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Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
AI-focused cloud offering NVIDIA GPU compute, storage and MLOps tooling for training and inference at scale, with usage-based pricing.
AI-driven network detection and response platform that identifies and stops identity-based and lateral-movement cyberattacks in real time.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- ✦NVIDIA GPU instances (H100, H200, B200, GB200)
- ✦On-demand and preemptible GPU pricing
- ✦High-performance and object storage
- ✦Managed Kubernetes and Slurm (Soperator)
- ✦Serverless and managed inference (Token Factory)
- ✦MLOps tooling and 24/7 expert support
- ✦Commitment discounts up to 35%
- ✦Real-time AI-driven threat detection beyond traditional EDR
- ✦Detection of identity-based attacks and lateral movement
- ✦360 Response for enforced containment across identity, devices, and network
- ✦Exposure management and security posture improvement tools
- ✦Managed detection and response (MXDR/MDR) services
- ✦Integrations across existing security tool ecosystems
- ✦Attack Labs research sharing threat intelligence and techniques
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively
- →Train large AI/ML models on GPU clusters
- →Run scalable inference workloads
- →Store and manage large training datasets
- →Run Slurm/Kubernetes AI pipelines
- →Security operations teams needing detection beyond EDR/SIEM gaps
- →Enterprises defending against identity-based and hybrid cloud attacks
- →Organizations needing managed threat detection and response services
- →Finance, healthcare, and public sector teams meeting compliance-driven security needs
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements