SWARAJ Bodhi

Brief

SWARAJ Bodhi provides a unified operational platform for managing production-grade AI environments, addressing the challenges businesses face while scaling AI across infrastructure, workloads, and governance.

• GPU Fleet Management — Centralized management and monitoring of GPU resources.
• AI Operations (AI Ops) — Streamlined operations for AI/ML workloads and environments.
• Enterprise Governance — Centralized policies, controls, compliance, and governance.
• Cost Visibility & Intelligence — Track, analyze, and optimize AI infrastructure costs.
• Secure Architecture — Enterprise-grade security for AI infrastructure and workloads.
• GPU Intelligence — Gain insights into GPU utilization, performance, and efficiency.
• Multi-Cluster GPU Management — Manage GPU resources across multiple clusters.
• OEM-Agnostic Operations — Support and manage infrastructure across different OEM environments.
• Hybrid Deployment — Operate AI workloads across on-premises, private, and hybrid environments.
• Unified Control Plane — Centralized visibility and operational control across the entire AI environment. 

According to page 2, the platform is organized into three major operational areas: GPU Fleet Management, AI Ops, and Governance & Cost Intelligence, providing a single operational control plane for AI environments.

Overview

Highlights

• Unified AI Operations Platform for production-grade AI
• Unified Control Plane
• Hybrid Deployment
• OEM Agnostic
• Multi-Cluster GPU support
• GPU Intelligence
• Enterprise Governance
• Secure Architecture
• Cost Visibility
• Production Stability
• GPU cluster management and utilization graphs
• MIG partition management
• GPU health monitoring including ECC/XID alerts
• Node-level metrics
• End-to-end ML pipelines
• Scalable distributed training
• Experiment tracking & reproducibility
• Automated hyperparameter tuning
• Production-ready model serving
• RBAC roles
• Approval workflows
• Audit logs
• GPU-hour consumption tracking per project
• Cost dashboard / showback

Usage

Support

Operational Structure: The platform consists of three operational areas: GPU Fleet Management, AI Ops, and Governance & Cost Intelligence.

GPU Fleet Management: GPU cluster lists, utilization graphs, MIG partitions, GPU health (ECC/XID alerts), and node-level metrics.

AI Ops: End-to-end ML pipelines, scalable distributed training, experiment tracking & reproducibility, automated hyperparameter tuning, production-ready model serving, and experiment tracking & visualization.

Governance & Cost Intelligence: RBAC roles, approval workflow, audit logs, GPU-hour consumption per project, and cost dashboard/showback.

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