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New Relic Integrates with NVIDIA NIM

New Relic is integrating its platform with NVIDIA NIM inference microservices to reduce the complexity and costs of developing, deploying, and monitoring generative AI (GenAI) apps.

Customers can use New Relic AI monitoring to gain broad visibility across the AI stack for applications built with NVIDIA NIM, all with a simplified setup and enhanced data security. This complements the security features and ease of use of NVIDIA NIM’s self-hosted models, which accelerates generative AI application delivery. Together, New Relic integrated with NVIDIA NIM can help customers adopt AI faster and achieve quicker ROI.

“In today’s hyper-competitive market, organizations cannot afford to wait years for AI ROI,” said New Relic CEO Ashan Willy. “Observability solves this by providing visibility across the AI stack. We are pioneering AI observability by extending our platform to include AI apps built with NVIDIA NIM. Combining NVIDIA’s AI technology with our expertise in observability and APM gives enterprises a competitive edge in the AI race."

“As enterprises race to adopt generative AI, NVIDIA NIM can help businesses quickly deploy applications in production,” said NVIDIA Director of AI Software Amanda Saunders. “New Relic’s integration with NVIDIA NIM enables IT and development teams to optimize their AI applications by rapidly observing and responding to operational insights.”

New Relic AI monitoring provides a broad view of the AI stack, along with key metrics on throughput, latency, and costs while ensuring data privacy. It also traces the request flows across services and models to understand the inner workings of AI apps. New Relic extends its in-depth monitoring to NVIDIA NIM, supporting a wide range of AI models including–Databricks DBRX, Google's Gemma, Meta's Llama 3, Microsoft's Phi-3, Mistral Large and Mixtral 8x22B, and Snowflake's Arctic. This helps organizations deploy AI applications built with NVIDIA NIM confidently, accelerate time-to-market, and improve ROI.

Key features and use cases for AI monitoring include:

- Full AI stack visibility: Spot issues faster with a view across apps, NVIDIA GPU-based infrastructure, AI layer, response quality, token count, and APM golden signals.

- Deep trace insights for every response: Fix performance and quality issues like bias, toxicity, and hallucinations by tracing the lifecycle of AI responses.

- Model inventory: Easily isolate model-related performance, error, and cost issues by tracking key metrics across NVIDIA NIM inference microservices in one place.

- Model comparison: Compare the performance of NVIDIA NIM inference microservices running in production in a single view to optimize model choice based on infrastructure and user needs.

- Deep GPU insights: Analyze critical accelerated computing metrics such as GPU utilization, temperature, and performance states; understand context and resolve problems faster.

- Enhanced data security: In addition to NVIDIA’s self-hosted model’s security advantage, New Relic allows you to exclude monitoring of sensitive data (PII) in your AI requests and responses.

This integration follows New Relic's recent addition to NVIDIA’s AIOps partner ecosystem. Leveraging NVIDIA AI’s accelerated computing, New Relic combines observability and AI to streamline IT operations and accelerate innovation through its machine learning, and generative AI assistant, New Relic AI. New Relic offers the most expansive observability solution with 60+ AI integrations including NVIDIA GPUs and NVIDIA Triton Inference Server software.

New Relic AI monitoring is available as part of its all-in-one observability platform and offered via its usage-based pricing model.

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New Relic Integrates with NVIDIA NIM

New Relic is integrating its platform with NVIDIA NIM inference microservices to reduce the complexity and costs of developing, deploying, and monitoring generative AI (GenAI) apps.

Customers can use New Relic AI monitoring to gain broad visibility across the AI stack for applications built with NVIDIA NIM, all with a simplified setup and enhanced data security. This complements the security features and ease of use of NVIDIA NIM’s self-hosted models, which accelerates generative AI application delivery. Together, New Relic integrated with NVIDIA NIM can help customers adopt AI faster and achieve quicker ROI.

“In today’s hyper-competitive market, organizations cannot afford to wait years for AI ROI,” said New Relic CEO Ashan Willy. “Observability solves this by providing visibility across the AI stack. We are pioneering AI observability by extending our platform to include AI apps built with NVIDIA NIM. Combining NVIDIA’s AI technology with our expertise in observability and APM gives enterprises a competitive edge in the AI race."

“As enterprises race to adopt generative AI, NVIDIA NIM can help businesses quickly deploy applications in production,” said NVIDIA Director of AI Software Amanda Saunders. “New Relic’s integration with NVIDIA NIM enables IT and development teams to optimize their AI applications by rapidly observing and responding to operational insights.”

New Relic AI monitoring provides a broad view of the AI stack, along with key metrics on throughput, latency, and costs while ensuring data privacy. It also traces the request flows across services and models to understand the inner workings of AI apps. New Relic extends its in-depth monitoring to NVIDIA NIM, supporting a wide range of AI models including–Databricks DBRX, Google's Gemma, Meta's Llama 3, Microsoft's Phi-3, Mistral Large and Mixtral 8x22B, and Snowflake's Arctic. This helps organizations deploy AI applications built with NVIDIA NIM confidently, accelerate time-to-market, and improve ROI.

Key features and use cases for AI monitoring include:

- Full AI stack visibility: Spot issues faster with a view across apps, NVIDIA GPU-based infrastructure, AI layer, response quality, token count, and APM golden signals.

- Deep trace insights for every response: Fix performance and quality issues like bias, toxicity, and hallucinations by tracing the lifecycle of AI responses.

- Model inventory: Easily isolate model-related performance, error, and cost issues by tracking key metrics across NVIDIA NIM inference microservices in one place.

- Model comparison: Compare the performance of NVIDIA NIM inference microservices running in production in a single view to optimize model choice based on infrastructure and user needs.

- Deep GPU insights: Analyze critical accelerated computing metrics such as GPU utilization, temperature, and performance states; understand context and resolve problems faster.

- Enhanced data security: In addition to NVIDIA’s self-hosted model’s security advantage, New Relic allows you to exclude monitoring of sensitive data (PII) in your AI requests and responses.

This integration follows New Relic's recent addition to NVIDIA’s AIOps partner ecosystem. Leveraging NVIDIA AI’s accelerated computing, New Relic combines observability and AI to streamline IT operations and accelerate innovation through its machine learning, and generative AI assistant, New Relic AI. New Relic offers the most expansive observability solution with 60+ AI integrations including NVIDIA GPUs and NVIDIA Triton Inference Server software.

New Relic AI monitoring is available as part of its all-in-one observability platform and offered via its usage-based pricing model.

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From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

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In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

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