MLCostIntel - AI/ML Cost Analytics and Optimization Platform

MLCostIntel is a cost intelligence platform purpose-built for machine learning workloads on AWS. It helps ML engineers, platform teams, and FinOps leaders understand the real cost of training jobs, GPU workloads, and large language model usage across their environments.

Generic cloud cost tools provide high-level billing visibility but lack the context needed for machine learning workloads. They cannot attribute shared GPU costs across concurrent experiments, correlate infrastructure spend with specific training runs, or break down LLM token usage by model and application. MLCostIntel bridges that gap by automatically connecting infrastructure usage with ML experiments, training runs, and model deployments - giving teams clear, experiment-level insight into what their ML workflows actually cost.

• GPU Utilization and Idle Resource Detection: Identify underutilized compute, detect idle GPU instances between experiment runs, and optimize ML infrastructure efficiency.
• SageMaker Training Job Tracking: Monitor costs at the individual training job and experiment level, with attribution that goes beyond what AWS Cost Explorer tagging provides.
• LLM and Amazon Bedrock Cost Monitoring: Analyze generative AI usage across Amazon Bedrock and LLM APIs. Track token usage and model requests to understand the real cost of running AI workloads.
• Cost Anomaly Detection: Automatically detect unexpected cost spikes across ML pipelines, enabling platform teams to triage issues before budgets are exceeded.
• Experiment-Level Cost Attribution: Connect infrastructure costs directly to ML experiments and training runs without requiring manual tagging or custom instrumentation.

A platform engineering team running hundreds of GPU-hours weekly notices a steady increase in their SageMaker bill but cannot identify the source using native AWS tools. Using MLCostIntel, they discover that a significant portion of GPU spend comes from idle notebook instances left running between experiment iterations and from over-provisioned training jobs that complete in a fraction of their allocated time. By right-sizing instances and implementing automated shutdown policies informed by MLCostIntel's utilization data, the team reclaims wasted compute spend and redirects budget toward productive experimentation.

MLCostIntel integrates with core AWS services including Amazon SageMaker, Amazon Bedrock, Amazon EC2 GPU instances, and AWS Cost Explorer. The platform reads infrastructure telemetry and billing data to provide unified cost views across your ML environment.

• Supported AWS services: Amazon SageMaker, Amazon Bedrock, EC2 GPU instances
• Requires cross-account IAM role for billing and infrastructure data access
• Works with AWS Organizations for multi-account environments

To begin using MLCostIntel, subscribe through AWS Marketplace and follow the onboarding flow to connect your AWS accounts. Contact contact@mlcostintel.com  to request a live demo or discuss a pilot engagement tailored to your ML environment.

MLCostIntel accesses billing and infrastructure metadata through scoped IAM roles with least-privilege permissions. Contact our team for detailed information about data handling practices, encryption, and compliance posture.