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Bugsnag vs Datadog: What are the differences?
Introduction
Bugsnag and Datadog are both popular tools used for monitoring and troubleshooting issues in software and applications. While they share some similarities, there are key differences between the two that set them apart.
Real-time Error Monitoring: Bugsnag focuses primarily on error monitoring and provides real-time alerts when errors occur in your software. It allows you to debug and diagnose issues quickly, pinpointing the root cause of errors and providing detailed information about each occurrence. On the other hand, Datadog offers a broader monitoring solution that includes error monitoring along with infrastructure and application performance monitoring.
Integration Capabilities: Bugsnag offers a wide range of integrations with popular development and collaboration tools, such as Jira, Slack, and GitHub. This allows teams to seamlessly incorporate Bugsnag into their existing workflow and enables efficient collaboration for issue resolution. Datadog also provides integrations with various tools and services, including cloud platforms and databases, making it suitable for monitoring diverse environments.
Customizable Dashboards: Datadog offers highly customizable dashboards that allow you to visualize and analyze your application's performance metrics, infrastructure data, and other monitoring information. This flexibility enables you to create tailored views and reports that suit your specific requirements. Bugsnag, on the other hand, provides pre-built dashboards and reports focused on error monitoring, streamlining the process of identifying and resolving software issues.
Log Management: While both Bugsnag and Datadog offer log management capabilities, they approach it differently. Bugsnag provides log ingestion and indexing, allowing you to search and analyze logs alongside error data. Datadog, on the other hand, offers more advanced log management features, including log collection, aggregation, and monitoring, which can be suitable for organizations with complex logging needs.
AIOps and Machine Learning: Datadog incorporates AIOps (Artificial Intelligence for IT Operations) capabilities into its monitoring platform, utilizing machine learning to automate anomaly detection and provide intelligent insights. This can help identify unusual behavior, predict potential issues, and optimize system performance. Bugsnag does not have built-in machine learning capabilities for monitoring and issue detection.
Pricing Model: Bugsnag offers a straightforward pricing model based on the number of error events per month. The cost scales with the volume of events captured. In contrast, Datadog's pricing is based on the metrics and logs ingested, allowing you to monitor a wide range of data sources but potentially leading to higher costs for large-scale deployments.
In summary, Bugsnag is focused specifically on real-time error monitoring and offers integrations and customizable dashboards tailored for this purpose. Datadog, on the other hand, provides a more comprehensive monitoring solution, including error monitoring along with infrastructure and application performance monitoring, log management, AIOps capabilities, and a flexible pricing model based on the data ingested.
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.
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 Bugsnag
- Lots of 3rd party integrations45
- Really reliable42
- Includes a free plan37
- No usage or rate limits25
- Design23
- Slack integration21
- Responsive support21
- Free tier19
- Unlimited11
- No Rate6
- Email notifications5
- Great customer support3
- React Native3
- Integrates well with Laravel3
- Reliable, great UI and insights, used for all our apps3
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
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Cons of Bugsnag
- Error grouping doesn't always work2
- Bad billing model2
Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
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