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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.
Free cloud cost-optimization platform that pools buying power to give startups enterprise-level AWS, GCP, and Azure discounts.
Agentic AI SRE using dynamic code analysis to find, root-cause, and remediate code and infrastructure issues before production.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
No public pricing
Free trial available
No public pricing
Free trial available
Free trial available
- ✦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
- ✦Automated cloud spend optimization
- ✦Group buying for enterprise discounts
- ✦Cost visibility and insights dashboards
- ✦Coverage across AWS, GCP, and Azure
- ✦No-cost service model
- ✦Dynamic Code Analysis engine
- ✦Automated root-cause analysis and remediation
- ✦Pull-request and config fix suggestions
- ✦MCP server for AI-assisted code review
- ✦Observability and data-source integrations
- ✦Runs locally or on-prem/private cloud
- ✦150+ recommendations across 50+ AWS services
- ✦Zombie and unused resource cleanup
- ✦Over-provisioned rightsizing
- ✦Idle-resource scheduler
- ✦SpotBot for ECS Fargate spot/on-demand switching
- ✦AWS console extension with Slack/Teams alerts
- →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
- →Reducing startup cloud bills
- →Automating reserved-capacity savings
- →Gaining visibility into multi-cloud spend
- →Accessing enterprise pricing without scale
- →Reducing incident resolution time
- →Catching performance issues pre-production
- →Enhancing AI code reviews with runtime data
- →Monitoring microservice performance
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
