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Sumo Logic Introduces Sensu Integration Catalog

Sumo Logic announced a new open source offering, the Sensu Integration Catalog.

Available today on GitHub, the Integration Catalog is an open, self-service marketplace featuring over 40 turn-key integrations. Built to speed production-ready infrastructure and application monitoring, the Sensu Integration Catalog is a showcase of its commitment to the open source community and calls on developers to contribute.

“The Sensu Integration Catalog is a game-changing offering for new and existing Sensu Go users in organizations of all sizes. Adding a marketplace UX for Sensu lowers the barrier of entry by providing self-service access to infrastructure and application monitoring,” said Caleb Hailey, Senior Director of Product Management at Sumo Logic. “I'm even more pleased that we're not only launching an integration marketplace for Sensu — we managed to do it without compromising on our commitment to open source software. I'm excited to see what the Sensu Community does with an open marketplace and low-code development platform."

The Sensu Integration Catalog takes the guesswork out of deploying infrastructure and application monitoring with automated data collection of metrics and events. This reduces infrastructure monitoring maintenance and overhead by reducing the need for complex third-party configuration management, or constant modification of monitoring agent configuration files. In addition, enterprise users with proprietary applications and heightened security protocols can develop and maintain private collections of integrations in custom catalogs.

The Sensu Integration Catalog builds on its monitoring-as-code solution by adding an open marketplace and low-code development platform for infrastructure and application monitoring integrations. The Sensu Integration Catalog is made up of three key components:

- Open source integration content. 100% of the Sensu Integration Catalog contents, including both commercially supported and community-supported integrations, are available on GitHub for users to clone, fork and contribute. New integrations with customized user prompts can be added with as little code as two YAML files and a README.

- Open source integration API generator: The Sensu Integration Catalog API is a CLI tool that converts low-code integration definitions into static API content that can be hosted on any HTTP web service. It supports reproducible builds and a local development server for contributing new integrations.

- In-app integration marketplace: This is a flagship feature of the Sensu Integration Catalog allowing users to browse and deploy turn-key monitoring integrations with the push of a button.

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 Introduces Sensu Integration Catalog

Sumo Logic announced a new open source offering, the Sensu Integration Catalog.

Available today on GitHub, the Integration Catalog is an open, self-service marketplace featuring over 40 turn-key integrations. Built to speed production-ready infrastructure and application monitoring, the Sensu Integration Catalog is a showcase of its commitment to the open source community and calls on developers to contribute.

“The Sensu Integration Catalog is a game-changing offering for new and existing Sensu Go users in organizations of all sizes. Adding a marketplace UX for Sensu lowers the barrier of entry by providing self-service access to infrastructure and application monitoring,” said Caleb Hailey, Senior Director of Product Management at Sumo Logic. “I'm even more pleased that we're not only launching an integration marketplace for Sensu — we managed to do it without compromising on our commitment to open source software. I'm excited to see what the Sensu Community does with an open marketplace and low-code development platform."

The Sensu Integration Catalog takes the guesswork out of deploying infrastructure and application monitoring with automated data collection of metrics and events. This reduces infrastructure monitoring maintenance and overhead by reducing the need for complex third-party configuration management, or constant modification of monitoring agent configuration files. In addition, enterprise users with proprietary applications and heightened security protocols can develop and maintain private collections of integrations in custom catalogs.

The Sensu Integration Catalog builds on its monitoring-as-code solution by adding an open marketplace and low-code development platform for infrastructure and application monitoring integrations. The Sensu Integration Catalog is made up of three key components:

- Open source integration content. 100% of the Sensu Integration Catalog contents, including both commercially supported and community-supported integrations, are available on GitHub for users to clone, fork and contribute. New integrations with customized user prompts can be added with as little code as two YAML files and a README.

- Open source integration API generator: The Sensu Integration Catalog API is a CLI tool that converts low-code integration definitions into static API content that can be hosted on any HTTP web service. It supports reproducible builds and a local development server for contributing new integrations.

- In-app integration marketplace: This is a flagship feature of the Sensu Integration Catalog allowing users to browse and deploy turn-key monitoring integrations with the push of a button.

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.