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New Relic Announces New Log Management Capabilities

New Relic added new capabilities to its log management feature:

- The ability to detect patterns and outliers in log data

Now out of beta and broadly available, you can reduce troubleshooting time by using machine learning to detect clustered patterns to surface outliers in log data automatically. This capability enables you to quickly find patterns to reduce noise and create queries, alerts, and dashboards based on those patterns for deeper analysis.

- Simple, more intuitive analytics

New Relic made significant changes in the Logs UI. For example, you can efficiently find and use advanced features directly from the main screen. You’ll find more UI space dedicated to logs, making it easier to see the details and debug faster. This updated UI experience also includes colorization of log levels, a message summary, expanded dashboard visualization for displaying long log messages directly in dashboards, and more.

- Partitioning of data any way you want for fast search performance

Massively scaled search performance based on data partitioning provides flexibility to segment data in a way that makes sense for your teams, use cases, or logical groupings. You can optimize searches against high volumes of data to search one or multiple partitions and quickly return the results needed.

- Easier log onboarding

New Relic made it even easier to get log data into New Relic One. The recently released guided install enables you to find additional logs in common locations that will help expand your visibility while also making it easier to onboard higher volumes of data. New Relic also added Native Heroku cloud support to expand out-of-box support for one of the most common cloud sources used by developers. And they added an option to bring in data without needing to install or maintain any third-party software, with agentless syslog support through our TCP endpoint.

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New Relic Announces New Log Management Capabilities

New Relic added new capabilities to its log management feature:

- The ability to detect patterns and outliers in log data

Now out of beta and broadly available, you can reduce troubleshooting time by using machine learning to detect clustered patterns to surface outliers in log data automatically. This capability enables you to quickly find patterns to reduce noise and create queries, alerts, and dashboards based on those patterns for deeper analysis.

- Simple, more intuitive analytics

New Relic made significant changes in the Logs UI. For example, you can efficiently find and use advanced features directly from the main screen. You’ll find more UI space dedicated to logs, making it easier to see the details and debug faster. This updated UI experience also includes colorization of log levels, a message summary, expanded dashboard visualization for displaying long log messages directly in dashboards, and more.

- Partitioning of data any way you want for fast search performance

Massively scaled search performance based on data partitioning provides flexibility to segment data in a way that makes sense for your teams, use cases, or logical groupings. You can optimize searches against high volumes of data to search one or multiple partitions and quickly return the results needed.

- Easier log onboarding

New Relic made it even easier to get log data into New Relic One. The recently released guided install enables you to find additional logs in common locations that will help expand your visibility while also making it easier to onboard higher volumes of data. New Relic also added Native Heroku cloud support to expand out-of-box support for one of the most common cloud sources used by developers. And they added an option to bring in data without needing to install or maintain any third-party software, with agentless syslog support through our TCP endpoint.

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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 ...

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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 ...