LLM-powered applications are powerful—but without proper cost control, they quickly become unsustainable. This course focuses on engineering cost-efficient LLM systems by teaching you how to measure, optimize, and scale AI workloads profitably.

You will learn how to break down LLM pricing structures, analyze token consumption, and identify hidden inefficiencies in prompts, context, and workflows. The course goes beyond theory into production-grade optimization techniques, including prompt compression, model routing, caching strategies, batch processing, and fallback systems.

You will also explore fine-tuning economics, ROI modeling, and real-time cost monitoring, enabling you to make data-driven decisions when scaling AI products.

By the end, you will build a complete cost-optimized LLM pipeline, equipped with telemetry, dashboards, and optimization loops—ensuring your AI systems are both high-performing and financially sustainable.