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SevOne Launches Performance Log Appliance 2.0

SevOne unveiled the SevOne Performance Log Appliance (PLA) 2.0, delivering real-time analytics of raw log data.

A critical component of the SevOne Performance Monitoring Cluster, PLA 2.0 enables automatic correlation of real-time network and infrastructure performance metrics, flows and logs at scale. This capability is now enhanced with 10-day certification of new logs that SevOne customers have in their service-delivery environments.

“Log data has become one of the most valuable infrastructure monitoring data sources, but current log analytics solutions are siloed offerings that focus on analyzing raw log data after it’s already been collected and stored,” said Jack Sweeney, CEO at SevOne. “SevOne PLA 2.0 represents a fundamental shift in the way people think about log analytics. It transforms raw log event data into real-time operational data that can be tightly integrated with network and infrastructure performance metrics and data flows.”

PLA 2.0 offers a 10-Day certification plan for new logs to ensure end users are never left in the dark, as log formats frequently change or new devices come on the market. To convert raw logs into actionable performance log metrics, PLA 2.0 processes heavy streams of raw log data in real time, and identifies critical metrics within the data that ultimately improve visibility into the root causes of performance degradation.

Additional key benefits of the SevOne PLA 2.0 include:

- Receiving proactive alerts of customer and end-user behavioral trends

- Decreasing time to troubleshoot and repair known issues

- Understanding the impact of configuration changes on application performance

“SevOne is advancing the field of log analytics by moving beyond traditional log search. For most operations teams, log analytics can mean manual searches for relevant logs, extensive amounts of time reviewing and compiling log reports, and manually correlating performance metrics with logs across multiple tools or interfaces,” said Shamus McGillicuddy,‎ Senior Analyst, Network Management at Enterprise Management Associates. “SevOne addresses all of those problems and extracts raw log data in real time then automatically correlates it to performance events, eliminating the need for search in your process.”

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SevOne Launches Performance Log Appliance 2.0

SevOne unveiled the SevOne Performance Log Appliance (PLA) 2.0, delivering real-time analytics of raw log data.

A critical component of the SevOne Performance Monitoring Cluster, PLA 2.0 enables automatic correlation of real-time network and infrastructure performance metrics, flows and logs at scale. This capability is now enhanced with 10-day certification of new logs that SevOne customers have in their service-delivery environments.

“Log data has become one of the most valuable infrastructure monitoring data sources, but current log analytics solutions are siloed offerings that focus on analyzing raw log data after it’s already been collected and stored,” said Jack Sweeney, CEO at SevOne. “SevOne PLA 2.0 represents a fundamental shift in the way people think about log analytics. It transforms raw log event data into real-time operational data that can be tightly integrated with network and infrastructure performance metrics and data flows.”

PLA 2.0 offers a 10-Day certification plan for new logs to ensure end users are never left in the dark, as log formats frequently change or new devices come on the market. To convert raw logs into actionable performance log metrics, PLA 2.0 processes heavy streams of raw log data in real time, and identifies critical metrics within the data that ultimately improve visibility into the root causes of performance degradation.

Additional key benefits of the SevOne PLA 2.0 include:

- Receiving proactive alerts of customer and end-user behavioral trends

- Decreasing time to troubleshoot and repair known issues

- Understanding the impact of configuration changes on application performance

“SevOne is advancing the field of log analytics by moving beyond traditional log search. For most operations teams, log analytics can mean manual searches for relevant logs, extensive amounts of time reviewing and compiling log reports, and manually correlating performance metrics with logs across multiple tools or interfaces,” said Shamus McGillicuddy,‎ Senior Analyst, Network Management at Enterprise Management Associates. “SevOne addresses all of those problems and extracts raw log data in real time then automatically correlates it to performance events, eliminating the need for search in your process.”

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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.