Engineering precision for your cloud spend.
We find the committed-use discounts you're missing, the GPU cycles you're wasting, and the Kubernetes requests that don't match reality. Typical engagement: 30–40% off your monthly bill inside 6 weeks.
Core Engineering Practices
Click any optimization practice below to inspect its core mechanics, targeted savings, and production configuration code.
Cost Optimization & FinOps
Line-item bill analysis, custom tagging taxonomies, showback/chargeback engine, and unit-economics tracking for engineering teams.
CUR Parquet querying, AWS Cost Explorer API, anomaly detection alerts, cost allocation tags.
# Run CUR SQL query for untagged EC2 resources
SELECT resource_id, unblended_cost, usage_start_date
FROM cur_parquet_dataset
WHERE line_item_type = 'Usage' AND tag_environment IS NULL
ORDER BY unblended_cost DESC LIMIT 10;Parametric Savings Calculator
Adjust your infrastructure scale below to calculate real-time savings estimates derived from empirical benchmarks.
GPU Fleet Consolidation for Series B ML Platform
A Series B ML platform was provisioning full A100s for inference workloads running at 15–30% SM utilization. We profiled with DCGM exporter, configured MIG 3g.40gb partitioning, moved training to spot instances with checkpointing, and set up GPU bin-packing with Karpenter.
Read Full Case Study Dossier →Ready to see what your bill should actually look like?
Our two-week diagnostic is zero-risk: if we cannot uncover at least 15% net savings, the audit is completely free.