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Sumo Logic App for MongoDB Released

Sumo Logic announced the availability of its Sumo Logic App for MongoDB to provide an in-depth view into the operational health and performance of MongoDB deployments.

This visibility enables the flexibility to monitor, optimize and secure modern applications powered by modern database engines that today’s digital businesses require.

With the Sumo Logic App for MongoDB, users can gain global oversight of their complete application infrastructure from a single interface. Issues that risk affecting customer experience can be quickly identified and isolated to specific components – whether attributable to devices, hardware infrastructure, networks, APIs, application code, databases and more. The new integration also helps speed application development and deployment for modern software delivery lifecycles.

Sumo Logic’s cloud-native machine data analytics platform delivers real-time, continuous intelligence across the entire application lifecycle and stack enabling organizations to build, run and secure their modern applications. As one of the fastest growing databases in the world, MongoDB enables modern applications with near instant access to data that is essential in the day-to-day running of successful digital businesses. The new integration with MongoDB’s NoSQL database – which allows users to rapidly store and access massive volumes of structured and unstructured data in a scalable way – will provide deep visibility into MongoDB deployments, alongside the rest of the technology stack. The Sumo Logic App for MongoDB is simple to set up and achieve efficiency out of the box, giving developers working with complex large-scale deployments access to:

- Operational Insights: Developers can receive a complete overview of their application and MongoDB deployment on a single dashboard. Users can monitor the overall health of their deployment, with deeper visibility into operational performance, security and query optimization to support complex deployments pinpointing problems to enable deep understanding of issues in the larger context of the entire application stack.

- Integrated with Operational Tooling: If the operations team needs finer grained telemetry into MongoDB, they can drill down into the 100+ system metrics maintained by MongoDB Ops Manager and Cloud Manager.

- Security Monitoring: Mitigate security threats by identifying issues with failed logins and geo-location of clients using the Sumo Logic App for MongoDB.

“The shift to the cloud has created an opportunity for companies such as Sumo Logic and MongoDB to capitalize on the complex and changing requirements of mission-critical applications,” said Alan Chhabra, VP of Partners at MongoDB. “We are excited about the Sumo Logic App for MongoDB as it further deepens the ecosystem for our customers to ensure the health of their cloud infrastructure.”

Sumo Logic provides real-time monitoring, troubleshooting and root cause analysis and advanced analytics through machine learning to identify patterns and anomalies so developers can uncover problems and predict potential issues before they impact customers.

“Today’s IT teams are struggling with getting a deep, holistic view into the health of their database deployments, so they can more monitor performance, diagnose problems and audit access to identify potential risks,” said Bruno Kurtic, founding VP of Product and Strategy. “The Sumo Logic App for MongoDB provides a modern solution built on machine data analytics that integrates with MongoDB environments to provide actionable intelligence in a unified, content-aware view across all applications and supporting infrastructure.”

The Sumo Logic App for MongoDB is available for free to all Sumo Logic customers.

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Sumo Logic App for MongoDB Released

Sumo Logic announced the availability of its Sumo Logic App for MongoDB to provide an in-depth view into the operational health and performance of MongoDB deployments.

This visibility enables the flexibility to monitor, optimize and secure modern applications powered by modern database engines that today’s digital businesses require.

With the Sumo Logic App for MongoDB, users can gain global oversight of their complete application infrastructure from a single interface. Issues that risk affecting customer experience can be quickly identified and isolated to specific components – whether attributable to devices, hardware infrastructure, networks, APIs, application code, databases and more. The new integration also helps speed application development and deployment for modern software delivery lifecycles.

Sumo Logic’s cloud-native machine data analytics platform delivers real-time, continuous intelligence across the entire application lifecycle and stack enabling organizations to build, run and secure their modern applications. As one of the fastest growing databases in the world, MongoDB enables modern applications with near instant access to data that is essential in the day-to-day running of successful digital businesses. The new integration with MongoDB’s NoSQL database – which allows users to rapidly store and access massive volumes of structured and unstructured data in a scalable way – will provide deep visibility into MongoDB deployments, alongside the rest of the technology stack. The Sumo Logic App for MongoDB is simple to set up and achieve efficiency out of the box, giving developers working with complex large-scale deployments access to:

- Operational Insights: Developers can receive a complete overview of their application and MongoDB deployment on a single dashboard. Users can monitor the overall health of their deployment, with deeper visibility into operational performance, security and query optimization to support complex deployments pinpointing problems to enable deep understanding of issues in the larger context of the entire application stack.

- Integrated with Operational Tooling: If the operations team needs finer grained telemetry into MongoDB, they can drill down into the 100+ system metrics maintained by MongoDB Ops Manager and Cloud Manager.

- Security Monitoring: Mitigate security threats by identifying issues with failed logins and geo-location of clients using the Sumo Logic App for MongoDB.

“The shift to the cloud has created an opportunity for companies such as Sumo Logic and MongoDB to capitalize on the complex and changing requirements of mission-critical applications,” said Alan Chhabra, VP of Partners at MongoDB. “We are excited about the Sumo Logic App for MongoDB as it further deepens the ecosystem for our customers to ensure the health of their cloud infrastructure.”

Sumo Logic provides real-time monitoring, troubleshooting and root cause analysis and advanced analytics through machine learning to identify patterns and anomalies so developers can uncover problems and predict potential issues before they impact customers.

“Today’s IT teams are struggling with getting a deep, holistic view into the health of their database deployments, so they can more monitor performance, diagnose problems and audit access to identify potential risks,” said Bruno Kurtic, founding VP of Product and Strategy. “The Sumo Logic App for MongoDB provides a modern solution built on machine data analytics that integrates with MongoDB environments to provide actionable intelligence in a unified, content-aware view across all applications and supporting infrastructure.”

The Sumo Logic App for MongoDB is available for free to all Sumo Logic customers.

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...