How to reduce costs for LLMs using Braintrust

Braintrust offers a comprehensive solution for managing and reducing costs associated with large language models (LLMs) in production environments by providing detailed insights into token usage and associated expenses at the span level of a trace. This allows engineering and product teams to identify specific cost drivers, such as inefficient prompts, model choices, and tool calls, and experiment with cost-effective alternatives without compromising output quality. By attaching estimated costs and token counts to every span, Braintrust enables targeted cost investigations and optimizes workflows through prompt and model experiments, ensuring that each change is validated for quality through CI/CD evaluations before reaching users. Additionally, Braintrust facilitates continuous cost optimization by converting production findings into reusable evaluation cases, supporting long-term efficiency improvements. Notable companies like Notion, Stripe, and Zapier have adopted Braintrust to enhance their AI workflows, with features like the Playground for prompt experimentation, model comparisons, and automated evaluation processes that maintain output quality while reducing expenses.

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