Grafana Enterprise

The main use case for Grafana is that we are using Prometheus to collect the metrics, and primarily use Grafana for infrastructure and application monitoring. It helps me visualize system metrics through interactive dashboards, monitor server health, track CPU, memory, disk, and network usage, and create alerts for critical events. We also use it alongside Prometheus to monitor Linux servers and quickly identify issues or resource bottlenecks. It provides a centralized view of our environment, making troubleshooting and capacity planning much easier.

A main use case I can say is infrastructure and server monitoring using Prometheus dashboards and alerting. For troubleshooting, when we receive an alert, we can act on it. So, for root cause analysis for infrastructure issues, identifying server problems, bottlenecks, and detecting network latency and connectivity issues, and monitoring service health during deployments.

I use Grafana daily to monitor infrastructure health. I use Grafana to analyze historical trends and troubleshoot performance issues, customizing dashboards for different teams to provide relevant operational insights. During deployments, it was really helpful while using Grafana to monitor the system health, such as how the metrics of the production servers were before and after the deployment. That comparison helps us if anything breaks in production.

The best features I can say are the customizable dashboards. They are interactive and customizable dashboards. Also, powerful alerting and notification. As soon as the threshold limit is reached, we receive an alert on the email directly. Also, historical data analysis and trend tracking, easy integration with Prometheus and other data sources, and a wide range of visualization panels that Grafana has. It supports multiple data sources.

To summarize, I can say customizable dashboards, real-time monitoring, seamless Prometheus integration, and powerful alerting capabilities.

For me, one feature I can say is the alerting feature and the customizable dashboard feature. They allow me to create role-specific views, monitor key metrics in real-time, and quickly identify performance issues in a single place. Prometheus integration makes collecting and visualizing metrics simple and reliable.

Real-time monitoring also provides instant visibility into infrastructure and application health.

Grafana has impacted us by improving infrastructure visibility and monitoring after we started using it. We have faster issue detection and resolution. For example, I can say if a disk of a server is ninety percent full or it is going to be one hundred percent full, before that, we receive an alert and we are taking action on that alert. So, it allows for faster issue detection and resolution, and better operational efficiency and reduced downtime through proactive alerting. It also reduced the mean time to resolution and provides centralized monitoring across multiple systems. That is the best thing I appreciate.

It also enhanced decision-making with real-time dashboards. Grafana significantly improved infrastructure visibility, reduced troubleshooting time, and enabled faster incident response through real-time dashboards and alerts.

It reduced troubleshooting time by approximately forty percent through the centralized dashboards. It improved incident detection by around thirty percent with real-time alerts and reduced mean time to resolution by about thirty-five percent. It cut manual monitoring effort by approximately fifty to sixty percent because earlier, if we were monitoring our Linux servers, for example, we were doing the top and htop command on the server itself. But after using Grafana, we rarely use the htop and top commands because we are getting the full view of the server on the Grafana dashboard. It also improved infrastructure visibility. Troubleshooting time was reduced by approximately forty to forty-five percent, and we improved incident response through centralized dashboards.

For improving Grafana, the alert configuration could be more intuitive for new users. Dashboard management becomes complex in large environments. So if we have multiple environments and we have to manage our dashboards for them, sometimes it becomes complicated to manage all the dashboards. The learning curve is steep for beginners. More built-in reporting and analytics features would be helpful. Documentation for advanced features could be more detailed. Role-based access management could be simpler to configure. Performance could be optimized for very large dashboards with high cardinality data sources.

Grafana could improve its alert configuration workflow and make advanced dashboard management easier for new users. More built-in reporting features would also be beneficial.

A simpler onboarding experience for first-time users and more built-in dashboard templates for common monitoring scenarios would be helpful. Grafana is a powerful monitoring platform, but it could improve dashboard organization in large environments, simplify alert management, provide more built-in reporting capabilities, enhance plugin compatibility during upgrades, and offer better AI-driven insights for fast root cause analysis. For example, if we are hitting a CPU metric of ninety to ninety-five percent or a system load of seventy to eighty percent, we have to act on that. But if Grafana suggests something on that metric, such as an AI suggestion, it would be more helpful. We could get an idea of how to solve that issue.

For the Grafana dashboards, they could provide more built-in compliance and audit reporting, enhance AI recommendations with clearer explanations of why an issue was flagged, enhance accessibility and keyboard navigation, add more native integrations with emerging cloud and DevOps tools, and improve scalability for very large enterprise deployments. Grafana is a mature platform, but it could benefit from better dashboard version control, simpler environment migration, enhanced AI explanations, more built-in reporting, and improved performance for large-scale deployments with complex dashboards.

I have been using Grafana for the last two years.

Grafana is stable. Stable in the sense that we have experienced minimal downtime, and it performs reliably for day-to-day monitoring and dashboard visualization.

The customer support for Grafana is acceptable, but as we used the community edition, we primarily relied on the Grafana documentation and community forums. The documentation is comprehensive, and the active community makes it easy to find solutions to common issues. Customer support was responsive and knowledgeable. Most issues were resolved quickly, and the support team provided clear guidance.

For monitoring, we were using Nagios previously. But Grafana is more advanced than the other tools I have used. We primarily relied on the native interface and basic monitoring tools. Grafana provided much better visualization, customizable dashboards, and a centralized view of our infrastructure metrics.

Grafana has delivered a strong return on investment by reducing troubleshooting time, improving incident response, and minimizing manual monitoring efforts through centralized dashboards and automated alerts. It helped us a lot.

For manual monitoring, we were using the top and htop commands for server monitoring. We also had scripts so that if the hard disk reached seventy to eighty-five percent, a script would run, check the threshold, and then we would receive an alert through the script on the server. But after using Grafana, that is automated. We receive the alert directly when the threshold hits the limit.

So, we had around a forty percent reduction in troubleshooting time, a thirty percent improvement in incident response time, and a fifty percent reduction in manual monitoring time. I can say the estimated return on investment was one hundred fifty percent to two hundred fifty percent within the first year due to the reduced downtime and faster troubleshooting.

We are using the community edition. We have used the community edition, the open-source edition. So, there were no licensing costs. The setup was straightforward, and the main investment was the time required for deployment and dashboard configuration. Most of the effort went to integrating data sources, creating dashboards, and configuring alerts rather than managing costs.

We previously used Nagios for monitoring. We evaluated Grafana alongside Kibana, DataDog, and Zabbix for the native UI. We chose Grafana because of its flexible dashboards, wide range of data source integrations, strong visualization capabilities, and active open-source community.

I have not used Grafana AI extensively yet, but I see value in its AI-powered anomaly detection, root cause analysis, and incident investigation features. These capabilities can help reduce troubleshooting time and improve operational efficiency.

Grafana AI appears reliable for detecting anomalies and assisting with incident analysis. Its accuracy depends on the quality of monitoring data, alert configuration, and historical metrics. In a well-configured environment, it can provide valuable insights, but I still recommend validating AI-generated suggestions before taking action in production.

Start with a clear monitoring strategy and identify the key metrics you want to track before building dashboards. Begin with the built-in templates, integrate Grafana with reliable data sources such as Prometheus, and configure meaningful alerts to avoid alert fatigue. Organize dashboards by application or team, review them regularly, and leverage the documentation and community resources to get the most value from the platform.

I would confidently recommend it to organizations of any size looking for a reliable and scalable monitoring solution. Grafana has been a valuable addition to our monitoring stack. Its flexibility, powerful visualization capabilities, and seamless integration with Prometheus have significantly improved our infrastructure monitoring and troubleshooting. While there is room for improvement in areas like reporting, alert management, and AI-assisted insights, it remains one of the best open-source monitoring and observability platforms available. I would rate this product a nine out of ten.

Hybrid Cloud

Amazon Web Services (AWS)

What do you like best about the product?

Grafana Labs is used to look at the logs, monitor and analyze them. The enterprise and cloud offering of Grafana is excellent tool for log monitoring

What do you dislike about the product?

Disadvantage about Grafana labs is it does not store logs, always it depends on secondary source to get the logs

What problems is the product solving and how is that benefiting you?

The business problem that Grafana solving for me is log analysis of complex applications.

What do you like best about the product?

At the moment, this is the best open-source unified dashboard and observability platform, backed by a strong open-source community.

What do you dislike about the product?

Grafana’s biggest benefit is also its biggest drawback: there’s an enormous amount I can configure on the platform. That’s great at first, but as teams scale, it can lead to conflicts, inconsistencies, and ongoing maintenance issues.

What problems is the product solving and how is that benefiting you?

It’s easy to set up a monitoring service for distributed systems and create custom dashboards for everyone who’s interested in the product.

What do you like best about the product?

Beautiful, interactive dashboards that bring all the KPIs together in one place, with very useful alerting. All in all, an essential tool for keeping things transparent across the company and across teams.

What do you dislike about the product?

About alerting a bit complex to configure and sometime not realtime.

What problems is the product solving and how is that benefiting you?

We can see all the stats visually, which helps us understand our pain points and maintain transparency across teams.

What do you like best about the product?

Customized dashboards as per the use case . Customized metrics as per the various details . Can pull data from various data sources without any troubled integration.

What do you dislike about the product?

Provides to many options which can be a problem as multiple options can lead to confusion.
Difficulties while initial setting up as it required certain type of expertise.

What problems is the product solving and how is that benefiting you?

Grafana solves the problem of having different parameters on different dashboard as it collects data from various sources and presents it at one dashboard which provides single observability platform

What do you like best about the product?

Grafana is a great tool for monitoring - it combines and hold all out monitoring dashboards, helping us keep everything organized and easily accessible. It’s also very easy to set up alerts when metrics deviate from expectations.

What do you dislike about the product?

It can be a bit tech-heavy, especially for inexperienced users, but that’s mainly because it offers a lot of capabilities and configurability.

What problems is the product solving and how is that benefiting you?

Grafana helps us keep all our traffic data in one place and actually make sense of it. Instead of jumping between tools, we can quickly see where users are coming from and spot any unusual changes. It’s saved us time and made monitoring way more straightforward.

What do you like best about the product?

Updated version ,user friendly and relaiable

What do you dislike about the product?

No ,dislikes but need more accuracy and speed

What problems is the product solving and how is that benefiting you?

Grafana solves collecting metrics, logs, and traces showing them in dashboards + alerts

What do you like best about the product?

I like Grafana Labs mainly because it makes monitoring feel simple while still staying flexible. The Grafana dashboards look clean, are easy to customize, and they work with almost any data source. It feels powerful without being overly rigid, which is rare to find.

What do you dislike about the product?

One thing I don’t like about Grafana Labs is that it can become pretty complex as setups grow. Grafana is powerful, but managing dashboards, permissions, and data sources at scale isn’t always straightforward or easy to keep consistent. On top of that, some of the more advanced features are locked behind paid tiers, which can feel limiting.

What problems is the product solving and how is that benefiting you?

With Grafana, everything is brought together in one place, which makes it much easier to monitor systems, spot issues quickly, and understand what’s happening in real time.

My main use case for Grafana is to create and design dashboards based on the metrics provided by different exporters via Prometheus.

We have different exporters, and we are creating different dashboards based on them. We have a set of dashboards related to Kafka, virtual machines, and instances. Inside Kafka, we have a broker dashboard, consumer dashboard, partition dashboard, and other ingestion and consumption rate dashboards. Apart from that, we have a dashboard for consumer lag and consumption by partition.

We are collecting metrics from Prometheus and creating dashboards inside Grafana. Inside Grafana, we have different data sources including Thanos and Prometheus. We are also using Grafana for alert setup. We have set up alerts based on the exceptions we are collecting from Loki, and if any such exception occurs, it will create an incident alert over Squadcast.

Grafana offers many features including the ability to create dashboards, add variables, and set up alerts, which also covers notifications via integration with incident management tools or by configuring your email ID to get the notifications.

You can directly configure alerts in Grafana by either creating a dashboard or using the explore icon in Grafana, where you can select Loki and set alerts based on your exceptions.

There are many features including dashboard creation being much easier. You can configure multiple data sources such as Prometheus and Thanos. Apart from that, you can directly link AWS CloudWatch with your Grafana and other tools. For alerting, you can create alerts based on thresholds and exceptions, and in Grafana, there are many plugins you can configure to create data source dashboards. Additionally, there is also a restriction in Grafana that allows you to provide viewer, editor, or admin access based on roles.

We have had very positive outcomes from Grafana because you can directly visualize the metrics based on past and current inputs and take timely actions based on the responses you are getting from the visualization dashboards. Apart from that, the alerts notify you through your incident management tool.

You can check those metrics in the incident management tool by filtering the alert source as Grafana, and it helps in reducing production incidents because you can acknowledge and visualize the metrics from Grafana on time.

Currently, I do not think that any improvement is required, but there are multiple use cases.

I have been using Grafana for the last four years.

Grafana is stable.

Grafana has excellent scalability.

The customer support for Grafana is excellent.

Positive

This is the only solution we are currently using.

Before choosing Grafana, we evaluated other options including DataDog, but it was quite costlier, so we switched to Grafana.

I have seen a return on investment as we actually need fewer employees, and you can take timely actions on the alerts. Apart from that, it reduces MTTR because you receive notifications through the incident management tool, allowing for timely action and better troubleshooting by visualizing metrics and logs inside Grafana. You can optimize these processes by visualizing issues earlier based on the metrics from Grafana.

I have seen a return on investment with fewer employees needed, and you can take timely actions based on alerts. Apart from this, it helps reduce MTTR because you receive notifications through the incident management tool, enabling timely responses and better troubleshooting by visualizing metrics and logs inside Grafana, thus allowing you to tackle issues earlier based on Grafana metrics.

My experience with pricing, setup cost, and licensing is that it is very reasonable and has excellent community support.

You are able to detect issues faster because you can configure alerts based on thresholds in your Grafana and get notifications from your tool like Squadcast, which will reduce MTTR. Apart from that, system visibility is there; you can visualize CPU metrics, memory, disk usage, API latencies, and other ports inside the Grafana dashboard. Based on these metrics, you can troubleshoot your issues very easily.

If you want a scalable solution, better visualization, optimization, centralized monitoring, and improved troubleshooting, then you can choose Grafana without any doubts in your mind. I would rate this product a ten out of ten.

Public Cloud

Amazon Web Services (AWS)

What do you like best about the product?

What I like most about Grafana Labs is its flexibility and strong visualization capabilities. Grafana makes it straightforward to create interactive, real-time dashboards by pulling data from multiple sources such as Prometheus, Elasticsearch, and various cloud platforms. The interface feels intuitive and highly customizable, which makes it especially well-suited for monitoring complex microservices environments.

What do you dislike about the product?

One drawback of Grafana Labs is that the more advanced features and enterprise capabilities can get expensive as you scale, particularly in larger environments with high data volumes.

For beginners, the initial dashboard setup and alert configuration can also feel a bit complex. Once you start integrating multiple data sources across distributed systems, it can take a while to fully understand how to structure queries, manage permissions, and set up alerting logic correctly.

What problems is the product solving and how is that benefiting you?

Grafana Labs addresses the challenge of fragmented observability by bringing metrics, logs, and traces together in a single, unified platform. Rather than jumping between multiple tools, I can visualize, compare, and correlate everything in one place.

This has noticeably improved our troubleshooting speed, provides clearer visibility across microservices, and helps our team make faster, data-driven decisions with greater confidence.