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.

My main use case for Grafana involves operational dashboarding and data visualization, where I use it as a central pane of glass to pull in metrics from multiple sources like Prometheus, Elasticsearch, and SQL databases to visualize the overall health of our systems in one unified view.

For example, I have built a NOC dashboard that tracks CPU memory usage and network traffic across all the pods. If a specific service starts failing, the Grafana dashboard highlights the issue in red, allowing my on-call engineers to identify the failing cluster at a glance.

Grafana's snapshot and dashboard sharing features are critical for our remote incident response. During production issues, I generate a public snapshot of a dashboard at a specific point and share the URL in our Slack war room so every engineer can see exactly what the metrics looked like when the error occurred. This helps significantly during the process of finding the root cause in those scenarios.

The best features Grafana offers go beyond just pretty charts; it is an integration engine. The fact that I can join data from my SQL database with metrics from Prometheus in the same table is a feature I have not found performed as well elsewhere.

My team uses this feature by comparing two different tables from the databases to show one single view, which Grafana is really helping with. In a visualized way, the charts can be displayed on one dashboard, allowing end users who are not familiar with these technical aspects to extract valuable data from it.

Grafana has positively impacted our organization by democratizing data within our company. Before using Grafana, only developers could see the system health, but now our product managers and executives have their own high-level dashboards, which has improved cross-departmental transparency and alignment.

I find that the alerting UI in Grafana can be complex for new users. While it is very powerful, it takes time to learn the differences between contact points, notification policies, and silences.

The documentation can be improved to provide more detailed descriptions, allowing new users to understand more concepts before they come to knowledge transfer sessions with senior team members.

I have been using Grafana for over four years to build real-time observability dashboards and monitor our complex infrastructure and application performance.

In my experience, Grafana is extremely stable. Even when handling millions of data points, the visualization layer remains responsive. Since it is decoupled from the actual data storage, the dashboard stays up even if one of our underlying data sources is temporarily slow.

Grafana's scalability is impressive. It is highly scalable and built on a big data architecture capable of ingesting trillions of data points. For our on-premise instance, I use a high availability configuration with a shared database to manage growth.

Customer support for Grafana is solid. The community support is massive, and the technical support team is very helpful with complex PromQL troubleshooting.

Before Grafana, I relied solely on the native monitoring console of our cloud providers, like AWS CloudWatch. I switched to Grafana because I needed a way to see all my clouds in a single dashboard rather than switching between multiple tabs.

Grafana's forever free tier for the cloud version allowed the initial setup cost to be zero. As I scaled, I moved to a paid tier based on my number of active series and users, which I found to be very fair compared to other observability vendors.

I identified over-provisioned servers and reduced my AWS monthly bill by 15%, which is a significant saving in terms of costs. Additionally, I see a 25% improvement in MTTD due to my shift from text-based logs to visualized dashboards.

I purchased my Grafana Cloud subscription through the AWS Marketplace, which simplified my procurement process and allowed me to apply the cost towards my AWS committed spend.

I looked at Kibana and Tableau before deciding on Grafana. I chose Grafana because Kibana is mostly limited to Elasticsearch, whereas Grafana can connect to almost any data source. Unlike Tableau, Grafana is specifically optimized for time series data and real-time monitoring.

When Grafana highlights an issue, it will trigger email alerts that engineers can rely on. Immediately when they receive these alerts, they involve other support teams, and a bridge is initialized to start troubleshooting.

For those looking into using Grafana, I advise starting with the Grafana play site to see what is possible and then using the pre-built dashboards from the Grafana dashboard gallery. There is likely already a perfect dashboard available for free tailored to your tech stack.

Grafana is unique in that I can join data from my SQL database with metrics from Prometheus in the same table, a feature I have not found performed as well elsewhere. My overall rating for this product is 10 out of 10.

We manage log processing with Grafana because we found that it is much easier for us to manage it on our infrastructure on AWS. We can maintain all the things we do not need. DataDog told us we have to wait and that they need to provide features we need to develop, so it does not suit our needs.

We are still using DataDog, but for important assets that we need to analyze the logs, we send it to Grafana.

The challenges we face with DataDog compared to Grafana include the need to analyze very important brands, network trafficking, and maintaining many websites, most of which are very important domains that cost a lot of money, so we are getting attacked each day and we need to analyze all of the logs. Sometimes we have false positives and things similar to that, so we have to make sure that we are doing the correct decision of blocking or trying to mitigate attacks. Using the logs with Grafana it is much easier for us to analyze rather than DataDog. DataDog has their own language and they want you to plot things with their own vocabulary. We do not have time to memorize things. We especially wanted to use something that was open source at the beginning, and then other people started using it, took that product and modified it for extra cost, but it is a better solution for us.

We switched from DataDog to Grafana because we wanted to reduce the logs costs, as we are streaming approximately five million logs or even less.

We can find information with Grafana much more quickly compared to DataDog because it was open source and there was extensive documentation about it, enabling us to fetch data or information much more quickly using AI tools. With DataDog, they always wanted us to have a meeting or talk with us on a call. It was redundant. We just wanted to get to the solution without making a big deal out of it.

Grafana saves us hours compared to DataDog. It takes about two weeks to figure out what is going on with DataDog, but with Grafana, we just started to initialize the service, had a few issues, fixed them, and that was it. I did not have any major problems that forced me to halt everything in my work. It cost me hundreds of hours with DataDog because I needed to see all the documentation and all the special caveats they have there.

I would rate Grafana overall as an eight out of ten. It is pretty good, and I would recommend it. I would give it a ten if it were much simpler for users who just want to get a simple objective in Grafana and are not experienced with technical configuration. It would be better if users could simply state they want to see the amount of requests on a graph through an AI implementation. For example, when getting attacked, users should be able to easily filter all requests to a specific site or resource, or identify IPs that were recently attempting access.

I would describe Grafana's ease of use as a necessity to just get things fixed. We are working in a marketing company, and if something does not work great, or people are looking for the solution, I am looking to get a temporary solution until I can fix it properly, and then I can reach support if I cannot fix it for the long-term.

I have been using Grafana for quite a long time, but we have only recently started using all of its features.

I would rate Grafana's overall stability as much more stable because when it is not working, users are given a much broader oversight of what is not working, rather than talking to DataDog and asking them to check it out. When something in their dashboard does not work, because it is open source, I am able to find all the relative combinations that people are having, making it much easier for me to fix.

The size of our infrastructure affects using Grafana as it really depends on that. We are considered quite small or medium at least, so it is quite easy for us. In terms of our company, the infrastructure is using two availability zones in AWS, one is US East, so we are looking to expand. Currently, we have only two availability zones to deploy, so it is quite easy for us right now.

I do not use Grafana's support for technical issues because I have found solutions on Stack Overflow and ChatGPT helps me as well.

On a day-to-day basis with Grafana, I tend to refrain from using their support, not because of their level of professionalism.

Positive

We switched from DataDog to Grafana because we wanted to reduce the logs costs, as we are streaming approximately five million logs or even less.

I initially learned to use Grafana by working locally and seeing how it works and presenting a proof of concept. We started to make a small plan of how we could make the change, how to deploy the switch, making the changes and checking it in other environments that we have. We are working with Agile, so I have to work with development first and then we are staging it. It is a long process but eventually we made the switch quite quickly, it was just a simple day when we deployed it.

Three people manage Grafana in our organization: one is a sysadmin, another one is a DevOps full-time, and I am managing the entire operation.

The costs associated with using Grafana are somewhere in the ten thousands because we are able to control the logs in a more efficient way to reduce it. That is pretty much great for us.

My recommendation for future users of Grafana is that it is all great. I am hoping that all the companies, at least the open-source projects, do not go closed source because then users will have to find something else. That is what we love to do - we love to use open source projects and improve them for our usage, not something that follows an agenda of another company's product.

My recommendation to other users of Grafana is to not be afraid and always look online. I started using it a long time ago for different projects. Users need to learn about the basics before they can really understand what is going on. There are people who start the self-hosted web server and see all the metrics going to their server, but they do not really understand what is happening, they just see a small count of the graph. Users need to understand that configuration is necessary. I experienced this when I started and was clueless, thinking of going to DataDog. Then I realized I just did not understand it correctly - there is much more once you see a bigger horizon of things that you could do.

Overall rating: 8/10

My main use case is monitoring. We use a lot of different products for monitoring, but Grafana is specifically for monitoring Kubernetes. We use Grafana mainly for Prometheus.

The features I appreciate most are the dashboards and the integrations with multiple data sources. The feature that sets Grafana apart from its competitors is how easy it is to set up data sources. The integration helps our organization in centralizing and analyzing data from diverse sources.

Regarding joining between queries, merging between two queries that give the same information could be simple, and there are some ways to do that, but if there was something even easier, it would be great.

The deployment process was completed before I joined the organization. We have been using it for a couple of years, but it was deployed previously.

We never had any issues with Grafana at all.

Grafana's customer support is mainly for developers. We didn't need to reach out to them for troubleshooting or any issues.

Positive

We centralize all the metrics from Prometheus and also from Graphite and all other data sources. We have dashboards to integrate Grafana's real-time metrics with visualization capabilities. We're not using Grafana's role-based access control and multi-tenancy features.

Seeing the metrics helps in finding issues, such as memory leaks or spikes and some optimization. We use Grafana on a day-to-day basis to get a better look at our environments and the usage of our resources.

We don't use Grafana for alerting, just for visualization. For alerting, we have different tools. On a scale of 1-10, I would rate Grafana as 8.5.