G2

Anand N.

Senior Systems Analyst

Mid-Market (51-1000 emp.)

9/23/2026

"No More Scavenger Hunts: A Unified Cockpit for Kubernetes Monitoring"

5/5

What do you like best about Grafana Labs?

What I like most about Grafana Labs is how it brings observability data from different parts of our infrastructure into one place. As a Senior DevOps Engineer, I regularly work with Kubernetes clusters, application services, and cloud infrastructure, so having a clear view of system health is extremely useful.

I particularly like the customizable dashboards and alerting capabilities. For example, when a deployment causes an unexpected increase in CPU or memory utilization, I can quickly check the relevant Grafana dashboards, correlate metrics, and identify whether the issue is related to resource limits, pod restarts, or application performance. Instead of manually checking multiple systems, I can get a much clearer picture from a single interface.

The integration with Prometheus is another feature I use regularly. It makes monitoring Kubernetes workloads, tracking resource consumption, and setting up alerts much more convenient. I also find the ability to visualize logs and metrics together through tools like Loki very useful during production troubleshooting.

The dashboard flexibility is probably my favorite part. I can create separate views for infrastructure health, application performance, and deployment monitoring depending on what the team needs. Overall, Grafana Labs has made our monitoring process more structured and reduced the time spent identifying issues during production incidents. Review collected by and hosted on G2.com.

What do you dislike about Grafana Labs?

Grafana Labs is a useful observability platform, but after working with it across different monitoring tasks, I feel there are a few areas that need improvement.

1. UI / UX: Managing dashboards with multiple panels and variables can get confusing, especially when working with complex queries. Sometimes, identifying why a panel is showing incorrect or incomplete data takes more time than expected. Dashboard editing could be more intuitive.

2. Integrations: Connecting Prometheus, Loki, and other monitoring tools works well overall, but some configurations still require manual effort. Correlating logs with metrics during troubleshooting is not always smooth, particularly when labels or timestamps don't match.

3. Performance: Dashboards with multiple panels and high-cardinality metrics can occasionally take longer to load. This becomes frustrating during production incidents when quick access to CPU usage, memory metrics, or pod health is important.

4. Pricing / ROI: As monitoring requirements increase, costs associated with log volume, metric retention, and data usage can add up. Better cost visibility and more flexible pricing would help teams manage their observability budgets.

5. Support / Onboarding: Basic setup is manageable, but configuring advanced alerting, permissions, and data sources takes some time. The documentation is detailed, although finding solutions to specific configuration issues sometimes requires checking multiple guides and community discussions.

6. AI / Intelligence: I think there is room to improve automated incident analysis. For example, when Kubernetes pods repeatedly restart or CPU utilization suddenly spikes, more useful recommendations connecting related metrics, logs, and infrastructure events would make troubleshooting faster.

Overall, Grafana Labs does a good job of bringing monitoring data together, but query performance, configuration simplicity, cost transparency, and smarter troubleshooting are areas where I would like to see further improvements. Review collected by and hosted on G2.com.

What problems is Grafana Labs solving and how is that benefiting you?

Before using Grafana Labs, monitoring different parts of our infrastructure was a bit time-consuming because we had to check multiple tools to understand what was happening. In my role as a Senior DevOps Engineer, I use Grafana regularly to keep track of Kubernetes clusters, pod health, CPU and memory usage, application performance, and service availability.

One situation where I find it particularly useful is when a new deployment causes unexpected CPU spikes or frequent pod restarts. By checking Prometheus metrics alongside Loki logs, I can narrow down the issue without spending too much time switching between different tools. It has made investigating production issues much easier for our team.

UI/UX: I like how easy it is to create dashboards based on what I actually need to monitor. Panels, variables, and filters are useful, although setting up dashboards with complex queries can sometimes take extra effort.

Integrations: Prometheus and Loki are two integrations I find especially useful. Having metrics and logs available together makes Kubernetes troubleshooting much easier.

Performance: Grafana handles our regular monitoring requirements well. Dashboards update smoothly, and I can quickly check resource utilization and identify unusual activity.

Pricing/ROI: The open-source version is a good starting point. Considering the visibility and troubleshooting capabilities it provides, I think it offers good value.

Support/Onboarding: The documentation and community resources have been helpful whenever I needed guidance with data sources, dashboard configurations, or queries.

AI/Intelligence: I find Grafana's AI capabilities interesting, particularly their potential to simplify troubleshooting and telemetry analysis. However, my everyday work still mainly involves dashboards, PromQL queries, logs, and alerts. Review collected by and hosted on G2.com.