Skip to main content

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.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...