Model Serving
Composite AI serving
Streamline operations for complex inference services that mix models and Python code with Ray on Anyscale
Scalable processing for multi-step, online inference
Deploy multi-model, heterogeneous (CPU+GPU) inference pipelines as a single service
Deploy services with Python
Define multi-model, multi-step AI services using familiar Python, no infrastructure orchestration
Scale models of any size
Distribute anything from lightweight models to large foundation models using any inference framework
Streamline operations
Cluster autoscaling, service upgrades, blue/green rollouts, and A/B testing are handled automatically
We needed a solution that could scale horizontally with our growth while maintaining strict low-latency performance requirements for our users. Anyscale was the answer. ”
One of our applied AI engineers said - we should use this model - and the next day it was running in production. Before Anyscale, that would’ve taken a week or more. ”
Ray and Anyscale aligned with our vision: to iterate faster, scale smarter, and operate more efficiently.”
We needed a solution that could scale horizontally with our growth while maintaining strict low-latency performance requirements for our users. Anyscale was the answer. ”
3x
Faster model deployment for their multimodal search service
Unified execution for multi-model, inference workflows
Ray on Anyscale abstracts infra from complex inference services so you can focus on your application
Multi-framework support
Distributed high-performance inference with PyTorch, vLLM, SGlang, TensorRT, etc
Distributed LLM Inference
Serve massive models that span multiple nodes with automatic placement and coordination
Model multiplexing
Load multiple models into the same GPU memory and intelligently switch between them on the fly
Model composition
Chain multiple models to deliver richer, end-to-end AI applications from a unified endpoint
Independent scaling
Independently scale each component of a Ray Serve application – keeping performance high and costs low
Fractional GPU allocation
Maximize GPU utilization and reduce waste with fractional hardware allocations
Build. Run. Scale. Repeat.
Deploy advanced AI applications without growing operational complexity with Ray on Anyscale.
Explore more on Anyscale
Multimodal data pipelines
Transform complex data modalities such as video, images, voice, text, and more into AI-ready datasets
LLM Reinforcement Learning
Scale RL post-training from a single node to thousands of GPUs with veRL, skyRL and more.
Embedding generation
Process large-scale multimodal datasets for AI and applications with your model of choice