Scalable shared environment management for ML/AI workflows, training & experimentation

Manage reproducible, isolated, and resource-efficient shared environments for your ML/AI training and experimentation — across teams and infrastructures

Automated lifecycle and power scheduling

Role-based access and usage analytics

Multi-cloud and hybrid infrastructure support

Automated lifecycle & scheduling

Automate your shared environment provisioning, suspension, and shutdown based on activity or time slots. Kiroframe allows you to:

Schedule environment uptime and pause windows
Set expiration dates and inactivity triggers
Use power schedules to reduce cloud costs
Prevent long-running idle compute sessions

Access control & usage insights

Use granular, role-based access control (RBAC) to manage who can view, edit, or run workloads in shared environments. Kiroframe tracks environment usage across teams to help you:

Audit access and changes
Monitor utilization by user/team
Allocate compute budgets per project

RBAC and usage analytics also support security policies and audit compliance, critical for regulated industries.

Multi-cloud & toolchain integration

Kiroframe supports AWS, Azure, GCP, and hybrid deployments. Easily integrate with your MLOps stack:

Kubernetes, Docker, and Terraform support
Prebuilt templates for Databricks, Spark, etc.
Environment variables and secret management

This flexibility prevents vendor lock-in and streamlines integration with existing CI/CD and MLOps pipelines.

Scalable shared environment management for ML/AI workflows, training & experimentation

Automated lifecycle & scheduling

Access control & usage insights

Multi-cloud & toolchain integration

Supported platforms