
Sumo Logic has taken steps to remove complexity from data collection, improved normalization of data, and consolidated collection agents with OpenTelemetry.
Sumo Logic was purpose-built to manage and analyze data from any source. Today, Sumo Logic extends that legacy by adopting OpenTelemetry as its de facto collection strategy to remove complexity in the collection and normalization of data. With Sumo Logic Distro for OT, a native OTel solution, customers no longer face vendor lock-in and can apply Sumo Logic flexibility and analytics to their vendor of choice.
"Sumo Logic has made a commitment to its customers and the community to develop and deliver on OTel-native collection so that customers can realize value, quickly,” said Erez Barak, VP of Product Development for Observability, Sumo Logic. “The enhancements announced today will make it possible for Sumo Logic to deliver choice and flexibility while continuing to provide comprehensive infrastructure and application monitoring. The best OTel-native experiences run on Sumo Logic."
With nearly 30 apps related to database, server, or infrastructure monitoring powered by OTel, Sumo Logic Distro for OT provides a single collector for telemetry. Now, with support for Windows, customers using Sumo Logic Distro for OT can gather logs, metrics, and traces from Windows, Linux and MacOS operating systems, which can be configured utilizing the new onboarding workflows.
Sumo Logic’s unified agent makes it easier to consolidate into one platform for observability use cases instead of disparate monitoring tools for logs or APM. Through a single app installation workflow for logs, metrics and traces, Sumo Logic makes it easier for developers to harness data and takes the complexity out of deploying OpenTelemetry as a collection strategy.
Sumo Logic also reduces the number of manual steps in the data onboarding process. Each step - Collector Setup, Source Configuration, Dashboard Setup - is now folded into a single workflow allowing users to onboard data in under 5 minutes. By standardizing on OTel-native collection, developers get a simplified installation to onboard data and set up data collection on Sumo Logic.
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 ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
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.