Anyscale

What Anyscale does

Anyscale provides a production-scale AI platform powered by Ray, the world's most widely adopted AI compute engine. It helps teams build and run AI workloads with speed, reliability, and cost-efficiency, offering solutions for distributed training, multimodal data curation, embedding generation, and LLM inference. The platform supports scaling existing AI libraries across thousands of nodes, provides developer tooling, workload-aware observability, and cluster orchestration. Anyscale offers both hosted and Bring Your Own Cloud (BYOC) deployment options, enabling users to maximize GPU utilization and manage costs with a pay-as-you-go approach or committed contracts. It aims to make scalable computing effortless for developers and teams, allowing them to focus on innovation rather than infrastructure bottlenecks.

Products

• Anyscale PlatformA platform for building and running AI workloads at production-scale with speed, reliability, and cost-efficiency, built on Ray. It includes developer tooling, workload-aware observability, and cluster orchestration.
• RayThe AI compute engine for every AI workload and use case, allowing developers to scale Python code elastically across hundreds of nodes or GPUs on any cloud with minimal changes. It handles distributed execution, autoscaling, and fault tolerance.
• Ray ServeAn ML library for model deployment and serving, optimized by Anyscale for improved performance, reliability, and scale. It supports complex patterns like many-model composition, multiplexing, and granular auto-scheduling.
• Ray TuneAn ML library for hyperparameter tuning, supported and optimized by Anyscale for experiment execution and hyperparameter tuning at any scale, integrating with various ML frameworks and optimization tools.
• Anyscale RuntimeA fully managed, Ray-compatible runtime supported by Ray experts, designed for building faster and running with confidence in production without vendor lock-in.

Key capabilities

• Distributed training
• Multimodal data curation
• Embedding generation
• LLM inference and training on post-training frameworks
• Elastic scaling across GPU clusters
• Last-mile data preprocessing
• GPU observability
• Python APIs for scaling AI libraries
• Open Source foundation (Ray)
• Pay-as-you-go pricing
• Committed contracts with volume discounts
• Hosted deployment (fully managed infrastructure)
• Bring Your Own Cloud (BYOC) deployment
• Deployment on VMs or Kubernetes
• Data residency options (Anyscale-managed or customer VPC)
• Anyscale-hosted compute or use existing GPU reservations
• Usage-based billing
• Parallelize Python with minimal code changes
• Distributed libraries for ML workloads
• Autoscaling for variable workload needs
• Fault-tolerant execution
• Scalable machine learning libraries (Ray Tune, Ray Serve)
• Developer experience with cluster-backed VS Code/Jupyter and managed dashboards
• Workspaces for building, debugging, and deploying AI on scalable Ray clusters
• Workload-specific observability dashboards with persistent logs
• Production-grade managed Ray clusters for data, training, and serving
• Lineage tracking for pipeline transparency
• Multi-cloud deployment and management with a single control plane
• Cross-cloud, priority-aware scheduling
• Real-time and persisted monitoring of Ray cluster health and utilization
• Access controls, authentication (SSO, SAML, SCIM), and audit logs for governance
• Budgets for usage attribution and spend quotas
• Head node recovery
• Multi-AZ support
• Zero downtime upgrades with incremental rollouts
• Fast node launching and autoscaling (60 seconds)
• Spot instance support
• Bursting from on-prem
• Model multiplexing
• Model composition
• Dynamic batching
• Fractional heterogeneous resource allocation
• Support for large model parallelism
• Log search, metrics, and alerts for fault tolerance
• Replica Compaction for optimizing resource use
• Elastic training with minimal interruption from spot instance preemption
• Job retries and fault tolerance support
• Detailed training dashboard
• Autoscaling development environment
• Distributed debugger
• Experiment tracking integrations
• Alerting for training jobs
• Resumable jobs
• Priority scheduling and job queues
• EFA support
• Custom model training
• Heterogeneous compute (CPUs and GPUs in same pipeline)
• Framework-agnostic model serving
• LLM serving features (response streaming, dynamic request batching, multi-node/multi-GPU serving)
• Cutting-edge optimization algorithms for hyperparameter tuning
• Multi-GPU and distributed training for hyperparameter searches
• Integration with various hyperparameter optimization tools (Ax, Optuna)
• Logs results to tools like Weights & Biases, MLflow, and TensorBoard
• Multiple storage options for experiment results (NFS, cloud storage)

Use cases

• Building and scaling Foundation Models
• Large-scale pipelines for curating and preparing multimodal data (videos, images, text, audio)
• Distributed model training across GPU clusters
• Batch embedding generation for search, retrieval, or training
• LLM inference and training
• Optimizing distributed training, data curation, and batch inference pipelines
• Scaling existing AI libraries like PyTorch, vLLM, SGLang, and XGBoost
• Parallel processing of Python applications
• Hyperparameter tuning
• Deep learning
• Model serving
• Reinforcement learning
• Data loading
• Building and running distributed applications
• Scaling LLMs and Generative AI models
• Detection of geospatial anomalies
• Real-time recommendation
• Deploying ML models at scale
• End-to-end ML applications with multiple models and API integrations
• Deploying base models, LoRA adapters, and embedding models with RayLLM
• Deploying Stable Diffusion models with Ray Serve
• Optimizing performance for Stable Diffusion with Triton on Ray Serve
• Training high-quality models on all available data
• Executing end-to-end LLM workflows
• Fine-tuning personalized Stable Diffusion XL models
• Pre-training Stable Diffusion V2 models
• Training large time-series forecasters
• Fraud prevention and security (e.g., detecting suspicious logins, preventing account takeovers, blocking fraudulent transfers)
• Risk assessment (e.g., evaluating transaction risk, monitoring external wallet safety)
• Customer experience (e.g., AI-powered chatbots, support systems)
• Processing massive quantities of video data for media generation models
• Processing complex, heterogeneous robotics logs and sensor data
• Distributed data processing for adaptive robotics models
• Scaling data processing across large compute clusters
• Running experiments in parallel

AI approach

Anyscale provides a platform for building, running, and optimizing AI workloads at production scale, powered by Ray, an open-source AI compute engine. It focuses on distributed training, data curation, batch inference, and model serving for various AI applications, including large language models and generative AI. The platform enables scaling of existing AI libraries and frameworks across thousands of nodes and supports multimodal data processing.

Tech named: Ray, PyTorch, vLLM, SGLang, XGBoost, TorchTrainer, SentenceTransformer, SkyRL, veRL, LLM, Ray Tune, Ray Serve, TensorFlow, scikit-learn, StatsForecast, Ax, Optuna, Keras, RayLLM, Stable Diffusion, Triton

Industries served

• AI/ML
• Generative AI
• Robotics
• Fintech
• Media Creation
• Ecosystem Restoration

What it says sets it apart

• Powered by Ray, the world's most widely adopted AI compute engine, co-created by Anyscale founders.
• Offers a fully managed solution for Ray, offloading DevOps burden.
• Seamlessly scales from laptop to cloud clusters with minimal code changes.
• Supports a wide range of AI/ML workloads including Foundation Models, LLMs, and multimodal data processing.
• Provides advanced features for distributed training, model serving, and hyperparameter tuning.
• Offers flexible deployment options: Hosted and Bring Your Own Cloud (BYOC).
• Focuses on maximizing GPU utilization and cost efficiency with usage-based billing and optimization features like Replica Compaction.
• Provides enterprise-grade reliability with fault tolerance, autoscaling, and zero downtime upgrades.
• Integrates with a vast ecosystem of popular AI/ML libraries, frameworks, data platforms, and observability tools.
• Direct access to Ray experts for support and guidance.
• Proven track record with leading AI teams and significant performance improvements cited by customers (e.g., 15x more jobs at same cost, 8x faster data transformation for Coinbase; 13x faster model loading for Runway; ~50% reduction in LLM inference costs for Samsara).