Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The unified compute platform for scaling AI and Python applications from laptop to cloud.
Anyscale is the commercial platform developed by the creators of Ray, the open-source unified framework for distributed Python. As of 2026, Anyscale has solidified its position as the premier orchestration layer for Generative AI, enabling organizations to scale compute-intensive workloads without the operational overhead of managing Kubernetes or raw cloud instances. Its architecture provides a seamless bridge from local development to massive-scale production, specifically optimized for LLM fine-tuning, large-scale batch inference, and reinforcement learning. The platform's core strength lies in its ability to dynamically manage resources across various cloud providers (AWS, GCP), utilizing spot instances and diverse GPU hardware to minimize the Total Cost of Ownership (TCO) for AI operations. By providing a unified interface for data ingestion (Ray Data), model training (Ray Train), and low-latency serving (Ray Serve), Anyscale eliminates the 'silos' of the traditional ML lifecycle, allowing for faster iteration cycles and a more robust path to production for enterprise AI initiatives.
Enables the hosting of multiple models on a single cluster with independent scaling policies per model.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatically handles spot instance preemption by migrating state to available nodes without job failure.
Integrated Prometheus and Grafana dashboards for real-time monitoring of Ray actors and tasks.
Rolling updates for Ray Serve applications ensuring constant availability during model swaps.
Interactive cloud IDEs that maintain state between sessions, allowing teams to collaborate on the same cluster.
Serverless API for running popular LLMs like Llama 3/4 and Mistral optimized for high throughput.
Offloads GPU memory to system RAM or disk when limits are reached to prevent Out-of-Memory (OOM) errors.
Local GPUs lack the VRAM required to fine-tune 70B+ parameter models.
Registry Updated:2/7/2026
Monitor loss curves in the Anyscale dashboard.
Single-server Python applications cannot handle 50,000 requests per second with sub-100ms latency.
Searching thousands of combinations of trading parameters is too slow on sequential hardware.