Deploy ML Models at Scale
Serving ML models at scale is hard—but Anyscale is built for the challenge. Get simplified development and high-performance distributed compute—all in one framework with Ray Serve on Anyscale.
From Your Laptop to Production Ready—Seamlessly
Many Model Serving Patterns
Build end-to-end ML applications with multiple models and easy API integrations on Anyscale. Get support for complex patterns, including many-model composition, multiplexing, granular auto-scheduling, and more.
Best-in-Class Reliability
Deploy without worrying. Anyscale is production ready, with head node recovery, Multi-AZ support, and zero downtime upgrades.
Advanced Observability
We know how important visibility is, which is why we support integrations with Datadog and W&B, as well as JSON logging and persistent dashboards.
Optimized Resource Scheduling
Set up fractional resources easily to match nodes and workloads exactly. Get flexible cloud configurations and framework integrations to boost efficiency and lower costs. Plus, try Anyscale’s Replica Compaction to optimize resource use and reduce costs.
Faster, More Reliable Model Deployment
Fast Node Launching and Autoscaling
Multi-AZ support
Zero Downtime Upgrades with Incremental Rollouts
Autoscale Workers to Zero
Spot Instance Support
Bursting from On-Prem
Model Multiplexing
Model Composition
Dynamic Batching
Fractional Heterogeneous Resource Allocation
Support for Large Model Parallelism
| | | | |
|---|---|---|---|---|
Fast Node Launching and Autoscaling | – | – | – | 60 seconds |
Multi-AZ support | – | – | ||
Zero Downtime Upgrades with Incremental Rollouts | Limited | |||
Autoscale Workers to Zero | Limited | |||
Spot Instance Support | – | – | – | |
Bursting from On-Prem | – | – | – | |
Model Multiplexing | Limited | – | ||
Model Composition | Limited | – | ||
Dynamic Batching | Limited | |||
Fractional Heterogeneous Resource Allocation | – | – | ||
Support for Large Model Parallelism |
Deploy Models to Production in Moments
Ready to deploy your AI model? Enable distributed cloud computing with a single Python decorator, and scale from your laptop to any number of GPUs easily.
Fault Tolerance You Can Trust
Ensure that any issues on the back end don’t lead to downtime for your end user. With advanced observability like log search, metrics, and alerts—plus zero downtime upgrades—you can ensure your deployed model is always available.
Maximize GPU and CPU Utilization
Combine many models and business logic with separate resource requirements in one application. Anyscale supports fine-grained auto-scaling on heterogeneous hardware so you can deploy models with ease.
“We have no ceiling on scale, and an incredible opportunity to bring AI features and value to our 170 million users.”
Greg Roodt
ML Lead, Canva
Out-of-the-Box Templates & App Accelerators
Jumpstart your development process with custom-made templates, only available on Anyscale.
Deploy LLMs
Base models, LoRA adapters, and embedding models. Deploy with optimized RayLLM.
Deploy Stable Diffusion
Text-to-image generation model by Stability AI. Deploy with Ray Serve.
Ray Serve with Triton
Optimize performance for Stable diffusion with Triton on Ray Serve.
FAQs
Anyscale is the smartest place to host Ray, providing a managed experience that increases performance, optimizes utilization, and reduces costs. Anyscale improves Ray Serve runtime and scalability with advanced features like:
- Multi-AZ serve
- Replica compaction
- Optimized runtime
- Fast autoscaling (launch 1000+ nodes in 60 seconds or less)
- Unified log viewing and observability
- Serverless pools
- And much more!
A Seamless Path to Deployment
Deploy and serve models at scale with Anyscale, the smartest place to run Ray.