As businesses rapidly integrate AI-driven solutions into their operations, advanced observability has emerged as a critical factor for ensuring operational efficiency, cost-effectiveness, and seamless digital experiences. In this exclusive conversation, Rajeev Ranjan, Editor at Digital Terminal, engages with Nic Benders, Chief Technology Strategist, New Relic to explore the dynamic shifts in monitoring, governance, and optimization within today’s digital landscape. Their discussion delves into the remarkable growth witnessed in 2024, the transformative role of AI in observability, and the strategies enterprises can deploy to navigate the evolving challenges of this technology era.
Rajeev: How has New Relic performed in 2024, and what key factors have contributed to its growth and market position?
Nic: We’ve seen incredible traction in the breadth and depth of customers that we have acquired since we officially launched our Indian business in 2020 and have continued to maintain that momentum throughout 2024 and beyond. Along with our incredible Indian customers, many of the large multinational customers that we work with across the finance, automotive, technology, and manufacturing sectors, have innovation centers here in India, so we’ve been able to run tailored training sessions, hackathons, and events to help further enable our customers to get the best out of our platform.
In a nutshell, our growth can be attributed to a number of factors; investment in the platform via industry leading product innovations that meet our customers needs; investment in our people via the significant expansion of our Hyderabad and Bangalore offices; and of course the growing need for observability across digital organisations of all sizes to improve operations, reduce cloud costs, enhance customer experiences, and gain valuable insights.
Rajeev: With the rapid adoption of AI in enterprises, how is New Relic positioning itself to lead in AI-powered observability?
Nic: As enterprises adopt AI technologies, they face the increased uncertainty from working with any new set of tools, plus the additional difficulty from the high cost, rapid development pace, and unpredictability of AI. To overcome these challenges, those enterprises will need deep visibility into their AI components, and an Observability tool which can pull together data from previously disconnected places and evolve rapidly right alongside the AI tech.
To help our customers through this journey, we are investing in instrumentation for AI technologies, AI powered features in our platform, and the use of AI for faster development of new features. Underpinning everything in AI is the need for data. Comprehensive, high scale, and with rich metadata. New Relic's extensive instrumentation and the power of the New Relic Database (NRDB) put us in a great position. But this is a fast moving field, and just like everyone else, we will need to keep going fast to keep up. Our platform is what makes that possible. When New Relic's AI Monitoring was released a year ago, we were the first in the market to give customers visibility into their LLM usage. Since then we have also been the first to support NVIDIA NIM and now DeepSeek AI.
Rajeev: As organizations strive to become AI-first, what are some of the biggest challenges they face in terms of monitoring, governance, and optimization?
Nic: This is an industry built on disruption. SaaS, microservices, containerization, public cloud, Kubernetes, each one of these and more have radically changed the jobs of software developers and operators over the past few decades. So while AI is new, I find it is helpful to look back at previous disruptions for what we might expect. When we look back at the public cloud for example, there are a few key lessons that we can learn. The biggest is that we are still in the early days of AI. Amazon released S3 and EC2 in 2006, but it took years to have an impact. Wherever AI takes us, we probably aren't thinking about it yet.
Public cloud also showed us that if you give people a new tool, they'll use a lot of it, and if that usage costs money, this can get very expensive very fast. Balancing the ability to innovate against costs means needing visibility into both what you are paying for and what you are getting, both in terms of technical output and in the business results of that system. Did the customer get their problem resolved? Did your AI recommendation turn into a sale? Turning AI spend into quantifiable business value will be a major topic at many companies over the next few years.
Rajeev: The rise of cost-effective AI has led to a surge in demand for observability. Can you explain the connection between affordability and the growing need for advanced monitoring?
Nic: When the DeepSeek R1 model came out this year, suddenly everyone was talking about a century-and-a-half old economics idea called the "Jevons Paradox", which notes that when the efficiency of a technology improves, its usage goes up so much that the total resource usage (or cost) goes up. This is on prominent display with AI, where each new innovation driving cost down results in wider and wider usage.
When AI was expensive, it was a research project. Something you might put on the side, but would keep away from your main customer base for fear of costing a fortune. But as AI has become more affordable and generally widespread, companies are putting it into those critical business paths. This also increases the scrutiny on the AI components. When in that critical path, every response has to be good, it has to be fast, and it has to work. Observability is the key tool for all of those aspects with any technology, and AI is no different.
Rajeev: What sets New Relic’s AI-powered observability apart from traditional monitoring solutions?
Nic: At New Relic, we think of the observability industry as having three different "eras". The first was the Instrumentation Era. During this time, enterprises had limited visibility into their software and observability providers competed to build the best agents and integrations. In this era, New Relic was the first SaaS observability provider, and the first APM for Ruby, PHP, .NET, Python and Node.js. The first monitoring that ran inside the end-user browser with Javascript, and the first performance monitoring for mobile applications on iOS and Android.
Next came the Data Platform Era. This is where most of the industry is today. Once enterprises had everything instrumented, they had a new problem: they needed somewhere to put all of that data, along with the dashboarding and alerting tools which could query it. New Relic released the first high-cardinality database for Observability with NRDB in 2014. Today, NRDB receives exabytes of data annually and regularly runs queries for results across billions of records. Getting to that point meant going beyond only our own instrumentation, and supporting direct submission of OpenTelemetry, Prometheus, and other open formats too.
However, just as comprehensive instrumentation created the need for a powerful data platform, now enterprises have so much data from so many different sources that the question is no longer "can you quickly run this query", but rather "where do I even start?". The era of data platforms, dashboards and alerts has taken us as far as it can.
Going forward, we’ll be entering the Intelligence Era. AI is a key piece of this new era, but it isn't the only part. Our job as an observability provider is not to give you data, but actually to give you answers. Our intelligent tools combine the vast amounts of data in the New Relic platform with other important information, such as team structure, and unstructured information like incident retro docs. AI "Agents", based on our own experience building and running a massive multi-tenant platform, use all of that information to work alongside human software developers and incident responders to help solve problems faster. This isn't just monitoring with a chat interface, it is a fundamentally new way of working.
Rajeev: What’s next for New Relic in the AI-powered observability space?
Nic: We recently announced 20 new product innovations that weave AI intelligence across every corner of our platform. These products span everything from security, to digital experience monitoring, agentic orchestration, FinOps, data pipeline controls and more. Our overall vision is to make intelligent observability mainstream, and ultimately help businesses eliminate digital interruptions for their customers. Some of our recently announced AI-strengthened innovations include:
Transaction 360: Leveraging AI, this provides a unified view of business-critical transactions so enterprises can quickly identify and fix the root cause of incidents.
Service architecture intelligence: Simplifies service, infrastructure, incident and quality management by consolidating critical knowledge into customizable catalogs, scorecards, teams and maps.
Cloud cost intelligence: Delivers insights into multi-cloud cost trends, drivers, and impacts so teams can manage current and future cloud investment strategically.
New agentic AI integrations with ServiceNow and Google Gemini: These integrations build upon our expanding AI integration ecosystem that includes GitHub Copilot and Amazon Q Business. These integrations bring our critical observability data and intelligent recommendations across the business and tech ecosystem.
AI-powered predictions: leverages machine learning algorithms to analyze historical data, identify patterns, and forecast time-series metrics within a singular interface to anticipate problems before they occur.
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