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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.
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.
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.
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.
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.
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.
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.
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!
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?
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!
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.
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
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.
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.
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.
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
Pros of Datadog
- Monitoring for many apps (databases, web servers, etc)139
- Easy setup107
- Powerful ui87
- Powerful integrations84
- Great value70
- Great visualization54
- Events + metrics = clarity46
- Notifications41
- Custom metrics41
- Flexibility39
- Free & paid plans19
- Great customer support16
- Makes my life easier15
- Adapts automatically as i scale up10
- Easy setup and plugins9
- Super easy and powerful8
- AWS support7
- In-context collaboration7
- Rich in features6
- Docker support5
- Cost4
- Full visibility of applications4
- Monitor almost everything4
- Cute logo4
- Automation tools4
- Source control and bug tracking4
- Simple, powerful, great for infra4
- Easy to Analyze4
- Best than others4
- Best in the field3
- Expensive3
- Good for Startups3
- Free setup3
- APM2
Pros of Honeycomb
- Powerful UI2
- High-Cardinality Data2
- BubbleUp + Heat maps2
- Better Value1
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Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of Honeycomb
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