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Reduce cloud spending across AWS, Azure, GCP, and OCI through rightsizing, reserved capacity, and cost governance. \n \n Covers four optimization pillars: visibility (tagging, dashboards, alerts), rightsizing (utilization analysis, auto-scaling), pricing models (reserved instances, spot/preemptible, savings plans), and architecture patterns (serverless, managed services, tiered storage) \n Includes cloud-specific strategies: AWS reserved instances and savings plans (30–72% savings), Azure hybrid

Analyze Azure resources and IaC files to identify cost optimizations, creating tracked GitHub issues for implementation. \n \n Discovers Azure resources across subscriptions and resource groups, analyzes IaC files (Bicep, Terraform, ARM templates), and collects usage metrics from Log Analytics to validate current costs \n Generates evidence-based optimization recommendations with priority scoring based on monthly savings, implementation effort, and risk assessment \n Creates individual GitHub is

Intelligent model routing, budget tracking, and retry logic to optimize LLM API costs without sacrificing quality. \n \n Routes requests to cheaper models (Haiku) for simple tasks and expensive models (Sonnet, Opus) only when complexity thresholds are met, reducing spend by 3–19x on routine work \n Tracks cumulative API costs with immutable dataclasses, enforces budget limits, and fails early to prevent overspend \n Implements narrow retry logic that retries only on transient errors (network, ra