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New Relic Launches JFrog Integration

New Relic and JFrog announced an integration that gives engineering teams a single point of access to monitor and keep software development operations running efficiently.

Using New Relic alongside JFrog provides real-time visibility into CI/CD pipelines, APIs, and web application development workflows, empowering DevOps and security leaders to quickly address software supply chain performance and security issues. The integration is available via New Relic Instant Observability, an open ecosystem of integrations with 500+ cloud services, tools, and pre-built resources designed to help every engineer embrace observability as a daily practice.

The new integration allows site reliability engineers (SREs), security, and operations teams to consistently monitor the health, security, and usage trends at every stage of the software development life cycle (SDLC). Engineering teams can track key metrics and generate alerts in New Relic to quickly identify any performance degradation, enabling administrators to proactively manage performance, mitigate risks, and remediate issues to ensure optimal uptime in one unified view.

"Today’s developers need a 360-degree view of applications to monitor and remediate both performance and security, no matter if they’re running on-premises, in the cloud, or at the edge,” said Omer Cohen, Executive Vice President of Strategy at JFrog. “Our integration with New Relic gives DevOps, security, and operations teams the real-time insights needed to optimize their software supply chain environment and accelerate time to market."

"Millions of engineers rely on JFrog to improve their software performance and security at every stage of development," said New Relic Vice President of Cloud and Product Partnerships Gal Tunik. "Together with JFrog, New Relic is making it possible for engineering teams to view observability data in tandem with their release pipelines to boost release velocity and quality. New Relic is uniquely committed to an open ecosystem approach to observability and we look forward to deepening our partnership with JFrog over the coming months."

Key benefits include:

- Unified visibility and actionable insights: Preconfigured New Relic dashboards provide a comprehensive view of their performance data, artifact usage, and security metrics from JFrog Artifactory and JFrog Xray environments alongside their telemetry data.

- Uncover and mitigate vulnerabilities: Identify urgent vulnerabilities, identify malicious users, and allow your teams to deploy fixes faster to deliver more secure software with less risk.

- Improved software supply chain performance: Proactively manage performance and remediate issues with actionable log insights, custom queries, and alerting to allow your teams to boost release velocity and quality.

The JFrog log analytics integration with New Relic is available at no additional cost to all New Relic full platform users, and the integration is free for all tiers of self-hosted JFrog customers.

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New Relic Launches JFrog Integration

New Relic and JFrog announced an integration that gives engineering teams a single point of access to monitor and keep software development operations running efficiently.

Using New Relic alongside JFrog provides real-time visibility into CI/CD pipelines, APIs, and web application development workflows, empowering DevOps and security leaders to quickly address software supply chain performance and security issues. The integration is available via New Relic Instant Observability, an open ecosystem of integrations with 500+ cloud services, tools, and pre-built resources designed to help every engineer embrace observability as a daily practice.

The new integration allows site reliability engineers (SREs), security, and operations teams to consistently monitor the health, security, and usage trends at every stage of the software development life cycle (SDLC). Engineering teams can track key metrics and generate alerts in New Relic to quickly identify any performance degradation, enabling administrators to proactively manage performance, mitigate risks, and remediate issues to ensure optimal uptime in one unified view.

"Today’s developers need a 360-degree view of applications to monitor and remediate both performance and security, no matter if they’re running on-premises, in the cloud, or at the edge,” said Omer Cohen, Executive Vice President of Strategy at JFrog. “Our integration with New Relic gives DevOps, security, and operations teams the real-time insights needed to optimize their software supply chain environment and accelerate time to market."

"Millions of engineers rely on JFrog to improve their software performance and security at every stage of development," said New Relic Vice President of Cloud and Product Partnerships Gal Tunik. "Together with JFrog, New Relic is making it possible for engineering teams to view observability data in tandem with their release pipelines to boost release velocity and quality. New Relic is uniquely committed to an open ecosystem approach to observability and we look forward to deepening our partnership with JFrog over the coming months."

Key benefits include:

- Unified visibility and actionable insights: Preconfigured New Relic dashboards provide a comprehensive view of their performance data, artifact usage, and security metrics from JFrog Artifactory and JFrog Xray environments alongside their telemetry data.

- Uncover and mitigate vulnerabilities: Identify urgent vulnerabilities, identify malicious users, and allow your teams to deploy fixes faster to deliver more secure software with less risk.

- Improved software supply chain performance: Proactively manage performance and remediate issues with actionable log insights, custom queries, and alerting to allow your teams to boost release velocity and quality.

The JFrog log analytics integration with New Relic is available at no additional cost to all New Relic full platform users, and the integration is free for all tiers of self-hosted JFrog customers.

The Latest

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 ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.