Datadog vs Honeycomb

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Datadog vs Honeycomb: What are the differences?

Introduction

Datadog and Honeycomb are both monitoring and observability platforms that help businesses gain insights and visibility into their applications and infrastructure. Although they share some similarities, there are several key differences between the two platforms that make each unique in its own way.

  1. Data Exploration and Querying: Honeycomb is specifically designed for high-cardinality data exploration and querying, making it a powerful tool for deep-dive investigations. It allows users to analyze and query logs and events in a highly flexible and customizable manner, enabling detailed exploration of the data. On the other hand, Datadog focuses more on structured metrics and provides a wide range of pre-built integrations and dashboards for quick visualization of metric data.

  2. Sampling Approach: Honeycomb uses a deterministic sampling approach, which means that for a given trace, the same spans are always sampled. This ensures consistent and repeatable sampling results, making it easier to analyze and compare different traces. In contrast, Datadog uses a probabilistic sampling approach, where each span is sampled individually, leading to variations in the sampled data for different traces.

  3. Pricing Model: Datadog's pricing model is based primarily on a host-based approach, where the cost is determined by the number of hosts being monitored. This can be beneficial for organizations with a large number of hosts but can become expensive when monitoring high-cardinality data. In contrast, Honeycomb's pricing is based on data volume, allowing users to capture and analyze large amounts of data without worrying about additional costs based on the number of hosts.

  4. Alerting and Notification: Datadog provides a robust alerting and notification system, allowing users to set up alerts based on various conditions and receive notifications via multiple channels. It offers a wide range of built-in alerting options and integrations with popular communication tools. While Honeycomb provides basic alerting capabilities, it is not as feature-rich as Datadog and may require additional tools or integrations to achieve comprehensive alerting and notification functionality.

  5. Integration Ecosystem: Datadog has a vast ecosystem of integrations, providing seamless integration with popular cloud platforms, databases, and other third-party tools. This allows users to easily collect and analyze data from various sources within a single platform. Honeycomb, although it supports integrations with some external tools, has a more limited integration ecosystem compared to Datadog.

  6. User Interface and Visualization: Datadog offers a user-friendly and intuitive interface with pre-built visualizations and dashboards, making it easier for users to get started and quickly gain insights from their data. It provides a wide range of customizable visualization options and widgets to create interactive and informative dashboards. Honeycomb, although it also offers a user-friendly interface, focuses more on providing raw data exploration capabilities, with less emphasis on pre-built visualizations and dashboards.

In summary, Honeycomb excels in high-cardinality data exploration and querying, utilizes deterministic sampling, offers a data-volume-based pricing model, provides basic alerting capabilities, has a limited integration ecosystem, and focuses on raw data exploration. On the other hand, Datadog emphasizes structured metrics, employs probabilistic sampling, adopts a host-based pricing model, offers robust alerting and notification capabilities, has a vast integration ecosystem, and emphasizes pre-built visualizations and dashboards.

Advice on Datadog and Honeycomb
Farzeem Diamond Jiwani
Software Engineer at IVP · | 8 upvotes · 1.4M views
Needs advice
on
AppDynamicsAppDynamicsDatadogDatadog
and
DynatraceDynatrace

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

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Needs advice
on
DatadogDatadogNew RelicNew Relic
and
SysdigSysdig

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

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Replies (3)
Recommends
on
DatadogDatadog

Can't say anything to Sysdig. I clearly prefer Datadog as

  • they provide plenty of easy to "switch-on" plugins for various technologies (incl. most of AWS)
  • easy to code (python) agent plugins / api for own metrics
  • brillant dashboarding / alarms with many customization options
  • pricing is OK, there are cheaper options for specific use cases but if you want superior dashboarding / alarms I haven't seen a good competitor (despite your own Prometheus / Grafana / Kibana dog food)

IMHO NewRelic is "promising since years" ;) good ideas but bad integration between their products. Their Dashboard query language is really nice but lacks critical functions like multiple data sets or advanced calculations. Needless to say you get all of that with Datadog.

Need help setting up a monitoring / logging / alarm infrastructure? Send me a message!

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Maik Schröder
Recommends
on
InstanaInstana

Hi Medeti,

you are right. Building based on your stack something with open source is heavy lifting. A lot of people I know start with such a set-up, but quickly run into frustration as they need to dedicated their best people to build a monitoring which is doing the job in a professional way.

As you are microservice focussed and are looking for 'low implementation and maintenance effort', you might want to have a look at INSTANA, which was built with modern tool stacks in mind. https://www.instana.com/apm-for-microservices/

We have a public sand-box available if you just want to have a look at the product once and of course also a free-trial: https://www.instana.com/getting-started-with-apm/

Let me know if you need anything on top.

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Attila Fulop
Management Advisor at artkonekt · | 2 upvotes · 310.3K views

I have hands on production experience both with New Relic and Datadog. I personally prefer Datadog over NewRelic because of the UI, the Documentation and the overall user/developer experience.

NewRelic however, can do basically the same things as Datadog can, and some of the features like alerting have been present in NewRelic for longer than in Datadog. The cool thing about NewRelic is their last-summer-updated pricing: you no longer pay per host but after data you send towards New Relic. This can be a huge cost saver depending on your particular setup

https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-pricing-billing/new-relic-one-pricing-billing

I'd go for Datadog, but given you have lots of containers I would also make a cost calculation. If the price difference is significant and there's a budget constraint NewRelic might be the better choice.

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Decisions about Datadog and Honeycomb
Kamil Kowalski
Lead Architect at Fresha · | 3 upvotes · 210.9K views

Coming from a Ruby background, we've been users of New Relic for quite some time. When we adopted Elixir, the New Relic integration was young and missing essential features, so we gave AppSignal a try. It worked for quite some time, we even implemented a :telemetry reporter for AppSignal . But it was difficult to correlate data in two monitoring solutions, New Relic was undergoing a UI overhaul which made it difficult to use, and AppSignal was missing the flexibility we needed. We had some fans of Datadog, so we gave it a try and it worked out perfectly. Datadog works great with Ruby , Elixir , JavaScript , and has powerful features our engineers love to use (notebooks, dashboards, very flexible alerting). Cherry on top - thanks to the Datadog Terraform provider everything is written as code, allowing us to collaborate on our Datadog setup.

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Attila Fulop

I haven't heard much about Datadog until about a year ago. Ironically, the NewRelic sales person who I had a series of trainings with was trash talking about Datadog a lot. That drew my attention to Datadog and I gave it a try at another client project where we needed log handling, dashboards and alerting.

In 2019, Datadog was already offering log management and from that perspective, it was ahead of NewRelic. Other than that, from my perspective, the two tools are offering a very-very similar set of tools. Therefore I wouldn't say there's a significant difference between the two, the decision is likely a matter of taste. The pricing is also very similar.

The reasons why we chose Datadog over NewRelic were:

  • The presence of log handling feature (since then, logging is GA at NewRelic as well since falls 2019).
  • The setup was easier even though I already had experience with NewRelic, including participation in NewRelic trainings.
  • The UI of Datadog is more compact and my experience is smoother.
  • The NewRelic UI is very fragmented and New Relic One is just increasing this experience for me.
  • The log feature of Datadog is very well designed, I find very useful the tagging logs with services. The log filtering is also very awesome.

Bottom line is that both tools are great and it makes sense to discover both and making the decision based on your use case. In our case, Datadog was the clear winner due to its UI, ease of setup and the awesome logging and alerting features.

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Benoit Larroque
Principal Engineer at Sqreen · | 4 upvotes · 408.4K views

I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).

The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight

NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon

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Pros of Datadog
Pros of Honeycomb
  • 137
    Monitoring for many apps (databases, web servers, etc)
  • 107
    Easy setup
  • 87
    Powerful ui
  • 83
    Powerful integrations
  • 70
    Great value
  • 54
    Great visualization
  • 46
    Events + metrics = clarity
  • 41
    Custom metrics
  • 41
    Notifications
  • 39
    Flexibility
  • 19
    Free & paid plans
  • 16
    Great customer support
  • 15
    Makes my life easier
  • 10
    Adapts automatically as i scale up
  • 9
    Easy setup and plugins
  • 8
    Super easy and powerful
  • 7
    AWS support
  • 7
    In-context collaboration
  • 6
    Rich in features
  • 5
    Docker support
  • 4
    Cost
  • 4
    Full visibility of applications
  • 4
    Monitor almost everything
  • 4
    Cute logo
  • 4
    Automation tools
  • 4
    Source control and bug tracking
  • 4
    Simple, powerful, great for infra
  • 4
    Easy to Analyze
  • 4
    Best than others
  • 3
    Best in the field
  • 3
    Expensive
  • 3
    Good for Startups
  • 3
    Free setup
  • 2
    APM
  • 2
    Powerful UI
  • 2
    High-Cardinality Data
  • 2
    BubbleUp + Heat maps
  • 1
    Better Value

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Cons of Datadog
Cons of Honeycomb
  • 19
    Expensive
  • 4
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated
    Be the first to leave a con

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    What is Datadog?

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!

    What is Honeycomb?

    We built Honeycomb to answer the hard questions that come up when you're trying to operate your software–to debug microservices, serverless, distributed systems, polyglot persistence, containers, and a world of fast, parallel deploys.

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    What are some alternatives to Datadog and Honeycomb?
    New Relic
    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    Grafana
    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
    AppDynamics
    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
    See all alternatives