TL;DR - Anyscale
- Anyscale is the enterprise platform for running Ray, the distributed computing framework, at scale
- It manages infrastructure for ML training, serving, and data processing workloads
- Custom pricing based on compute usage and support requirements
Pricing: Paid only
Best for: Enterprises & professionals
Score: 82/100
Pricing Plans
free trialFree Credits
Free
$100 to start
- Full platform access
- Ray runtime
- All features
- Community support
Most Popular
Pay-As-You-Go
Usage-based
Per compute
- Spot instances
- Volume discounts
- Cost governance
- Team budgets
Enterprise
Custom
Custom pricing
- BYOC support
- Dedicated support
- SLA guarantee
- Custom contracts
About Anyscale
Anyscale makes Ray easy to use at scale. Ray parallelizes Python code across clusters, but managing that infrastructure is complex. Anyscale handles it so you can focus on your application.
Scale from your laptop to hundreds of machines without changing code. The platform handles provisioning, scaling, and monitoring. Teams ship faster because they're not fighting infrastructure.
AI teams processing large datasets or training models use Anyscale to scale their Python workloads. It removes the distributed systems expertise barrier.
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Anyscale FAQ
Anyscale is the company behind Ray, the distributed computing framework. They offer managed Ray platform for scaling Python and AI/ML workloads. Founded by Ray creators.
Anyscale uses consumption-based pricing on top of cloud compute costs. Custom pricing for enterprise. Generally expensive - designed for large-scale ML workloads.
Ray is an open-source framework for distributed computing. Scales Python code across clusters. Used for ML training, data processing, and reinforcement learning. Free to use.
Both handle distributed computing. Databricks focuses on data engineering and Spark. Anyscale focuses on Ray and ML workloads. Databricks more mature; Anyscale better for Ray-native work.
Large-scale ML training, batch inference, reinforcement learning, and data processing. Companies use it when single machines can't handle their compute needs.