With Honeycomb, all telemetry is available instantly for querying, giving you live feedback on both production or dev environments. You can run complex queries across all fields instantly without going through an indexing selection or wait time.

Datadog requires adding custom metrics and pre-indexing what you’ll want to analyze by to access all the context you need. Otherwise, you’re searching logs, where wait times are unacceptable for live incidents.

With Honeycomb, every field and value in your telemetry is available for complex, performant querying operations with unlimited unique values (cardinality) and high dimensionality. Define, visualize, or get a custom metric on any complex query from your real-time data, with unrestricted Group By analysis on multiple fields.

For fast analysis and granular detection, Datadog requires indexing data and adding custom metrics, which impact your bill.

Dramatically speed up debugging 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. 

Datadog detects metric spikes (that may be random noise) but does not offer comprehensive analysis of all fields within anomalous data. To retrieve important context, a user has to cycle between reading context from separate data sources and relying on correlations of known fields that are already being tracked—a time-consuming task that can slow down debugging and get expensive.

Vendors like Datadog use metric-based SLOs, meaning they simply check a count with no context on severity. Honeycomb’s alerts are directly tied to the reality that people are experiencing, so you can better understand severity and meet users’ high performance expectations. 

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.

Honeycomb is a leader of the OpenTelemetry (OTel) project, a vendor-agnostic framework that enables teams to instrument, collect, and export rich telemetry data. Prior to OTel, teams were stuck using vendors’ proprietary SDKs; with OTel, you can instrument once and send to multiple tools if needed, avoiding vendor lock-in. 

Datadog supports OpenTelemetry—with limitations. To unlock Datadog’s full debugging feature-set requires the use of their proprietary agent, which creates vendor lock-in.

Honeycomb defaults to 60 days of data retention for all high-speed debugging data, with reasonable prices on extra retention.

Datadog indexed fields and trace data are available for less than 30 days, and extra retention gets expensive fast.

With Honeycomb, you simply pay by event volume—not by seats, servers, or fields—solving the cost/visibility tradeoff. Unlike legacy metrics and monitoring tools, Honeycomb enables you to capture unlimited custom attributes for debugging, with no impact on spend. Honeycomb charges by number of events, not how much data each event contains.

Datadog’s pricing, however, can result in costly overages from host count, 
indexed fields, custom metric count, and memory. Their pricing has been criticized in recent months, and we go in detail in our cost-visibility tradeoff blog post.

With Honeycomb, customers receive burst protection for unexpected spikes that would impact their event usage.

With Datadog, high watermark billing for APM and infra monitoring means that spikes in telemetry will impact your budget.

Unlike with other tools, engineers 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.

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