Compare Honeycomb vs. Datadog: Observability vs APM

Honeycomb is an observability solution with a modern approach that’s fundamentally different from other platforms. It’s built to help teams find and solve unexpected problems within their ever-evolving cloud applications without running into roadblocks and production slowdowns.

With Honeycomb’s observability approach, users can speed up debugging dramatically, escape escalating costs, and avoid the cost/visibility tradeoff in observability tooling by removing the need for disparate tools and data sources. Honeycomb is the only tool you need.

With Honeycomb, companies can experience profound cost savings:

$1.9M

increased revenue due to higher uptime and performance

$940,000

cost savings from avoided employee churn

$422,000

decrease in legacy solution dependence and tool sprawl

$2M

cost savings from faster incident response

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Datadog is a SaaS platform that integrates and automates infrastructure monitoring, application performance monitoring, log management, real user monitoring, and more to provide observability and security for their customers.

Compare our capabilities

Capability	Honeycomb	Datadog
Transcends three pillars approach for one single source of truth
Real-time querying and pattern detection on production code
Instant, unrestricted analysis and parsing of highly specific values
Faster debugging and anomaly analysis to drill down and filter on every graph view
Actionable alerts with accurate service level objectives (SLOs)
Full compatibility with OpenTelemetry
Long retention on debugging data
Pricing for inclusion of contextual data to prepare for any unknowns
No peak usage billing for data
Real user monitoring
AI-powered querying capabilities to facilitate engineer adoption
Last updated: 06/12/2024

Dive deeper into the differences

Transcends three pillars approach for one single source of truth

While some orgs like to have Datadog’s logs, metrics, traces, network, and real user monitoring signals all stored separately and correlating together, it’s easy to see how others think that many separate tabs convolute what’s really important.

With Honeycomb, wide events make it possible to rely on a singular debugging solution, consolidating multiple use cases into one workflow. Honeycomb’s trace/log events stitch together events to illuminate what happened within the flow of system interactions. And unlike metrics, which provide indirect signals about user experience, tracing in Honeycomb models how your users actually interact with your system, surfacing relevant events by comparing across all columns. And unlike metrics-based tools, Honeycomb never breaks when you need to analyze highly-contextual data within your system.

Real-time querying and pattern detection on production code

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.

Instant, unrestricted analysis and parsing of highly specific values

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.

Faster debugging and anomaly analysis to drill down and filter on every graph view

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.

Actionable alerts with accurate service level objectives (SLOs)

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.

Full compatibility with OpenTelemetry

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.

Long retention on debugging data

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.

Pricing for inclusion of contextual data to prepare for any unknowns

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.

No peak usage billing for data

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.

AI-powered querying capabilities to facilitate engineer adoption

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.