Deploy DeepSeek models locally and monitor with New Relic AI Monitoring

The AI landscape is rapidly advancing with the introduction of models like DeepSeek-R1, Deepseek-V3, Kimi K-1.5, and Qwen-2.5-max, which are democratizing AI development by making advanced technologies more accessible and affordable. This evolution raises questions about the suitability and impact of different models for specific applications, necessitating robust monitoring tools to provide actionable insights into performance, quality, and cost. New Relic has expanded its AI monitoring capabilities to include DeepSeek models, offering developers the ability to leverage these open-source models' efficiency and innovative architecture while maintaining comprehensive application visibility. DeepSeek models, particularly the DeepSeek-R1, are noted for outperforming leading AI models in reasoning tasks at a lower cost due to their mixture-of-experts architecture, which activates only relevant submodels for specific tasks. The article guides users through setting up DeepSeek models locally and integrating them with New Relic AI monitoring, enabling real-time performance evaluation and model comparison to optimize AI application functionality. Additionally, it highlights the security benefits of locally hosted models and explains how New Relic's monitoring can exclude sensitive data, further enhancing privacy and security for AI applications.