New Relic AI monitoring provides comprehensive visibility into DeepSeek applications by tracking several key metrics. These metrics are crucial for optimizing performance, quality, and cost in AI applications. Here are the specific metrics and features that New Relic AI monitoring can track for DeepSeek applications:
1. Throughput and Latency: New Relic AI monitoring allows users to track throughput and latency in DeepSeek applications. This helps in understanding how efficiently the AI models are processing requests and identifying potential bottlenecks that could impact performance[1][4][7].
2. Cost Metrics: The platform tracks token usage, which is essential for managing AI development costs. By monitoring token usage, businesses can optimize their AI expenses and make informed decisions about model selection based on cost-effectiveness[1][3].
3. Model Comparison: New Relic enables the comparison of different AI models, including DeepSeek, to assess their performance and cost implications. This feature helps businesses choose the most suitable models for their applications, ensuring optimal performance and cost efficiency[1][3][9].
4. Response Quality and AI Metrics: New Relic AI monitoring provides insights into AI metrics such as response quality and token counts. It also offers a consolidated view of LLM responses, including sentiment analysis and user feedback, which helps in identifying trends and outliers in AI responses[2][5].
5. Deep Trace Insights: The platform offers deep tracing capabilities that allow users to trace the lifecycle of complex AI responses. This feature is particularly useful for debugging performance issues and quality problems such as bias, toxicity, and hallucination in AI outputs[2][8].
6. Infrastructure and Application Insights: New Relic provides a holistic view across the application, infrastructure, and AI layers. This comprehensive visibility helps in identifying issues and optimizing overall system performance[2][5].
7. Data Security and Privacy: New Relic includes features to safeguard sensitive data by selectively excluding specific data types from monitoring. This ensures compliance with privacy regulations and protects user data[2][3].
By tracking these metrics and leveraging these features, businesses can optimize their DeepSeek applications for better performance, reliability, and cost efficiency.
Citations:
[1] https://www.dqchannels.com/news/new-relic-introduces-observability-solution-for-deepseek-ai-monitoring-8689063
[2] https://docs.newrelic.com/whats-new/2024/03/whats-new-03-28-aimonitoringga/
[3] https://newrelic.com/blog/how-to-relic/deploy-deepseek-models-locally-and-monitor-with-new-relic-ai-monitoring
[4] https://ecommercenews.com.au/story/new-relic-unveils-ai-monitoring-for-deepseek-applications
[5] https://newrelic.com/blog/how-to-relic/ai-in-observability
[6] https://docs.newrelic.com/docs/ai-monitoring/explore-ai-data/view-ai-responses/
[7] https://www.itopstimes.com/ai/new-relic-creates-observability-integration-with-deepseek/
[8] https://newrelic.com/blog/nerdlog/ai-monitoring-ga
[9] https://newrelic.com/press-release/20250203
[10] https://www.computerweekly.com/news/366618774/New-Relic-extends-observability-to-DeepSeek