In Grafana, some tools have granular event analysis, but all metric dashboard and SLO views are pre-aggregated and cannot be parsed by granular high-cardinality fields.

Honeycomb lets you instantly filter by high-cardinality data (userID, hostID) from every view, such as trace waterfalls and metric counts.

Honeycomb’s rich context analysis makes debugging faster from any starting point. Dramatically speed up investigations by automatically detecting hidden outliers with BubbleUp. Highlight anomalies on any heatmap visualization or query result, and BubbleUp will surface which events have the highest degree of difference across thousands of high-cardinality and high-dimensionality events. Because BubbleUp is an easy-to-grasp visualization tool, any team member can quickly identify outliers for further investigation.

Grafana does not make it easy to find hidden attributes behind any issue or anomalous trend in any data. To retrieve important context, your team has to cycle between separate data sources and context stored in separate formats—a time-consuming task that can slow down debugging and requires zooming in and out multiple times.

With Honeycomb, you can query across all fields instantly from production without pre-indexing and find patterns in every granular attribute or datapoint.

With Grafana, complex querying is siloed to Parquet and PromQL experts and restricted within each pillar of telemetry. Automatic pattern detection is based on aggregated metrics that miss the nuances in events. Instant analysis is only performant if pre-selected for dedicated column storage.

Honeycomb provides event-based SLOs, enabling higher-fidelity alerts that give teams insight into the underlying “why.” When errors begin, Honeycomb SLOs can ping your engineers in an escalating series of alerts. Unlike other vendors, Honeycomb SLOs reveal the underlying event data so anyone can quickly see how to improve performance against a particular objective.

With Grafana, SLO counts are aggregated from streamed events or metrics, and do not retain full context.

With Honeycomb, all users enjoy full analysis, usage, team history, and AI assistance features with one workflow to learn and adopt for all signal sources.

With Grafana, instances, resourcing, and user environments can get very fragmented. It’s hard to investigate in collaboration or follow the breadcrumbs of your teammates.

Grafana and Honeycomb both offer RUM dashboards and capabilities.

Going beyond RUM capabilities, Honeycomb for Frontend Observability gives engineers the unified, granular data they need to quickly pinpoint issues across their stack and understand user impact. Click through the standard RUM views to the event attributes and queries behind them.

Unlike with other tools, engineers using Honeycomb don’t need to be experts in a specific query language to ask intelligent questions about their systems—Query Assistant enables them to ask in plain English. An engineer can, for example, ask for slow endpoints by status code and Honeycomb will run a relevant query and return results to help start the investigation.

Grafana offers AI assistance but does not execute queries with access to its features and visuals from a language prompt.