New Relic announced plans to launch its Japanese data center.
The strategic investment will optimize New Relic’s product suite for the unique requirements of its Japanese customers and will be New Relic’s first data center in the region.
"New Relic helps some of the most prominent Japanese companies bring impactful solutions to market. In the fast-moving AI era, observability is the engine behind Japan’s legacy of high-performance technology," said New Relic CEO Ashan Willy. "Our new local data center is a strategic milestone that resolves data residency challenges, providing Japanese enterprises with the high-performance governance and data sovereignty required to maintain their global competitive edge.”
Japan is a critical, fast-growing market for New Relic. The company has held the number one position in observability market share for the past seven years. In addition to retail, manufacturing, and telecommunications, there has been an increase in customers from the financial and infrastructure and public services sector. In the past year, New Relic Japan grew its local employee base by 20 percent.
Anchoring its data center as a data residency hub, customers from highly regulated industries like finance and manufacturing will soon have control over domestic data residency through data collection, storage, and processing. Advanced compliance will provide a reliable foundation to meet Japanese security and privacy requirements, with the data center hosted entirely within Japan to meet strict data residency regulations. Its ultra-low latency will support real-time insights so customers can make rapid business decisions.
The data center, which will be located in Tokyo, will be available to all New Relic customers, with those in close proximity to Japan more likely to benefit from its performance. The data center will be available starting in July 2026.
The Latest
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...