Start your 14-day free trial and discover how Kiroframe helps streamline your ML workflows, automate your MLOps flow, and empower your engineering team.
Start your 14-day free trial and discover how Kiroframe helps streamline your ML workflows, automate your MLOps flow, and empower your engineering team.

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

Thank you for your request!

We will be in touch soon.

We respect your privacy. See our Privacy Policy. You can unsubscribe at any time.

shared usage of environments in Kiroframe
automated lifecycle and scheduling in Kiroframe

Automated lifecycle and power scheduling

role-based access

Role-based access and usage analytics

multi-cloud and hybrid infrastructure

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

Automated-lifecycle-gif
Role-based-access-gif

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.

Multi-cloud-Toolchain-Integration-gif

Supported platforms

aws
ms azure logo
google cloud platform
Alibaba Cloud Logo
Kubernetes
databricks-image
PyTorch-image
kubeflow-image
TensorFlow-image
spark-apache