Skip to main content

Sumo Logic Expands Observability Suite

Sumo Logic has broadened the Sumo Logic Observability suite, powered by its Continuous Intelligence Platform™, with new and expanded solutions to provide enterprises with a unified view of real-time analytics across application and infrastructure logs, metrics, traces and metadata.

New additions to the suite include the Sumo Logic AWS Observability Solution and the Sumo Logic Software Development Observability Solution, along with new tracing capabilities added to its existing Microservices Observability Solution. These new and expanded solutions further position Sumo Logic as the intelligence layer for modern application and cloud environments to drive a unified view of real-time analytics across operations, security, business and customer use cases.

"Observability is making the transition from being a niche concern to a mainstream approach for user experience, systems and service management in startups, SaaS and enterprise companies. Change rather than stability is the goal and there is a lot more uncertainty in systems and applications than there used to be," said James Governor, founder for RedMonk. "Sumo Logic is building tools designed to support this new culture and the platforms associated with it."

Sumo Logic is expanding its observability suite by adding distributed transaction tracing capabilities and three new suite solutions that unify application and infrastructure logs, metrics, traces and metadata and enable sophisticated analytics on both structured and unstructured data. These solutions targeted toward developers, cloud architects, site reliability engineers, DevSecOps, security teams and more and include: Sumo Logic AWS Observability, Sumo Logic Software Development Observability and Microservices Observability.

Sumo Logic’s new Distributed Transaction Tracing, enables customers to monitor and troubleshoot transaction execution and performance across a distributed application environment. These new tracing capabilities are fully integrated with logs, metrics, metadata in order to provide a seamless end-to-end experience during the process of managing and responding to production incidents, and designed to reduce downtime by streamlining root cause analysis. Sumo Logic Tracing, currently in closed beta, supports the OpenTelemetry standard and leverages open source componentry from the Cloud Native Computing Foundation (CNCF) to collect distributed tracing data. Sumo Logic has been an active member of CNCF since 2018.

New updates to the Sumo Logic Microservices Observability solution continue to build on capabilities for monitoring and troubleshooting Kubernetes platform and custom applications. These updates include expanded metrics collection from application components and infrastructure. The solution enables hierarchical, metadata based topology navigation, contextual drill down from signals, to traces, to logs across application, platform and infrastructure in order to enable rapid diagnosis and troubleshooting of production issues. Solution leverages open source components such as Prometheus, FluentD and FluentBit to integrate into Kubernetes platform and collect data and enables “single click” deployment using Helm charts.

Now generally available, the Sumo Logic AWS Observability solution for AWS takes a cross-cutting approach to managing reliability of AWS services by collecting, unifying and analyzing telemetry data from popular AWS services like Application Load Balancer, Amazon elastic Cloud Compute (EC2), Amazon Relational Database (RDS), AWS Lambda, Amazon DynamoDB, and Amazon API Gateway in order to detect anomalous events, determine timeline and scale of anomalies, and enable root cause analysis through machine learning aided technology. An innovative user experience approach enables navigation through the AWS hierarchy using metadata to enable customers to easily explore their multi-account and multi-region AWS deployments. With the Global Intelligence Service for AWS CloudTrail, enterprises can also benchmark the behavior or their own usage of many of these services against AWS peer user groups to monitor efficiency, detect misconfigurations and security exposure.

Sumo Logic is extending its Software Development Observability solution to support GitHub, Jenkins and PagerDuty, enabling development organizations to continuously benchmark and optimize their software development performance by automatically correlating data across their CI/CD pipelines. The solution already integrates with leading development tools like Jira, Bitbucket, OpsGenie and can be set up in minutes to help teams collaborate more effectively and release secure, high quality code faster. The Sumo Logic Software Development Observability solution leverages the KPI methodology developed by the DevOps Research and Assessment (DORA) organization to automatically derive industry standard metrics backed by actionable insights and raw logs that are specifically designed to give teams complete visibility and observability of the entire DevOps lifecycle.

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.

Sumo Logic Expands Observability Suite

Sumo Logic has broadened the Sumo Logic Observability suite, powered by its Continuous Intelligence Platform™, with new and expanded solutions to provide enterprises with a unified view of real-time analytics across application and infrastructure logs, metrics, traces and metadata.

New additions to the suite include the Sumo Logic AWS Observability Solution and the Sumo Logic Software Development Observability Solution, along with new tracing capabilities added to its existing Microservices Observability Solution. These new and expanded solutions further position Sumo Logic as the intelligence layer for modern application and cloud environments to drive a unified view of real-time analytics across operations, security, business and customer use cases.

"Observability is making the transition from being a niche concern to a mainstream approach for user experience, systems and service management in startups, SaaS and enterprise companies. Change rather than stability is the goal and there is a lot more uncertainty in systems and applications than there used to be," said James Governor, founder for RedMonk. "Sumo Logic is building tools designed to support this new culture and the platforms associated with it."

Sumo Logic is expanding its observability suite by adding distributed transaction tracing capabilities and three new suite solutions that unify application and infrastructure logs, metrics, traces and metadata and enable sophisticated analytics on both structured and unstructured data. These solutions targeted toward developers, cloud architects, site reliability engineers, DevSecOps, security teams and more and include: Sumo Logic AWS Observability, Sumo Logic Software Development Observability and Microservices Observability.

Sumo Logic’s new Distributed Transaction Tracing, enables customers to monitor and troubleshoot transaction execution and performance across a distributed application environment. These new tracing capabilities are fully integrated with logs, metrics, metadata in order to provide a seamless end-to-end experience during the process of managing and responding to production incidents, and designed to reduce downtime by streamlining root cause analysis. Sumo Logic Tracing, currently in closed beta, supports the OpenTelemetry standard and leverages open source componentry from the Cloud Native Computing Foundation (CNCF) to collect distributed tracing data. Sumo Logic has been an active member of CNCF since 2018.

New updates to the Sumo Logic Microservices Observability solution continue to build on capabilities for monitoring and troubleshooting Kubernetes platform and custom applications. These updates include expanded metrics collection from application components and infrastructure. The solution enables hierarchical, metadata based topology navigation, contextual drill down from signals, to traces, to logs across application, platform and infrastructure in order to enable rapid diagnosis and troubleshooting of production issues. Solution leverages open source components such as Prometheus, FluentD and FluentBit to integrate into Kubernetes platform and collect data and enables “single click” deployment using Helm charts.

Now generally available, the Sumo Logic AWS Observability solution for AWS takes a cross-cutting approach to managing reliability of AWS services by collecting, unifying and analyzing telemetry data from popular AWS services like Application Load Balancer, Amazon elastic Cloud Compute (EC2), Amazon Relational Database (RDS), AWS Lambda, Amazon DynamoDB, and Amazon API Gateway in order to detect anomalous events, determine timeline and scale of anomalies, and enable root cause analysis through machine learning aided technology. An innovative user experience approach enables navigation through the AWS hierarchy using metadata to enable customers to easily explore their multi-account and multi-region AWS deployments. With the Global Intelligence Service for AWS CloudTrail, enterprises can also benchmark the behavior or their own usage of many of these services against AWS peer user groups to monitor efficiency, detect misconfigurations and security exposure.

Sumo Logic is extending its Software Development Observability solution to support GitHub, Jenkins and PagerDuty, enabling development organizations to continuously benchmark and optimize their software development performance by automatically correlating data across their CI/CD pipelines. The solution already integrates with leading development tools like Jira, Bitbucket, OpsGenie and can be set up in minutes to help teams collaborate more effectively and release secure, high quality code faster. The Sumo Logic Software Development Observability solution leverages the KPI methodology developed by the DevOps Research and Assessment (DORA) organization to automatically derive industry standard metrics backed by actionable insights and raw logs that are specifically designed to give teams complete visibility and observability of the entire DevOps lifecycle.

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.