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Sumo Logic Announces Machine Data Analytics Platform for Logs and Metrics Data

Sumo Logic introduced a new machine data analytics platform to natively ingest, index and analyze structured metrics data and unstructured log data together in real-time.

The platform unifies logs and metrics, transforming a variety of data types into real-time continuous intelligence for modern applications and business insights.

In addition, Sumo Logic’s patented, advanced analytics technologies currently used for log data extends to time-series metrics to make correlating logs and metrics easy, instant, contextual and comprehensive. Now, Sumo Logic customers have instant access to the full analytics breadth for their modern applications – from code to end-user behaviors – to move with the speed and agility necessary to compete in today’s digital world.

“Sumo Logic set out to solve the pain of centralized log management by applying the power of distributed cloud computing, and became the machine data platform of choice for modern applications,” said Ramin Sayar, President and CEO for Sumo Logic. “Now we take another big step forward for our 1,000 plus customers and the industry by enriching the Sumo Logic service with an additional data dimension, which will result in an explosion of use cases that leverage machine learning.”

Sumo Logic’s platform handles the unique structure types of log and time-series metrics data natively – within the context of each data types’ unique form – to make log and metric analytics viewable in real-time via graphical, interactive dashboards. Log and metric data can be viewed side-by-side or contextually overlaid in an instant to speed up troubleshooting and enable digital businesses to optimize their modern applications for performance, availability, sustainability, security, customer satisfaction and new revenue-generating opportunities quickly and easily. And, all of these capabilities are available in a multi-tenant, cloud native service so customers are up and running fast with scalability and on demand elasticity, without the hassles of learning, managing, scaling and tuning on-premise analytics solutions or dealing with the rigidity of cloud-hosted analytics solutions.

Sumo Logic is a cloud-native, machine data analytics platform that transforms log and metric data into real-time continuous intelligence for richer operational, business and customer insights. The Sumo Logic platform also extends its advanced analytics technology, powered by machine learning algorithms, to time-series metrics, beginning with infrastructure metrics and, more importantly, extending to custom application metrics needed to monitor and understand operational and business KPIs generated by the application itself as users interact with it. This enables Sumo Logic users to identify patterns, anomalies and threshold outliers quickly and efficiently to address issues that impact modern application performance, availability, security and customer satisfaction.

In addition, Sumo Logic’s multi-tenant, cloud-native architecture scales with the data demands of modern applications, seamlessly handling the volume and elasticity of machine data caused by usage spikes and seasonality, such as end of month activity spike, Super Bowl commercial spurred shopping sprees or unexpected events such as distributed denial of service attacks. Machine data is analyzed in real-time so customers do not have to wait for it to be indexed and stored to start receiving value. All machine data is securely collected and stored within Sumo Logic’s cloud-native environment, with the industry’s highest security attestations and certifications, including PCI DSS 3.0.

Key features and benefits of Sumo Logic enhanced platform include:

- Full stack visibility of the entire modern application stack using all machine data sources – Graphite, AWS Cloud Watch, Docker, Chef, Apache, MySQL and more.

- Business and Operational KPIs for Development, DevOps, TechOps, Security Ops and Lines of Business teams through custom and standard out-of-the-box metrics and logs.

- Monitoring and troubleshooting with correlation of logs and metrics – single pane of glass that overlays logs, metrics and other types of machine data for easy, one-click troubleshooting.

- Powerful real-time analytics through machine learning – outliers and anomaly detection of logs and metrics for faster root cause analysis.

Sumo Logic’s enhanced platform for unifying logs and metrics is available for early access and will be generally available this summer.

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Sumo Logic Announces Machine Data Analytics Platform for Logs and Metrics Data

Sumo Logic introduced a new machine data analytics platform to natively ingest, index and analyze structured metrics data and unstructured log data together in real-time.

The platform unifies logs and metrics, transforming a variety of data types into real-time continuous intelligence for modern applications and business insights.

In addition, Sumo Logic’s patented, advanced analytics technologies currently used for log data extends to time-series metrics to make correlating logs and metrics easy, instant, contextual and comprehensive. Now, Sumo Logic customers have instant access to the full analytics breadth for their modern applications – from code to end-user behaviors – to move with the speed and agility necessary to compete in today’s digital world.

“Sumo Logic set out to solve the pain of centralized log management by applying the power of distributed cloud computing, and became the machine data platform of choice for modern applications,” said Ramin Sayar, President and CEO for Sumo Logic. “Now we take another big step forward for our 1,000 plus customers and the industry by enriching the Sumo Logic service with an additional data dimension, which will result in an explosion of use cases that leverage machine learning.”

Sumo Logic’s platform handles the unique structure types of log and time-series metrics data natively – within the context of each data types’ unique form – to make log and metric analytics viewable in real-time via graphical, interactive dashboards. Log and metric data can be viewed side-by-side or contextually overlaid in an instant to speed up troubleshooting and enable digital businesses to optimize their modern applications for performance, availability, sustainability, security, customer satisfaction and new revenue-generating opportunities quickly and easily. And, all of these capabilities are available in a multi-tenant, cloud native service so customers are up and running fast with scalability and on demand elasticity, without the hassles of learning, managing, scaling and tuning on-premise analytics solutions or dealing with the rigidity of cloud-hosted analytics solutions.

Sumo Logic is a cloud-native, machine data analytics platform that transforms log and metric data into real-time continuous intelligence for richer operational, business and customer insights. The Sumo Logic platform also extends its advanced analytics technology, powered by machine learning algorithms, to time-series metrics, beginning with infrastructure metrics and, more importantly, extending to custom application metrics needed to monitor and understand operational and business KPIs generated by the application itself as users interact with it. This enables Sumo Logic users to identify patterns, anomalies and threshold outliers quickly and efficiently to address issues that impact modern application performance, availability, security and customer satisfaction.

In addition, Sumo Logic’s multi-tenant, cloud-native architecture scales with the data demands of modern applications, seamlessly handling the volume and elasticity of machine data caused by usage spikes and seasonality, such as end of month activity spike, Super Bowl commercial spurred shopping sprees or unexpected events such as distributed denial of service attacks. Machine data is analyzed in real-time so customers do not have to wait for it to be indexed and stored to start receiving value. All machine data is securely collected and stored within Sumo Logic’s cloud-native environment, with the industry’s highest security attestations and certifications, including PCI DSS 3.0.

Key features and benefits of Sumo Logic enhanced platform include:

- Full stack visibility of the entire modern application stack using all machine data sources – Graphite, AWS Cloud Watch, Docker, Chef, Apache, MySQL and more.

- Business and Operational KPIs for Development, DevOps, TechOps, Security Ops and Lines of Business teams through custom and standard out-of-the-box metrics and logs.

- Monitoring and troubleshooting with correlation of logs and metrics – single pane of glass that overlays logs, metrics and other types of machine data for easy, one-click troubleshooting.

- Powerful real-time analytics through machine learning – outliers and anomaly detection of logs and metrics for faster root cause analysis.

Sumo Logic’s enhanced platform for unifying logs and metrics is available for early access and will be generally available this summer.

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.