AI workload cost optimization is more complex than traditional cloud management because costs are driven by expensive GPUs (often 10–20x CPU costs), token-based usage, distributed architectures, and unpredictable demand. Many organizations overspend due to hidden factors such as idle or poorly scheduled GPUs, repeated model training and experimentation, and the “context window tax,” where conversation history increases token volume. Effective optimization starts with unified visibility that connects infrastructure telemetry, AI-specific metrics (token usage, model activity, inference load), and financial data. With this alignment, teams can identify waste, rightsize resources, deallocate idle capacity, detect anomalies early, forecast spend accurately, and continuously balance performance

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