You can query your profile data using the built-in data source query editor or you can use the open source Grafana Explore Profiles app.
## Explore Profiles
[Explore Profiles](ref:explore-profiles) is a native Grafana application designed to integrate seamlessly with Pyroscope, the open source continuous profiling platform, providing a smooth, queryless experience for browsing and analyzing profiling data.
You can use Explore Profiles in Grafana Cloud or in your own Grafana instance.
For more information, refer to [Access or install Explore Profiles](ref:explore-profiles-install).
### Use cases
There are several different modes for viewing, analyzing, and comparing profiling data.
The main use cases are the following:
- Proactive: Cutting costs, addressing latency issues, or optimizing memory usage for applications
- Reactive: Resolving incidents with line-level accuracy or debugging active latency/memory issues
Explore Profiles provides an intuitive interface to specifically support these use cases.
You get a holistic view of all of your services and how they're functioning, but also the ability to drill down for more targeted root cause analysis.
Explore Profiles offers a convenient platform to analyze profiles and get insights that are impossible to get from using other traditional signals like logs, metrics, or tracing.
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### Continuous profiling
While code profiling has been a long-standing practice, continuous profiling represents a modern and more advanced approach to performance monitoring.
This technique adds two critical dimensions to traditional profiles:
Time
: Profiling data is collected _continuously_, providing a time-centric view that allows querying performance data from any point in the past.
Metadata
: Profiles are enriched with metadata, adding contextual depth to the performance data.
These dimensions, coupled with the detailed nature of performance profiles, make continuous profiling a uniquely valuable tool.
### Flame graphs
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Flame graphs help you visualize resource allocation and performance bottlenecks, and you even get suggested recommendations and performance fixes via AI-driven flame graph analysis, as well as line-level insights from our GitHub integration.
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On views with a flame graph, you can use **Explain flame graph** to provide an AI flame graph analysis that explains the performance bottleneck, root cause, and recommended fix.
For more information, refer to [Flame graph AI](https://grafana.com/docs/grafana-cloud/monitor-applications/profiles/flamegraph-ai/).
While the label selector can be left empty to query all profiles without filtering by labels, you must select a profile type or app for the query to be valid.
1. Select a query type to return the profile data. Data is shown in the [Flame Graph](ref:flame-graph), metric data visualized in a graph, or both. You can only select both options in Explore. The panels used on dashboards allow only one visualization.
In conjunction with **Group by**, you can set a positive number in the **Limit** input to limit the maximum number of time series returned by the data source. The series returned are always ordered by descending value for the total aggregated data over the time period.
