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Sysdig Monitor 3.0 Announced

Sysdig announced Sysdig Monitor 3.0.

Sysdig Monitor 3.0 delivers enterprise-grade Prometheus monitoring.

As enterprises move toward modern infrastructure to support digital transformation initiatives, visibility into the performance of dynamic, distributed microservices becomes a significant challenge. Successful monitoring of business-critical applications and infrastructure based on cloud-native solutions like Kubernetes and Docker requires a new approach. Enterprises in early-phase Kubernetes projects often use open source Prometheus to research the performance of their applications on this new platform. As these organizations transition to full-scale production, there are additional requirements for broader telemetry and event data, long-term data retention, high availability, and access control. Sysdig delivers these capabilities to help enterprises deliver efficient, secure applications at enterprise scale and accelerate time-to-value with Prometheus.

“We love Prometheus and the tremendous monitoring value it adds, which is why we have started to contribute to the open source project in addition to providing Prometheus capabilities for enterprise customers,” said Loris Degioanni, CTO and founder of Sysdig. “Sysdig has embraced Prometheus to provide users with another source of rich data alongside our existing kernel instrumentation. With Sysdig Monitor 3.0, enterprise customers see the whole picture and can resolve issues more quickly. ”

Enterprise-grade Prometheus with Sysdig Monitor 3.0 focuses on five key areas:

- Scale: Provides a horizontally scalable distributed system platform that handles tens of millions of metrics per second with cross-cluster aggregation to keep pace with large, complex environments.

- Scope: Collects, analyzes, and correlates Prometheus metrics with granular metrics and events for system processes, applications, cloud platforms, networks, orchestrators, and customer metrics like StatsD and Java TM Management Extensions (JMX), with advanced visualizations like topology maps.

- Simplicity: Reduces complexity with a turn-key solution that eliminates the headaches of managing multiple isolated monitoring systems and services.

- Security: Isolates services and data without resorting to isolated infrastructure, protecting and simplifying control with Lightweight Directory Access Protocol (LDAP) authentication, role-based access control (RBAC), single sign-on (SSO), and Sysdig Teams for fine-grained service-based access control.

- Support: Extends technical support and services to enterprise Prometheus users to resolve issues faster.

Key Features of Monitor 3.0:

PromQL: Sysdig Monitor is the only enterprise monitoring solution to offer PromQL for metric queries that extend beyond Prometheus metrics. DevOps can now calculate advanced metrics with Prometheus and any other collected application, system, and custom metric like JMX or StatsD using PromQL to meet the unique monitoring needs of their enterprise.

Grafana Plugin: The new Grafana plugin lets users choose to utilize the open source Grafana visualization and dashboarding solution popular with Prometheus users to view Sysdig-hosted time series metrics.

New Kubernetes Metrics and Dashboards: Support for StatefulSet metrics and default dashboards enhances Kubernetes performance monitoring for users that deploy stateful applications such as databases – streamlining problem identification and resolution. New cluster state, master health, and capacity management dashboards increase operational efficiency, helping cluster operators ensure the health of their orchestration system.

Universal Kubernetes Support: Sysdig extends the value of Sysdig Monitor to all popular Kubernetes packages, including Pivotal Kubernetes Service (PKS), Kubernetes with Mesosphere DC/OS, Kubernetes with Docker 2.0, IBM Cloud Kubernetes Service, Amazon Elastic Container Service for Kubernetes (Amazon EKS), and Azure Container Service (AKS).

Sysdig launched in 2013 with its open source monitoring technology, sysdig, and later with the Sysdig open source security project, Sysdig Falco. This release further reinforces the commitment to the open source movement by providing the Sysdig Cloud-Native Intelligence Platform to open source Prometheus users and extending its adoption to enterprise customers.

Sysdig Monitor 3.0 will be rolled out to all Sysdig customers in this coming quarter and it will be demoed for the first time at AWS re:INVENT in November.

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.

Sysdig Monitor 3.0 Announced

Sysdig announced Sysdig Monitor 3.0.

Sysdig Monitor 3.0 delivers enterprise-grade Prometheus monitoring.

As enterprises move toward modern infrastructure to support digital transformation initiatives, visibility into the performance of dynamic, distributed microservices becomes a significant challenge. Successful monitoring of business-critical applications and infrastructure based on cloud-native solutions like Kubernetes and Docker requires a new approach. Enterprises in early-phase Kubernetes projects often use open source Prometheus to research the performance of their applications on this new platform. As these organizations transition to full-scale production, there are additional requirements for broader telemetry and event data, long-term data retention, high availability, and access control. Sysdig delivers these capabilities to help enterprises deliver efficient, secure applications at enterprise scale and accelerate time-to-value with Prometheus.

“We love Prometheus and the tremendous monitoring value it adds, which is why we have started to contribute to the open source project in addition to providing Prometheus capabilities for enterprise customers,” said Loris Degioanni, CTO and founder of Sysdig. “Sysdig has embraced Prometheus to provide users with another source of rich data alongside our existing kernel instrumentation. With Sysdig Monitor 3.0, enterprise customers see the whole picture and can resolve issues more quickly. ”

Enterprise-grade Prometheus with Sysdig Monitor 3.0 focuses on five key areas:

- Scale: Provides a horizontally scalable distributed system platform that handles tens of millions of metrics per second with cross-cluster aggregation to keep pace with large, complex environments.

- Scope: Collects, analyzes, and correlates Prometheus metrics with granular metrics and events for system processes, applications, cloud platforms, networks, orchestrators, and customer metrics like StatsD and Java TM Management Extensions (JMX), with advanced visualizations like topology maps.

- Simplicity: Reduces complexity with a turn-key solution that eliminates the headaches of managing multiple isolated monitoring systems and services.

- Security: Isolates services and data without resorting to isolated infrastructure, protecting and simplifying control with Lightweight Directory Access Protocol (LDAP) authentication, role-based access control (RBAC), single sign-on (SSO), and Sysdig Teams for fine-grained service-based access control.

- Support: Extends technical support and services to enterprise Prometheus users to resolve issues faster.

Key Features of Monitor 3.0:

PromQL: Sysdig Monitor is the only enterprise monitoring solution to offer PromQL for metric queries that extend beyond Prometheus metrics. DevOps can now calculate advanced metrics with Prometheus and any other collected application, system, and custom metric like JMX or StatsD using PromQL to meet the unique monitoring needs of their enterprise.

Grafana Plugin: The new Grafana plugin lets users choose to utilize the open source Grafana visualization and dashboarding solution popular with Prometheus users to view Sysdig-hosted time series metrics.

New Kubernetes Metrics and Dashboards: Support for StatefulSet metrics and default dashboards enhances Kubernetes performance monitoring for users that deploy stateful applications such as databases – streamlining problem identification and resolution. New cluster state, master health, and capacity management dashboards increase operational efficiency, helping cluster operators ensure the health of their orchestration system.

Universal Kubernetes Support: Sysdig extends the value of Sysdig Monitor to all popular Kubernetes packages, including Pivotal Kubernetes Service (PKS), Kubernetes with Mesosphere DC/OS, Kubernetes with Docker 2.0, IBM Cloud Kubernetes Service, Amazon Elastic Container Service for Kubernetes (Amazon EKS), and Azure Container Service (AKS).

Sysdig launched in 2013 with its open source monitoring technology, sysdig, and later with the Sysdig open source security project, Sysdig Falco. This release further reinforces the commitment to the open source movement by providing the Sysdig Cloud-Native Intelligence Platform to open source Prometheus users and extending its adoption to enterprise customers.

Sysdig Monitor 3.0 will be rolled out to all Sysdig customers in this coming quarter and it will be demoed for the first time at AWS re:INVENT in November.

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.