Anyscale
Fully managed Ray platform for building and running AI workloads with pay as you go compute, autoscaling clusters, GPU utilization tools and $100 get started credit.
Pricing Pay as you go
Category data
Difficulty Beginner
Type Web App
Status Active
BigML
End to end machine learning platform with GUI and REST API that covers data prep modeling evaluation deployment and governance for cloud or on premises use.
Pricing Free trial, contact sales
Category data
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in Anyscale
raydistributedtraininginferencegpuautoscaling
Shared
None
Only in BigML
machine-learningautomlapideploymentsgovernancecloud
Key Features
Anyscale
- • Managed Ray clusters with autoscaling and placement policies
- • High GPU utilization via pooling and queue aware scheduling
- • Model serving endpoints with rolling updates and canaries
- • Ray compatible APIs so existing code ports quickly
- • Observability and cost tracking across jobs and users
- • Environment images with Python CUDA and dependency control
BigML
- • GUI and REST API for the full ML lifecycle with reproducible resources
- • AutoML and ensembles
- • Time series anomaly detection clustering and topic modeling
- • WhizzML to script and share pipelines
- • Versioned immutable resources
- • Organizations with roles projects and dashboards
Use Cases
Anyscale
- → Scale fine tuning and batch inference on pooled GPUs
- → Port Ray pipelines from on prem to cloud with minimal edits
- → Serve real time models with canary and rollback controls
- → Run retrieval augmented generation jobs cost efficiently
- → Consolidate ad hoc notebooks into governed projects
- → Share clusters across teams with quotas and budgets
BigML
- → Stand up a governed ML workflow
- → Automate repeatable training and evaluation with WhizzML
- → Detect anomalies for risk monitoring
- → Forecast demand with time series
- → Cluster customers and products
- → Embed predictions through the REST API
Perfect For
Anyscale
ml engineers data scientists and platform teams that want Ray without managing clusters and need efficient GPU utilization with observability and controls
BigML
Data scientists, analytics engineers, and ML platform teams who want a standardized GUI plus API approach to build govern and deploy models
Capabilities
Anyscale
Managed Clusters Professional
Model Endpoints Intermediate
Utilization and Cost Intermediate
Enterprise Controls Intermediate
BigML
AutoML and Models Professional
Pipelines with WhizzML Professional
Cloud or Private Enterprise
Versioning and Roles Professional
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