New Relic announced New Relic AI Monitoring with a set of new features to meet the evolving needs of organizations developing AI applications.
New features include detailed AI response tracing insights with real-time user feedback and model comparisons to ensure continued performance, quality, and cost of AI applications while ensuring data security and privacy. promote improvement.
With over 60 integrations, New Relic AI Monitoring helps organizations discover the root causes of AI application problems faster, accelerate AI adoption, and support you at every stage of your AI journey.
Arnie Lopez, chief customer officer at New Relic, said: “Based on conversations with CIOs, CTOs, and executives across our customer base, it’s clear that every company is considering how to leverage AI to grow their business.” IT and technology leaders look to New Relic to confidently navigate the exciting future of AI and optimize performance, quality, and cost. Because observability is essential to managing and ultimately delivering a great customer experience.”
Organizations are keen to implement AI to provide better digital experiences to their customers. According to Gartner, by 2026, more than 80% of enterprises will use generative AI APIs or deploy generative AI-enabled applications. Demand for AI is strong and growing, but organizations struggle to incorporate it into their technology stacks due to its complexity. I'd like to introduce_______
New Relic AI Monitoring directly addresses this challenge and helps organizations manage the complexity of their AI stack by providing a unified view of their entire AI ecosystem along with the rest of their performance data.
The main features are:
- automatic instrumentation: New Relic agents provide easy setup for popular frameworks like OpenAI, AWS Bedrock, and LangChain across Python, Node.js, Ruby, Go, and .NET languages.
- Complete visibility into your AI stack: A holistic view across application, infrastructure, and AI layers, including AI metrics such as request count, response time, and token usage.
- AI response view with end-user feedback: Quickly identify trends and outliers in AI responses, analyze sentiment, and see user feedback in a single unified view.
- Detailed trace insight into every response: Use tools like LangChain to trace the lifecycle of AI responses and fix performance and quality issues such as bias, toxicity, and hallucinations.
- Enhanced data security: Maintain your organization's security and compliance policies by excluding sensitive data (PII) in AI requests and responses from monitoring.
- Model comparison: Compare the performance and cost of base models running in production in a single view and choose the model that best suits your needs.
- Quickstart integration: One of the most comprehensive solutions for monitoring AI ecosystems, integrating critical components such as NVIDIA GPUs and 60 vector databases such as Pinecone and Weaviate.
