Modern systems
need modern tools

Modern systems
need modern tools

Modern systems
need modern tools

Honeycomb's datastore and query engine are purpose-built to give you fast answers to any question. Quickly understand performance and behavior down to a single user’s experience or by grouping together any combination of attributes. Now you can solve complex engineering mysteries you couldn’t solve before with traditional monitoring, metrics, and logging tools.

See how Honeycomb's approach compares to the best APM tools in various troubleshooting and development scenarios:

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Compare: Analyzing Telemetry Data

Easily query and analyze complex telemetry data in seconds, with no limits on high-cardinality data or how many dimensions you group results by.

Capability

Honeycomb Approach

APM Approach

Group results by unlimited high-cardinality dimensions, query results still return in less than 3s

Slow, expensive analysis (if at all), costly dimensions, with pre-indexing required

Speed of query results

Fast, no matter how big your data set or how many high-cardinality dimensions you analyze

Results only return quickly when your data set is small

Speed of data query availability after ingestion

Newly ingested telemetry is available to query in under 5 seconds (near real-time)

Some telemetry available after seconds, some vendors need several minutes

Compare: Alerting for Issues in Production

Avoid on-call fatigue using debuggable alerts that matter the most to your users and warn you before service levels fall below acceptable targets.

Capability

Honeycomb Approach

APM Approach

Deciding what to monitor

Monitor what's important to users with service-level objectives (SLOs)

Monitor for known symptoms of past failures

Taking action on an alert

Debug SLO-triggered alerts using the same interface in the same tool

One tool triggers alerts, debugging using a different tool

Decreasing alert noise

Trigger single alerts before user experience falls under acceptable targets

Many error alerts are grouped together with AI deciding priority

Measuring failure or success

Uses individual requests to measure requests succeeded vs. requests failed

Uses metrics to measure good minutes vs. bad minutes

Compare: Isolating the Source of Issues

Any team member can use our consistent workflow for fast querying, heatmaps, and correlation detection to intuitively find the sources of novel problems.

Capability

Honeycomb Approach

APM Approach

Correlation detection

Multi-dimensional heatmaps, powered by machine analysis, let you decide what matters

Trust AI suggestions or single-dimension range selection

Triage workflow

Use one workflow, driven by querying and first principles, to surface the relevant telemetry data you need

Analyze different dashboards, then switch to other tools based on intuition

Know where to start

Quickly find the relevant data you need, regardless of prior experience

Relies on intuition and prior experience to determine which dashboards are most relevant

Finding telemetry data showing why an issue is occuring

Follow one workflow to surface relevant data and click directly into the telemetry you need

Manually check related dashboards, check logs, check traces, context-switch between many different tools

Compare: Organizational Adoption

New users can adopt Honeycomb without deep system familiarity, proprietary query languages, closed instrumentation, or any additional seat costs. We’re designed for easy adoption.

Capability

Honeycomb Approach

APM Approach

Supporting vendor-neutral standards

OpenTelemetry is the de facto standard for instrumentation with Honeycomb

Recommend proprietary agents and libraries, but may support OpenTelemetry

Cross-team adoption

Unlimited hosts, seats, users, etc. Simple event-based pricing encourages adoption

Price increases the more you use it (per host, seats, service, teams, etc.)

System familiarity required to triage

Anyone can triage correctly, regardless of familiarity

The less familiarity you have, the less likely you will triage correctly

Querying accessibility

Build queries intuitively with a visual UI

Learn proprietary query languages

Intended audience

Workflows for operations, development, product, support teams, and more

Focused on the needs of ops teams, with developer accessible tools like dashboards

Informing stakeholders

Technical teams and exec teams aligned on business goals with SLOs

Simplified exec dashboards to report current state as measured by metrics

Organizational memory

Learn by following past investigative steps with Team History; pin and save what’s useful

See and share dashboards with static views, lacking context for how they’re being used / what they mean

Compare: Analyzing Telemetry Data

Easily query and analyze complex telemetry data in seconds, with no limits on high-cardinality data or how many dimensions you group results by.

Group results by unlimited high-cardinality dimensions, query results still return in less than 3s

Speed of query results

Fast, no matter how big your data set or how many high-cardinality dimensions you analyze

Speed of data query availability after ingestion

Newly ingested telemetry is available to query in under 5 seconds (near real-time)

Compare: Alerting for Issues in Production

Avoid on-call fatigue using debuggable alerts that matter the most to your users and warn you before service levels fall below acceptable targets.

Deciding what to monitor

Monitor what's important to users with service-level objectives (SLOs)

Taking action on an alert

Debug SLO-triggered alerts using the same interface in the same tool

Decreasing alert noise

Trigger single alerts before user experience falls under acceptable targets

Measuring failure or success

Uses individual requests to measure requests succeeded vs. requests failed

Compare: Isolating the Source of Issues

Any team member can use our consistent workflow for fast querying, heatmaps, and correlation detection to intuitively find the sources of novel problems.

Correlation detection

Multi-dimensional heatmaps, powered by machine analysis, let you decide what matters

Triage workflow

Use one workflow, driven by querying and first principles, to surface the relevant telemetry data you need

Know where to start

Quickly find the relevant data you need, regardless of prior experience

Finding telemetry data showing why an issue is occuring

Follow one workflow to surface relevant data and click directly into the telemetry you need

Compare: Organizational Adoption

New users can adopt Honeycomb without deep system familiarity, proprietary query languages, closed instrumentation, or any additional seat costs. We’re designed for easy adoption.

Supporting vendor-neutral standards

OpenTelemetry is the de facto standard for instrumentation with Honeycomb

Cross-team adoption

Unlimited hosts, seats, users, etc. Simple event-based pricing encourages adoption

System familiarity required to triage

Anyone can triage correctly, regardless of familiarity

Querying accessibility

Build queries intuitively with a visual UI

Intended audience

Workflows for operations, development, product, support teams, and more

Informing stakeholders

Technical teams and exec teams aligned on business goals with SLOs

Organizational memory

Learn by following past investigative steps with Team History; pin and save what’s useful

Compare: Analyzing Telemetry Data

Easily query and analyze complex telemetry data in seconds, with no limits on high-cardinality data or how many dimensions you group results by.

Slow, expensive analysis (if at all), costly dimensions, with pre-indexing required

Speed of query results

Results only return quickly when your data set is small

Speed of data query availability after ingestion

Some telemetry available after seconds, some vendors need several minutes

Compare: Alerting for Issues in Production

Avoid on-call fatigue using debuggable alerts that matter the most to your users and warn you before service levels fall below acceptable targets.

Deciding what to monitor

Monitor for known symptoms of past failures

Taking action on an alert

One tool triggers alerts, debugging using a different tool

Decreasing alert noise

Many error alerts are grouped together with AI deciding priority

Measuring failure or success

Uses metrics to measure good minutes vs. bad minutes

Compare: Isolating the Source of Issues

Any team member can use our consistent workflow for fast querying, heatmaps, and correlation detection to intuitively find the sources of novel problems.

Correlation detection

Trust AI suggestions or single-dimension range selection

Triage workflow

Analyze different dashboards, then switch to other tools based on intuition

Know where to start

Relies on intuition and prior experience to determine which dashboards are most relevant

Finding telemetry data showing why an issue is occuring

Manually check related dashboards, check logs, check traces, context-switch between many different tools

Compare: Organizational Adoption

New users can adopt Honeycomb without deep system familiarity, proprietary query languages, closed instrumentation, or any additional seat costs. We’re designed for easy adoption.

Supporting vendor-neutral standards

Recommend proprietary agents and libraries, but may support OpenTelemetry

Cross-team adoption

Price increases the more you use it (per host, seats, service, teams, etc.)

System familiarity required to triage

The less familiarity you have, the less likely you will triage correctly

Querying accessibility

Learn proprietary query languages

Intended audience

Focused on the needs of ops teams, with developer accessible tools like dashboards

Informing stakeholders

Simplified exec dashboards to report current state as measured by metrics

Organizational memory

See and share dashboards with static views, lacking context for how they’re being used / what they mean

Learn more
about Honeycomb

Learn more
about Honeycomb

Learn more
about Honeycomb

Comparing Honeycomb with another solution?

Read about how we’re different from tools like
Lightstep, Datadog, Splunk, or New Relic.
Or ask us to show you
how in a demo.

book a demo

Don’t just take our word for it

Teams who switch to Honeycomb see significant increases in incremental revenue,
faster incident response, reduction in severe incidents, and decreased developer
burnout and churn. See what they have to say:

What would take me hours to debug before Honeycomb now takes me a couple of minutes, if that. The speed to get to the actual area where you’ve seen something, there’s just nothing like it.

There was no real way to find 
possible culprits with our classic APM. We had to know what we needed to find before we could find it—a dead end. With Honeycomb, our producers were delighted to see issues solved so quickly.

A lot of the data we care about most, like app IDs, have very high cardinality. We needed to drill down and group by data that helped us understand what individual customers are seeing happen with specific apps. Honeycomb’s ability to handle that turned out to be huge and incredibly useful for our future.

I keep thinking back to older 
problems, many took days or weeks to understand–we could have solved them in moments with Honeycomb.

Before we had Honeycomb, you used to have to know the schemas so you could optimize queries; you had to know what you needed to ask ahead of time.

Before Honeycomb, we would just speculate wildly about who was impacted by a given issue, or what changes would affect which customer. Now we know more, and guess less.

We’ve been told so many times that logging customer ID was impossible. Honeycomb just handles it. There have been outages where, without Honeycomb, it would have taken us significantly longer to get to the answer.

Honeycomb moved us in a direction of better and happier engineering outcomes. Honeycomb really fills a gap with my existing tools by providing insight into what’s actually going on inside applications. You look at the graphs and go, my application is doing what!? You can then go ahead and fix it.

Honeycomb saved us days of debugging.

What would take me hours to debug before Honeycomb now takes me a couple of minutes, if that. The speed to get to the actual area where you’ve seen something, there’s just nothing like it.

There was no real way to find 
possible culprits with our classic APM. We had to know what we needed to find before we could find it—a dead end. With Honeycomb, our producers were delighted to see issues solved so quickly.

A lot of the data we care about most, like app IDs, have very high cardinality. We needed to drill down and group by data that helped us understand what individual customers are seeing happen with specific apps. Honeycomb’s ability to handle that turned out to be huge and incredibly useful for our future.

I keep thinking back to older 
problems, many took days or weeks to understand–we could have solved them in moments with Honeycomb.

Before we had Honeycomb, you used to have to know the schemas so you could optimize queries; you had to know what you needed to ask ahead of time.

Before Honeycomb, we would just speculate wildly about who was impacted by a given issue, or what changes would affect which customer. Now we know more, and guess less.

We’ve been told so many times that logging customer ID was impossible. Honeycomb just handles it. There have been outages where, without Honeycomb, it would have taken us significantly longer to get to the answer.

Honeycomb moved us in a direction of better and happier engineering outcomes. Honeycomb really fills a gap with my existing tools by providing insight into what’s actually going on inside applications. You look at the graphs and go, my application is doing what!? You can then go ahead and fix it.

Honeycomb saved us days of debugging.

What would take me hours to debug before Honeycomb now takes me a couple of minutes, if that. The speed to get to the actual area where you’ve seen something, there’s just nothing like it.

There was no real way to find 
possible culprits with our classic APM. We had to know what we needed to find before we could find it—a dead end. With Honeycomb, our producers were delighted to see issues solved so quickly.

A lot of the data we care about most, like app IDs, have very high cardinality. We needed to drill down and group by data that helped us understand what individual customers are seeing happen with specific apps. Honeycomb’s ability to handle that turned out to be huge and incredibly useful for our future.

I keep thinking back to older 
problems, many took days or weeks to understand–we could have solved them in moments with Honeycomb.

Before we had Honeycomb, you used to have to know the schemas so you could optimize queries; you had to know what you needed to ask ahead of time.

Before Honeycomb, we would just speculate wildly about who was impacted by a given issue, or what changes would affect which customer. Now we know more, and guess less.

We’ve been told so many times that logging customer ID was impossible. Honeycomb just handles it. There have been outages where, without Honeycomb, it would have taken us significantly longer to get to the answer.

Honeycomb moved us in a direction of better and happier engineering outcomes. Honeycomb really fills a gap with my existing tools by providing insight into what’s actually going on inside applications. You look at the graphs and go, my application is doing what!? You can then go ahead and fix it.

Honeycomb saved us days of debugging.