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New Relic Deepens Relationship with AWS to Provide AI Monitoring

New Relic announced that New Relic AI monitoring (AIM) is now integrated with Amazon Bedrock, a fully managed service by Amazon Web Services that makes foundation models (FMs) from leading AI companies accessible via an API to build and scale generative AI applications.

AWS customers can now use New Relic to gain greater visibility and insights across the AI stack, making it easier to troubleshoot and optimize their applications for performance, quality, and cost.

While AI is revolutionizing modern applications, it introduces new challenges and complexity to organization’s tech stacks. AI tech stacks include new components like large language models (LLMs) and vector data stores and generate additional telemetry to track such as quality and cost. AIM solves these new challenges by bringing APM to the AI stack. Similar to how engineers monitor their application stack with New Relic APM, AIM provides engineers with full visibility into all components of the AI stack. AIM provides a single easy view to troubleshoot, compare, and optimize different LLM prompts and responses for performance, cost and tokens, and quality issues including hallucinations, bias, toxicity, and fairness across all models supported by Amazon Bedrock.

AIM integrates with Amazon Bedrock to provide in-depth end-to-end observability. With AIM’s built-in integrations such as Langchain, Amazon Bedrock customers can get metrics and tracing throughout the life-cycle of LLM prompt and response, ranging from raw prompts to repaired and business-compliant responses.

Key features and use cases include:

- Auto instrumentation: New Relic agents come equipped with all AIM capabilities, including full AI stack visibility, response tracing, model comparison, and more for quick and easy setup.

- Full AI stack visibility: Holistic view across the application, infrastructure, and the AI layer, including AI metrics like response quality and tokens alongside APM golden signals.

- Deep trace insights for every LLM response: Trace the lifecycle of complex LLM responses built with tools like LangChain to fix performance issues and quality problems such as bias, toxicity, and hallucination.

- Compare performance and costs: Track usage, performance, quality, and cost across all models in a single view; optimize use with insights on frequently asked prompts, chain of thought, and prompt templates and caches.

- Enable responsible use of AI: Ensure safe and responsible AI use by verifying that responses are appropriately tagged to indicate AI-generated and are free from bias, toxicity, and hallucinations using response trace insights.

- Instantly monitor your AI ecosystem: The most comprehensive solution for monitoring the entire stack of any AI ecosystem with 50+ integrations and quickstarts including:
Orchestration framework: Langchain
Vector databases: Pinecone, Weaviate, Milvus, FAISS, Zilliz
LLM: Amazon Bedrock (models from AI21 Labs, Amazon, Anthropic, and Cohere)
AI infrastructure: Amazon SageMaker

“AI workloads are now part of modern organizations’ application architectures, and observability is essential for any company building AI applications,” said New Relic Chief Product Officer Manav Khurana. “Today’s news builds upon our deep work with AWS to bring the power of observability to engineers and developers who are modernizing their tech stacks. And by putting our AI monitoring solution front and center with AWS customers, we are multiplying our ability to reach every engineer using leading LLMs like Anthropic.”

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New Relic Deepens Relationship with AWS to Provide AI Monitoring

New Relic announced that New Relic AI monitoring (AIM) is now integrated with Amazon Bedrock, a fully managed service by Amazon Web Services that makes foundation models (FMs) from leading AI companies accessible via an API to build and scale generative AI applications.

AWS customers can now use New Relic to gain greater visibility and insights across the AI stack, making it easier to troubleshoot and optimize their applications for performance, quality, and cost.

While AI is revolutionizing modern applications, it introduces new challenges and complexity to organization’s tech stacks. AI tech stacks include new components like large language models (LLMs) and vector data stores and generate additional telemetry to track such as quality and cost. AIM solves these new challenges by bringing APM to the AI stack. Similar to how engineers monitor their application stack with New Relic APM, AIM provides engineers with full visibility into all components of the AI stack. AIM provides a single easy view to troubleshoot, compare, and optimize different LLM prompts and responses for performance, cost and tokens, and quality issues including hallucinations, bias, toxicity, and fairness across all models supported by Amazon Bedrock.

AIM integrates with Amazon Bedrock to provide in-depth end-to-end observability. With AIM’s built-in integrations such as Langchain, Amazon Bedrock customers can get metrics and tracing throughout the life-cycle of LLM prompt and response, ranging from raw prompts to repaired and business-compliant responses.

Key features and use cases include:

- Auto instrumentation: New Relic agents come equipped with all AIM capabilities, including full AI stack visibility, response tracing, model comparison, and more for quick and easy setup.

- Full AI stack visibility: Holistic view across the application, infrastructure, and the AI layer, including AI metrics like response quality and tokens alongside APM golden signals.

- Deep trace insights for every LLM response: Trace the lifecycle of complex LLM responses built with tools like LangChain to fix performance issues and quality problems such as bias, toxicity, and hallucination.

- Compare performance and costs: Track usage, performance, quality, and cost across all models in a single view; optimize use with insights on frequently asked prompts, chain of thought, and prompt templates and caches.

- Enable responsible use of AI: Ensure safe and responsible AI use by verifying that responses are appropriately tagged to indicate AI-generated and are free from bias, toxicity, and hallucinations using response trace insights.

- Instantly monitor your AI ecosystem: The most comprehensive solution for monitoring the entire stack of any AI ecosystem with 50+ integrations and quickstarts including:
Orchestration framework: Langchain
Vector databases: Pinecone, Weaviate, Milvus, FAISS, Zilliz
LLM: Amazon Bedrock (models from AI21 Labs, Amazon, Anthropic, and Cohere)
AI infrastructure: Amazon SageMaker

“AI workloads are now part of modern organizations’ application architectures, and observability is essential for any company building AI applications,” said New Relic Chief Product Officer Manav Khurana. “Today’s news builds upon our deep work with AWS to bring the power of observability to engineers and developers who are modernizing their tech stacks. And by putting our AI monitoring solution front and center with AWS customers, we are multiplying our ability to reach every engineer using leading LLMs like Anthropic.”

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...