Grafana Enterprise

What do you like best about the product?

I like its clean UI, with dashboards, metrics, and logs. It makes it easy for me to track my work data and performance.

What do you dislike about the product?

I don’t like its pricing; they could make it more affordable for the features they’re offering.

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

It helps me check my dashboards, metrics, and log files all in one place, instead of having to use multiple software tools.

What do you like best about the product?

What really stands out to me is the level of visibility it provides into what’s actually happening behind the scenes during testing cycles. When I’m running automated test suites, being able to correlate test failures with real-time system metrics makes root-cause analysis much faster. I no longer have to dig through separate logs and monitoring tools—everything I need is visible in one dashboard. The alerting setup has also been a game-changer, helping me catch environment issues early before they derail an entire test run.

What do you dislike about the product?

Getting dashboards set up the way I actually want has taken more time than I expected, especially if you’re not already familiar with querying the underlying data sources. There’s definitely a learning curve if you’re coming from a testing background rather than a DevOps one, and it can take a while to get comfortable with how everything fits together.

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

In the past, when a test failed intermittently, it took a lot of manual digging to figure out whether it was a real bug, a flaky test, or an environment issue. Now I can pull up system performance data right alongside the test execution results and spot patterns much faster—for example, noticing that a spike in failures lines up with a memory leak or a slow database query. It’s cut down on a lot of back-and-forth with the dev team, since I can actually show the data instead of just saying, “it failed again.” Overall, it’s made root cause analysis during regression cycles feel far less like guesswork.

What do you like best about the product?

As a backend engineer building an AI-driven insurance claims platform, I really value Grafana Labs' ability to aggregate metrics from our diverse tech stack. The custom dashboards we build are essential for tracking response times and visualising our system's performance. Being able to see container health, message queue depth, and model influence time all on one screen helps us identify bottlenecks in our microservices. Hooking Prometheus up to Grafana gave us clear panels to track latency percentiles and container RAM usage across the board. Grafana provides a unified telemetry layer that significantly reduces our mean time to detection, and the vendor-agnostic plugin ecosystem is a huge advantage for seamlessly connecting our software stack.

What do you dislike about the product?

The learning curve for LogQL is undeniably steep. It required a bit of learning to get the syntax right, so it wasn't just plug-and-play. There were some challenges, especially when it came to setting things up for the first time. But once I got a handle on it, things got better.

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

In our claim categorisation system, Grafana Labs solves the issue of diving into isolated logs by providing a unified telemetry layer that drastically reduces our mean time to detection. It helps us pinpoint bottlenecks and track microservice performance, improving issue resolution before impacting our users.

What do you like best about the product?

I like that Grafana has a good UI that brings dashboards, metrics, and traces together in one place. The performance is good enough for what I need.

What do you dislike about the product?

I dislike grafana's pricing and also it took me some time to understand and learn it's features and options.

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

Grafana helps me find performance issues, errors and other application problems without switching between different monitoring tools.

What do you like best about the product?

I use Grafana daily as our central monitoring hub for both infrastructure and model health. We rely on it to track GPU utilization during massive training jobs, monitor inference latency in real-time, and keep a close eye on data drift for our production models. It pulls metrics from Prometheus and our custom databases, giving us a single pane of glass to understand exactly what is happening across our entire machine learning ecosystem without having to dig through raw log files.The flexibility and visual depth Grafana offers are simply unmatched. I can build highly specific dashboards that perfectly fit the needs of my team, whether we are tracking a specific node's memory consumption or visualizing confidence scores from a newly deployed computer vision model. The sheer volume of community plugins means we can connect it to almost any obscure data source we throw at it. On top of that, the alerting system is incredibly reliable. Being able to easily route an alert directly to our team's Slack channel the exact moment a model's error rate spikes has been a massive upgrade for our incident response times.

What do you dislike about the product?

The biggest hurdle with Grafana is the steep learning curve, particularly when it comes to writing complex queries in PromQL or Flux just to get your data to display the way you want. It can be a very frustrating and time-consuming experience for newer engineers who just want to build a simple graph. Furthermore, as our team has grown, managing dashboard versions and user permissions has become surprisingly messy. It is far too easy for someone to accidentally edit a shared dashboard and break a critical operational view, and the rollback process could definitely be smoother. We also occasionally run into annoying compatibility headaches where a third-party plugin we rely on suddenly breaks after a major Grafana version update.

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

Before Grafana, we struggled heavily with silent model degradation. We once had a recommendation engine serving stale results for two days because a background data pipeline stalled, but the server itself stayed up so our basic checks entirely missed it. Grafana solved this by allowing us to visualize data freshness and prediction distributions continuously on one screen. Just recently, a custom dashboard alerted us to a severe memory leak in a new deep learning training job within minutes of it starting. We were able to kill the job and patch the code before it exhausted the resources on our shared Kubernetes cluster, which saved the company significant compute costs and prevented a wider outage.