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Grafana Labs Announces New Grafana Cloud Capabilities

Grafana Labs announced new Grafana Cloud capabilities designed for Kubernetes platform teams seeking to reduce cloud costs and gain more unified monitoring experiences across their entire cloud native infrastructure.

“Kubernetes leveled up platform engineering and redefined how global distributed teams could access shared infrastructure – but teams have to use multiple different platforms to cover the full breadth of cost monitoring, system health, incident management and related K8s infrastructure concerns,” said Tom Wilkie, Grafana Labs CTO and CNCF Governing Board member. “We believe that Grafana Cloud, our fully managed offering that makes it easier to get started with observability and includes a generous forever-free tier, gives platform teams more insight under one roof than any other observability tool for Kubernetes environments.”

With Kubernetes Monitoring, the solution introduced to the fully managed Grafana Cloud observability platform last year, users can automatically ship metrics to Grafana Cloud after installing the Grafana Agent into one or more Kubernetes clusters. Once this connection is made, Grafana Cloud users have out-of-the-box access to their Kubernetes metrics, logs, and events via prebuilt dashboards and alerts.

The latest updates include:

- Cost monitoring: This feature, which leverages the CNCF sandbox project OpenCost, allows platform teams to measure infrastructure spend on Kubernetes deployments – breaking down costs to nodes, persistent volumes, and load balancers – across multi-cloud environments. Cost monitoring shows your AWS, GCP, and Azure environment costs alongside suggestions for resource areas where you can optimize for savings, such as CPUs, RAM, and more. For more about tracking cloud costs, check out the KubeCon session Where's Your Money Going? The Beginner's Guide to Measuring Kubernetes Costs. Grafana Labs engineers Mark Poko and JuanJo Ciarlante will discuss Grafana Labs' journey toward cost observability and lessons learned in optimizing cloud spend.

- Out-of-the-Box Kubernetes Traces: Grafana Cloud is experimenting with adding the possibility of scraping traces for Kubernetes clusters. Data can be then sent to Grafana Tempo for visualization. Rather than jumping between different Kubernetes infrastructure components to find out “what happened” in complex incident resolution scenarios, Grafana Cloud would allow platform teams to trace specific Kubernetes events from start to finish with a simple agent install.

- Kubernetes Monitoring landing page: Grafana Cloud’s new Kubernetes Monitoring landing page further reduces context switching for platform teams by bringing all of the most pressing issues you might have in your Kubernetes infrastructure to the surface automatically, in a single, predefined view. From pods in trouble (either crashlooping or not starting correctly), to nodes that have memory or disk pressure, to persistent volumes above 90 percent capacity, Grafana Cloud’s Kubernetes Monitoring makes intelligent inferences that identify problem areas before they bring systems down.

- Simplified Helm installation: Grafana Cloud’s new Helm installation makes it easy to install the Kubernetes Monitoring solution and get started scraping Kubernetes metrics, logs, and traces. It’s open source, any platform team can run it with the Grafana Agent, and it ships with basic configurations for what you want it to include. Kubernetes Monitoring is compatible with ArgoCD, Prometheus, Terraform, OTel Collector, Windows Exporter, or Ansible.

- Easy monitoring of services running on your Kubernetes fleet: Kubernetes Monitoring in Grafana Cloud includes out-of-the-box integrations that come with prebuilt dashboards, rules, and alerts for Aerospike, Apache ActiveMQ, Cilium, CoreDNS, etcd, NGINX, GitLab, Apache Kafka, CockroachDB, Apache Cassandra, PostgreSQL, MySQL. Grafana Cloud has bundled all of these integrations into a single solution themed for various monitoring use cases. If you have an application running in Kubernetes, you can also see where your application lives within your Kubernetes fleet – whether on AWS, Google, Amazon, OpenShift, or any other common Kubernetes distributions.

Continued contributions to CNCF open source projects

- Deeper OpenTelemetry and Prometheus integrations: Grafana Labs is the only company leading in contributions to Prometheus and OpenTelemetry. One main area of focus has been interoperability between the two projects. Now that OpenTelemetry Metrics is stable, it has gained traction among users, and more people are coupling OTel with Prometheus as the backend. In the last year and a half, the Prometheus working group, which includes Grafana Labs' Goutham Veeramachaneni, has been improving the usability of Prometheus with OpenTelemetry, including adding native OTLP ingestion in Prometheus.

- Continuous profiling for OpenTelemetry: Grafana Labs Engineering Director Ryan Perry is working with the community to integrate continuous profiling into the OpenTelemetry project. At KubeCon, Perry’s session – A Tale of Two Flamegraphs: Unlocking Performance Insights in a Diverse Application Landscape – will trace the evolution of performance profiling as a key “fourth pillar” in observability (adding a new dimension beyond metrics, logs, and traces), and provide an update on the efforts of Grafana Labs and other OpenTelemetry contributors to enable optimizing applications across diverse programming languages and platforms.

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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 ...

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Grafana Labs Announces New Grafana Cloud Capabilities

Grafana Labs announced new Grafana Cloud capabilities designed for Kubernetes platform teams seeking to reduce cloud costs and gain more unified monitoring experiences across their entire cloud native infrastructure.

“Kubernetes leveled up platform engineering and redefined how global distributed teams could access shared infrastructure – but teams have to use multiple different platforms to cover the full breadth of cost monitoring, system health, incident management and related K8s infrastructure concerns,” said Tom Wilkie, Grafana Labs CTO and CNCF Governing Board member. “We believe that Grafana Cloud, our fully managed offering that makes it easier to get started with observability and includes a generous forever-free tier, gives platform teams more insight under one roof than any other observability tool for Kubernetes environments.”

With Kubernetes Monitoring, the solution introduced to the fully managed Grafana Cloud observability platform last year, users can automatically ship metrics to Grafana Cloud after installing the Grafana Agent into one or more Kubernetes clusters. Once this connection is made, Grafana Cloud users have out-of-the-box access to their Kubernetes metrics, logs, and events via prebuilt dashboards and alerts.

The latest updates include:

- Cost monitoring: This feature, which leverages the CNCF sandbox project OpenCost, allows platform teams to measure infrastructure spend on Kubernetes deployments – breaking down costs to nodes, persistent volumes, and load balancers – across multi-cloud environments. Cost monitoring shows your AWS, GCP, and Azure environment costs alongside suggestions for resource areas where you can optimize for savings, such as CPUs, RAM, and more. For more about tracking cloud costs, check out the KubeCon session Where's Your Money Going? The Beginner's Guide to Measuring Kubernetes Costs. Grafana Labs engineers Mark Poko and JuanJo Ciarlante will discuss Grafana Labs' journey toward cost observability and lessons learned in optimizing cloud spend.

- Out-of-the-Box Kubernetes Traces: Grafana Cloud is experimenting with adding the possibility of scraping traces for Kubernetes clusters. Data can be then sent to Grafana Tempo for visualization. Rather than jumping between different Kubernetes infrastructure components to find out “what happened” in complex incident resolution scenarios, Grafana Cloud would allow platform teams to trace specific Kubernetes events from start to finish with a simple agent install.

- Kubernetes Monitoring landing page: Grafana Cloud’s new Kubernetes Monitoring landing page further reduces context switching for platform teams by bringing all of the most pressing issues you might have in your Kubernetes infrastructure to the surface automatically, in a single, predefined view. From pods in trouble (either crashlooping or not starting correctly), to nodes that have memory or disk pressure, to persistent volumes above 90 percent capacity, Grafana Cloud’s Kubernetes Monitoring makes intelligent inferences that identify problem areas before they bring systems down.

- Simplified Helm installation: Grafana Cloud’s new Helm installation makes it easy to install the Kubernetes Monitoring solution and get started scraping Kubernetes metrics, logs, and traces. It’s open source, any platform team can run it with the Grafana Agent, and it ships with basic configurations for what you want it to include. Kubernetes Monitoring is compatible with ArgoCD, Prometheus, Terraform, OTel Collector, Windows Exporter, or Ansible.

- Easy monitoring of services running on your Kubernetes fleet: Kubernetes Monitoring in Grafana Cloud includes out-of-the-box integrations that come with prebuilt dashboards, rules, and alerts for Aerospike, Apache ActiveMQ, Cilium, CoreDNS, etcd, NGINX, GitLab, Apache Kafka, CockroachDB, Apache Cassandra, PostgreSQL, MySQL. Grafana Cloud has bundled all of these integrations into a single solution themed for various monitoring use cases. If you have an application running in Kubernetes, you can also see where your application lives within your Kubernetes fleet – whether on AWS, Google, Amazon, OpenShift, or any other common Kubernetes distributions.

Continued contributions to CNCF open source projects

- Deeper OpenTelemetry and Prometheus integrations: Grafana Labs is the only company leading in contributions to Prometheus and OpenTelemetry. One main area of focus has been interoperability between the two projects. Now that OpenTelemetry Metrics is stable, it has gained traction among users, and more people are coupling OTel with Prometheus as the backend. In the last year and a half, the Prometheus working group, which includes Grafana Labs' Goutham Veeramachaneni, has been improving the usability of Prometheus with OpenTelemetry, including adding native OTLP ingestion in Prometheus.

- Continuous profiling for OpenTelemetry: Grafana Labs Engineering Director Ryan Perry is working with the community to integrate continuous profiling into the OpenTelemetry project. At KubeCon, Perry’s session – A Tale of Two Flamegraphs: Unlocking Performance Insights in a Diverse Application Landscape – will trace the evolution of performance profiling as a key “fourth pillar” in observability (adding a new dimension beyond metrics, logs, and traces), and provide an update on the efforts of Grafana Labs and other OpenTelemetry contributors to enable optimizing applications across diverse programming languages and platforms.

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.