Migrating from Claude to DeepSeek without breaking everything (blog.firetiger.com)

Firetiger has successfully transitioned from using Claude's large language models (LLMs) to the open-source DeepSeek, significantly lowering costs—achieving a remarkable 62% reduction in annual spending from $606K to $231K for three agent types. This migration is noteworthy because it not only aims to alleviate financial pressures but also addresses the challenges of maintaining product quality and operational effectiveness, crucial for businesses relying heavily on LLMs for incident investigation and telemetry analysis. During the transition, Firetiger encountered various behavioral differences between Claude and DeepSeek models, leading to modifications in the way tasks were approached. For instance, DeepSeek required additional prompting to consider secondary effects of code changes and often ignored context, impacting task completion efficiency. To fine-tune performance, Firetiger implemented several prompt optimizations, enabling DeepSeek to achieve completion rates closely matching those of Claude while ensuring that operational costs aligned with expectations. By focusing on these nuances, Firetiger not only showcased the viability of migrating to cost-efficient models but also underscored the importance of thorough evaluation metrics beyond initial performance scores.