Grafana Reviews

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)

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.

Hybrid Cloud

Amazon Web Services (AWS)

I am using it myself. My specialty is application performance monitoring, but when I'm putting systems monitoring in place for the backend, Grafana is the best solution for views and dashboards.

I have utilized Grafana's integration capabilities with Prometheus. In a previous role, we displayed some data with Elasticsearch. I have used it heavily with Zabbix integration as well. I have done Nagios in the past. It depends on the toolset that the customer has, but Grafana is very appealing because the business appreciates the views that come out of Grafana as it gives them the views they want.

I have employed real-time metric visualization capabilities. That's basically what monitoring is - a real-time view. I build up these dashboards or real-time views, but they can also be used as historical views, pulling out more data.

Grafana's role-based access control and multi-tenancy features are important for my organization. At the moment, most of our Grafana is on-premises, but we're looking at cloud options as well. Multi-tenancy is quite important because we have customers that will only want to see their information and not any other. They're not allowed to see some of the other data that we collect, so it's important to have a multi-tenant system.

The most valuable feature is Grafana's ability to plug in data or events and monitoring data from any kind of source. The graphing capability is what I use the most.

I have made use of Grafana's customizable dashboards and templating features. These features have enhanced my data visualization and analytical processes by enabling me to give real-time views to our end-users of what they're doing or what their service is doing. The historical and trend data allow people to pull up data from Grafana and from whatever source to display it and examine how an environment is being used.

I have employed real-time metric visualization capabilities for monitoring and real-time views. These dashboards can also be used as historical views, pulling out more data.

The effectiveness of Grafana in delivering real-time notifications through its alerting system is quite good. The widgets are able to highlight errors if they happen in real-time on the dashboard. The alerting functionality is improving. I generally tend to use the monitoring tool for alerting rather than Grafana, but I have had to use Grafana occasionally without any issues.

Grafana could be improved through enhancement of graphs and visualizations and providing more integrations. Grafana is going to start working with OpenTelemetry, which would be helpful to have the same sort of design across all different platforms.

I would appreciate additional features in the next release to make it closer to a perfect score. Improved templates would be beneficial in some aspects. Some of the older templates that I used to be able to get in Grafana are only available with a license.

I have been using Grafana for about five or six years.

The product has been stable. I have never really had any major issues. There have been times when the integrations don't work as smoothly, but that's usually a configuration issue, not the tool itself.

I usually solve problems by myself. I very rarely get in touch with technical support as we don't have that option.

Neutral

I used the built-in graphing from the tools themselves before Grafana. I have pushed some data into Power BI as well in the past. I have used various displays, mainly just whatever the platform provides, such as Nagios dashboards, Zabbix dashboards, or SCOM. It depends on what monitoring tool the customer has.

The initial setup for Grafana was very straightforward. I followed along and did some design work. I had to calculate the database size and figure out requirements, but beyond that, it's not difficult to install at all.

I did not evaluate other options before choosing Grafana for graphing. Grafana seemed the logical choice. There aren't many other platforms that offer the kind of flexibility and number of integrations that Grafana does. They are quite niche in the graphing and dashboarding space.

I find the pricing of Grafana to be reasonable. I have only ever used the free tier of Grafana, so I cannot comment on the pricing model itself.

Based on my experience, I would recommend Grafana to others.

I rate Grafana an 8 out of 10.

On-premises

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

Public Cloud

Amazon Web Services (AWS)

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)

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.

Public Cloud

Amazon Web Services (AWS)

We actually work with BMC and similar solutions, and while I wouldn't say that they are the same as a full, fully-fledged APM, we're working our way up using open source and everything, so I don't know how far we'll get with that. We're a government agency, and although these costly tools are very efficient and useful, it's hard for me to convince the organization about that, especially since we're not allowed to use public cloud solutions, so we have an on-prem solution, which makes us a demanding customer.We use BMC Helix with the AIOps solution, and while all of these are public domain knowledge because we have to be open with how we procure and purchase our services, I am not the one who would purchase such a system. I would be the customer to it and someone else would host it, while I work more with observability, such as the Grafana stack and the cloud-native solutions.We're working with Grafana more actively now for maybe a couple of years, and we are looking at Tempo and the tracing solutions to see if it will scale. We have metrics back-ends and everything, and we're basically late-stage discovery, early-stage implementation at large scale, with 1,400 people in our tech organization and a 14,000 people organization, where the technology side is just 10%.We get alerts from the alerting tools, and we act on them. I'm looking now at logs, Grafana, trace profiles, and while we're not yet into AI/ML and root-cause analysis, we do focus on alerting and, to some extent, plugins and app observability, mainly utilizing the basics for now.

We use Tempo for tracing and, of course, alerting and monitoring metrics. We've tested some features but we're only using a few of them actually, so I can't provide the full range of what different tools Grafana offers as an open-source project, but it's definitely useful for monitoring, alerting, logs, and analysis.Grafana integrates with some of the tools we've chosen beforehand, and while I'm not the right person to elaborate on this since I'm more focused on managing big teams, the teams working with it seem happy. We used to have performance issues, which could be due to running it in an OpenShift environment, but functionality-wise, it seems to be useful, albeit not offering the same power tools as APM such as AppDynamics.

The whole AI capability would be useful for Grafana in the future, and while I don't think we're mature yet, I would want to see improvements, especially in the tracing part, where following different requests between different services could be more powerful.

Depending on how you define use, whether it's in production or just dabbling with it, I would say that using it actively with a subset of the team has been for maybe two years.

You have to scale horizontally to deal with issues.

We might have gotten help from technical support, but I'm not sure how much since I haven't seen an invoice for purchasing support, so it's probably community open-source style.

Neutral

I would probably recommend Grafana for monitoring and analytics tasks, but since I haven't really pushed it to the limits from a non-functional perspective, I would hesitate if someone from a high-scale Telco environment asked for my recommendation.I rate Grafana a six out of ten.

On-premises

Other

For my personal usage, I do use Grafana, and it does make life easier for solving stuff.

The main benefits I have seen from using Grafana in my day-to-day activities is the visualization of the metrics, specifically Dora Metrics. Instead of saying 'I feel the team is developing bad or slow,' you can actually have metrics to support that. You can actually show that this team could work better here, in this case, or needs to focus more on peer reviews or on deployments. The visualization of the data is key.

For my personal usage of Grafana, I think there could be improvements, particularly that I use the Grafana Cloud SAS solution. The integration guides could be simpler and easier.

Regarding the clarity of the official documentation for installation, I think the official documentation, which has something called Alloy, the Alloy integration, is not that clear. It was mostly for personal usage that I'm talking about right now.

I have been using Grafana for around a year, maybe more.

Grafana is stable; however, in the previous company, we also had Grafana for a long period of time, and its stability depends on the load. Once you get to a higher load, you need to re-evaluate your architecture and put that into account.

In assessing Grafana's scalability, we started noticing logs missing or metrics not syncing in time, which indicated a bottleneck that we had to address by updating the infrastructure.

I'm not familiar with Grafana's feature in terms of community and documentation since I've been here only a few months. The community provides extensive knowledge about Grafana. You will get faster answers about Grafana than about Datadog from the community.

My advice for people who are new to Grafana or considering it is to reach out to the community mainly, as that's the primary benefit of Grafana.

Positive

The reasons for moving from Grafana to Datadog include the fact that Datadog is a SAS solution, and we don't need to manage it. I was told, and as I mentioned I'm kind of new, there was already Datadog running, so it's not creating a new one but just moving the data. The main benefit of Datadog versus Grafana is the ease of creating a dashboard, which is just simple for the user.

In the previous company, about three or four people worked on Grafana implementation. It was an ongoing effort that took several months to complete.

Grafana is easier to sell to management compared to Datadog and other solutions because it has an open-source tier, which makes it easier.

If I'm coming as a personal user, then Grafana is the best solution out there right now. But if we're looking at it as an enterprise, then when money is less of an issue, Datadog is much easier to use and implement than Grafana.

Managing and maintaining Grafana is kind of managing another application; as far as Grafana itself, you don't have much to do. It's mostly about the infrastructure, resources, and if you're deploying it in high availability, maybe more instances. So it's mostly infrastructure work and less about Grafana itself.

I rate Grafana an eight out of ten.

Public Cloud

Other

The primary use case for Grafana is to display data from Prometheus in a graphical user interface for easy monitoring and analysis of metrics. It is used for end-to-end tracing of applications and integrates with AWS services.

Grafana improves the organization by showing data from Prometheus in a user interface.

Grafana provides a user-friendly interface for viewing infrastructure metrics through dashboards. It integrates with Prometheus to pull data and offers a straightforward setup process. 

Users can monitor metrics with greater ease, and the tool aids in quickly identifying issues by providing a visual representation of data. Grafana's integration capability is straightforward, which facilitates building dashboards as needed.

Grafana could consider building its own metrics system to eliminate the reliance on other tools like Prometheus, providing a one-stop solution.

I have used Grafana for the last four to five years.

There are some instances, about one to two percent of the time, where dashboards do not work, however, these are manageable and typically attributed to the underlying data source.

Grafana is pretty scalable as it can source data from multiple providers without limitation.

The initial setup was straightforward. Those familiar with deployment find it easy to manage and deploy.

The implementation is managed by a different team, and issues with integration have been minimal.

I used the open-source version for my learning, which was free. In an enterprise setting, pricing is reasonable, as many customers use it.

I rate Grafana eight or nine out of ten. The deployment of multiple tools could be streamlined into a single solution for better efficiency.

I use Grafana to deploy OpenVideo and to write an article on Medium on how we can deploy some open source projects with CloudFormation.

I have installed Grafana to see custom metrics like the number of connected users, number of rooms, and CPU usage.

All the features are complicated. It's comparable to Terraform, and I prefer CloudWatch.

The features are complicated and not intuitive. It would be better if they made the technology easy to use without needing to read extensive documentation. The dashboard interface is too wide, and it's challenging to find buttons or features.

Grafana is stable and has great engineers.

I would rate the scalability at six out of ten. It meets some needs, however, it could be improved.

I prefer CloudWatch over Grafana due to its ease of use.

The initial setup is too complicated. Even at a junior level, I found it challenging to navigate without guidance.

I prefer CloudWatch with custom metrics.

Make the interface intuitive and user-friendly. Highlight features clearly on the interface so that users can easily find and access them.

I'd rate the solution four out of ten.

Public Cloud