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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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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 MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...