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Execution-fabric infrastructure that splits AI/compute workloads into units and packs GPUs to cut cost and raise utilization.
Cloud deployment platform for developers that auto-detects code and frameworks to ship apps, servers, and AI-hub services with one push.
Open-source, encrypted web terminal sharing tool letting people collaborate live on one command line via a browser link.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
Local deep packet inspection and network intelligence giving businesses full visibility into application, VPN and Tor traffic.
No public pricing
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No public pricing
No public pricing
- ✦Decomposes workloads into small routable units
- ✦Packs GPUs/CPUs to raise utilization
- ✦Works with Kubernetes, SLURM, CUDA and ROCm
- ✦Runs on cloud, on-prem and edge
- ✦Sits beneath existing orchestration with no rewrite
- ✦Automatic language and framework detection and deployment
- ✦Git-push CI/CD with zero configuration
- ✦Auto-scaling compute resources
- ✦Built-in object storage similar to S3
- ✦One-click managed VPS purchase
- ✦Unified AI Hub API for multiple AI models
- ✦Domain and DNS management
- ✦In-browser file management console
- ✦One-command installation and session sharing via link
- ✦End-to-end encryption so the server cannot read terminal data
- ✦Multiplayer infinite canvas for arranging multiple terminals
- ✦Live cursors and chat for real-time collaboration
- ✦Cross-platform CLI for macOS, Linux and Windows
- ✦Distributed mesh networking for low-latency global connections
- ✦Python-native dynamic workflow authoring
- ✦Automatic failure recovery, caching, and versioning
- ✦Zero Trust architecture keeping data inside customer's cloud
- ✦Real-time inference and agentic-AI workflow support
- ✦High-throughput scaling (tens of thousands of actions per run)
- ✦Local development environment matching production behavior
- ✦Local deep packet inspection agent
- ✦Application identification data feeds
- ✦VPN and Tor IP datasets
- ✦Network informatics and analytics
- ✦Developer documentation
- →Increasing GPU cluster utilization
- →Cutting AI/compute infrastructure cost
- →Speeding up training and inference workloads
- →Getting more from existing hardware without migration
- →Developers deploying apps without manual server config
- →Teams wanting predictable, fixed-plan hosting costs
- →Startups needing quick CI/CD pipelines
- →Projects needing bundled AI model access alongside hosting
- →Pair debugging a remote server with a teammate
- →Teaching command-line skills over a shared live session
- →Sharing a CI/CD pipeline terminal for troubleshooting on GitHub Actions
- →Providing temporary cloud access without exposing SSH credentials
- →ML teams orchestrating training and inference pipelines at scale
- →Biotech/geospatial companies needing GPU-heavy pipeline orchestration
- →Enterprises migrating off Airflow for ML workflow management
- →Teams requiring workflows that never send data outside their own cloud
- →Classify application traffic on a network
- →Detect VPN and Tor usage
- →Feed DPI data into security and analytics tools
