AI Cost Optimization for AWS
One platform to see and cut spend across your entire AWS AI stack — Amazon Bedrock, SageMaker, EC2/EKS GPU, and LLM APIs. Attribute every dollar to teams, models, and experiments, then act on a prioritized savings roadmap.
Common AI Cost Challenges
Fragmented AI Spend
Amazon Bedrock, SageMaker, GPU EC2 instances, and third-party LLM APIs are each billed differently, in different places. There's no single view of what your AI workloads actually cost across the stack.
No Attribution
AWS Cost Explorer can tell you the total, but not which model, team, or experiment drove the spend. Without per-model and per-team attribution, chargeback and prioritization are pure guesswork.
Idle & Over-Provisioned Compute
Idle GPUs and inference endpoints running 24/7 at low utilization quietly burn budget. Training jobs on on-demand instances and oversized instance types leave compute and GPU memory underused.
Untracked Generative AI Spend
Generative AI and LLM token spend — across Amazon Bedrock and external APIs — grows unpredictably. Without token-level tracking, a single runaway workload can blow the monthly budget before anyone notices.
How MLCostIntel Optimizes AI Costs
MLCostIntel is full-stack AI FinOps for AWS. It connects via a read-only IAM role, ingests your AWS Cost and Usage Report, and normalizes every AI cost — Amazon Bedrock, SageMaker, EC2/EKS/ECS GPU, AWS Lambda, AWS Batch, Amazon EMR, Amazon OpenSearch Service, and Amazon S3 — to the FOCUS 1.2 specification. GPU utilization comes from Amazon CloudWatch, and LLM API spend from OpenAI and Anthropic is tracked alongside your AWS costs, so you finally see the whole picture and know exactly where to cut.
- ✓ Unified, FOCUS-normalized cost model across Amazon Bedrock, SageMaker, EC2/EKS/ECS GPU, and LLM APIs
- ✓ Per-team, per-model, and per-experiment cost attribution via tags, time-windows, and MLOps metadata
- ✓ Idle GPU and inference endpoint detection with rightsizing recommendations
- ✓ Spot and commitment (Savings Plan / Reserved Instance) purchasing recommendations
- ✓ Real-time anomaly detection on daily spend using Z-score analysis
- ✓ Cost forecasting and budgets so AI spend stays predictable month over month
How It Works
Connect Your AWS Account
Deploy a read-only IAM role via CloudFormation. No agents to install, no code changes. Setup takes less than 5 minutes.
We Analyze Your AI Spend
MLCostIntel ingests your Cost and Usage Report and unifies AI spend across Amazon Bedrock, SageMaker, EC2/EKS/ECS GPU, and LLM APIs into one FOCUS-normalized model.
Get Your Savings Roadmap
Receive prioritized recommendations with estimated savings, implementation guides, and an optimization score to track progress over time.
Where Teams Save on AI
Total AI Spend Optimized
Typical optimizable spend surfaced across the full AWS AI stack
Idle GPU & Notebook Reduction
Detect idle GPUs and notebooks running outside working hours and shut them down
Spot Training
Switch eligible training jobs to EC2 Spot and managed spot with checkpointing
Right Model & Instance Selection
Match models and instance families to actual workload profiles and utilization