Why your LLM bill exploded overnight (and how to regain control)

Organizations using Large Language Models (LLMs) in production face challenges with unexpected cost spikes, often driven by factors like retries, agent loops, and prompt bloat. These issues are compounded by provider opacity and architectural patterns that make it difficult to detect cost spikes until it is too late. To manage these costs, it is recommended to route LLM traffic through a centralized boundary where retry budgets, token caps, and cost-aware routing can be enforced. By monitoring unit metrics such as tokens per request and retry rates, organizations can quickly identify and address cost spikes. Additionally, implementing guardrails such as retry budgets, circuit breakers, token limits, and cost-aware model routing can help control expenses. WunderGraph's Cosmo provides tools to manage these challenges by centralizing control and enforcing policies at a single boundary, thus preventing financial risks and optimizing LLM usage.

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