New Relic signed a definitive agreement to acquire Pixie Labs, a next generation machine intelligence observability solution for developers using Kubernetes.
Pixie dramatically simplifies the process of troubleshooting and live debugging applications in Kubernetes environments by providing instant access to telemetry data without the need to manually add instrumentation to the code.
The anticipated addition of Pixie will expand New Relic’s opportunity to serve the rapidly growing Kubernetes market, and drive the acceleration of observability across organizations of every size.
Pixie Labs’ technology significantly simplifies observability for Kubernetes environments. With Pixie, telemetry data runs entirely inside Kubernetes at the edge. This first-of-its-kind approach provides telemetry to developers with lower overhead, latency, and costs. New Relic plans to integrate the best of Pixie use cases with New Relic One, where customers will gain access to powerful features, including advanced correlation and alerting with Applied Intelligence, advanced visualization and analytics with Full Stack Observability, and extended data retention and compliance through Telemetry Data Platform. Pixie’s technology will complement New Relic’s powerful Kubernetes observability features in New Relic One available today, including the Kubernetes Cluster Explorer.
More than 300 engineering teams are using the Pixie beta today at companies ranging from startups to enterprises running internet-scale Kubernetes clusters.
“At New Relic, we believe that every developer in the world should have observability as part of their toolkit, so they can easily visualize and troubleshoot their entire software stack,” said New Relic CEO and Founder Lew Cirne. “As Kubernetes rapidly becomes the default environment for deploying and managing software in the cloud, we're doubling down on our Kubernetes strategy with the acquisition of Pixie Labs. Our goal is to make Pixie and New Relic One ubiquitous to the millions of developers responsible for building and deploying applications in Kubernetes environments.”
“We founded Pixie Labs to build a magical developer experience that redefines how developers explore, monitor, secure, and manage their applications. And the team at New Relic shares our developer-first approach,” said Zain Asgar, Co-founder/CEO of Pixie Labs and an Adjunct Professor of Computer Science at Stanford University. “Joining forces with New Relic will enable us to scale the Pixie platform faster and accelerate our ability to deliver on that vision for developers.”
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...