Today’s systems can have more than 50GB daily logs per 100-node cluster

New capabilities create a proactive, consistent flow of crucial insights when and where engineers need them, all in a trusted and controlled environment

SAN FRANCISCO, November 04, 2025--(BUSINESS WIRE)--NEW RELIC NOW—New Relic, the Intelligent Observability company, announced Logs Intelligence, a series of AI-strengthened capabilities that automate the time and effort required to reduce mean time to resolution (MTTR) and extract critical insights from logs. Featured key innovations, like AI Log Alerts Summarization, transform how teams work with log data by providing automated analysis to generate a rapid hypothesis, dramatically accelerating time to root cause understanding and incident response.

"Modern distributed systems and AI tools generate logs at an unprecedented rate," said New Relic CTO Siva Padisetty. "Traditional log management, the backbone of troubleshooting but a source of immense complexity and manual effort, becomes infinitely challenging at this scale. With Logs Intelligence, New Relic turns the wall of unstructured data into actionable insights — accelerating incident response, reducing MTTR, and freeing teams to focus on higher-value work."

“Bringing Snowflake to SAP Business Data Cloud empowers our customers with openness and choice,” said Irfan Khan, President and Chief Product Officer for SAP Data and Analytics, SAP SE. “Together, we combine SAP’s decades of leadership in mission-critical business applications with Snowflake’s modern data platform to deliver a unified, enterprise-ready, and SAP-supported experience that extends the value of business data across the entire ecosystem.”

AI Log Alerts Summarisation

One feature included with Logs Intelligence is AI Log Alerts Summarisation. This tool activates when an alert is triggered from log data, automatically analysing relevant logs and highlighting dominant error trends. It then generates a summary aimed at clarifying the cause of technical issues, providing an actionable hypothesis for teams to start resolution efforts.

Rather than simply notifying engineering teams of an incident, the system offers a concise explanation as to why the incident occurred and presents this assessment directly within their usual workflow. The goal is to help engineers resolve issues more quickly by minimising the time spent trawling through extensive logs during urgent periods.

New Relic says by handing off analytical burdens to AI, engineering time and attention can be directed towards applying fixes and maintaining system performance, instead of manual data review.

Today’s systems can have more than 50GB daily logs per 100-node cluster, 10,000-plus log lines per transaction across services, and verbose model inference logs that burden AI workloads. Instead of engineers manually correlating logs and hunting for root causes, intelligent observability can automatically identify patterns, predict issues before they become outages, and provide instant context during incidents — ultimately increasing system stability and business uptime. Now a capability of New Relic’s Intelligent Observability Platform, New Relic Logs Intelligence transforms a manual process into an automated, insight-driven analysis model.

Instead of engineers manually relating logs and hunting for root causes, intelligent observability can automatically identify patterns, predict issues before they become outages, and provide instant context during incidents -- ultimately increasing system stability and business uptime. Now a capability of New Relic's Intelligent Observability Platform, New Relic Logs Intelligence transforms a manual process into an automated, insight-driven analysis model. AI Log Alerts Summarisation provides an immediate "why" for remediation: When a system alert is triggered on log data, the race to identify the root cause begins -- often forcing engineers to wade through reams of log data under intense time pressure.

New Relic's Fine Grain Access Control for Logs gives organizations precise, policy-driven control over who can access specific log data without forcing them to compromise on observability. Security and compliance teams can enforce granular permissions that align with organizational policies, while engineers retain the ability to troubleshoot effectively using the full power of logs-in- context.

By - Aaradhay Sharma