoML offers a free MIT-licensed Python package and a free plan, making it an option for teams seeking a free alternative. AutoKeras is a free Python package installed with pip for those who prefer that form of tool. LightAutoML is a free, open-source Python library installable from PyPI under Apache License 2.0. Siemens Building X is an alternative with yearly subscriptions and quote-based pricing. AutoGluon is a free, open-source Python library under Apache 2.0.

Readers comparing categories can also browse AutoML Software and Predictive Analytics Software.

Verdict

Choose BigML if your team needs broad modeling coverage, inspectable predictions, workflow automation, and flexible deployment in one platform. Its principal advantage is the connection between modeling and operational tools; its principal drawback is the steep paid entry price alongside tight free-tier workload limits. Teams with substantial budget and varied modeling needs should consider it, while cost-conscious buyers should compare the free alternatives first.