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Datadog vs Sentry: What are the differences?
Datadog and Sentry are both monitoring tools used by developers and operations teams to track and analyze application performance. Here are the key differences between the two:
Data collection and monitoring: Datadog focuses on collecting and analyzing metrics and logs from various sources, providing real-time monitoring and alerts. It offers comprehensive visibility into the infrastructure, application performance, and user experience. In contrast, Sentry is primarily an error and exception tracking tool. It focuses on capturing and reporting software errors and exceptions, helping developers identify and fix issues quickly.
Alerting and troubleshooting: Datadog's alerting capabilities are highly customizable, allowing users to set up thresholds and conditions for generating alerts based on various metrics and logs. It also provides correlated data views to aid troubleshooting. Sentry, on the other hand, offers rich context for error reports, including stack traces, user data, and tags. This makes it easier for developers to identify and reproduce errors, leading to faster resolution.
Deployment and integration: Datadog supports a wide range of integrations, making it compatible with different programming languages, frameworks, and devops tools. It provides libraries and agents for easy deployment across various environments. Sentry also offers integrations with popular development tools and frameworks, but its focus is primarily on capturing errors and exceptions in code. It provides SDKs and plugins for easy integration into different programming languages.
User interface and visualization: Datadog offers a comprehensive and customizable user interface with interactive dashboards and visualizations. It allows users to create custom metrics, alerts, and monitors, and provides drag-and-drop functionality for easy visualization. Sentry, on the other hand, has a simpler user interface focused on error tracking and exception handling. Its interface provides detailed error reports with stack traces and other relevant information, aiding in debugging and fixing issues.
Scalability and pricing: Datadog is designed for large-scale deployments and can handle monitoring needs for complex infrastructures. It offers various pricing plans with different feature sets based on the scale and requirements of the organization. Sentry, on the other hand, is more focused on capturing software errors and exceptions and may not scale to the same extent as Datadog. Its pricing plans are primarily based on the number of events or users per month.
Use cases and target audience: Datadog caters to operations teams, providing comprehensive monitoring and observability solutions for infrastructure and applications. It is suitable for organizations with complex architectures and large-scale deployments. Sentry, on the other hand, is primarily targeted towards developers and engineering teams who want to capture and track errors and exceptions in code. It is particularly useful during development and testing phases to identify and fix issues early on.
In Summary, Datadog is a comprehensive monitoring tool focusing on metrics, logs, and alerts, while Sentry is primarily an error and exception tracking tool for developers. Datadog provides extensive monitoring capabilities and scalability, whereas Sentry offers rich error-context and easy integration into codebases for quicker debugging and issue resolution.
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 essentially inherited a Shopify theme that was originally created by an agency. After discovering a number of errors being thrown in the Dev Console just by scrolling through the website, I needed more visibility over any errors happening in the field. Having used both Sentry and TrackJS, I always got lost in the TrackJS interface, so I felt more comfortable introducing Sentry. The Sentry free tier is also very generous, although it turns out the theme threw over 15k errors in less than a week.
I highly recommend setting up error tracking from day one. Theoretically, you should never need to upgrade from the free tier if you're keeping on top of the errors...
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)138
- 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
- In-context collaboration7
- AWS support7
- Rich in features6
- Docker support5
- Cute logo4
- Source control and bug tracking4
- Monitor almost everything4
- Cost4
- Full visibility of applications4
- Simple, powerful, great for infra4
- Easy to Analyze4
- Best than others4
- Automation tools4
- Best in the field3
- Free setup3
- Good for Startups3
- Expensive3
- APM2
Pros of Sentry
- Consolidates similar errors and makes resolution easy237
- Email Notifications121
- Open source108
- Slack integration84
- Github integration71
- Easy49
- User-friendly interface44
- The most important tool we use in production28
- Hipchat integration18
- Heroku Integration17
- Good documentation15
- Free tier14
- Self-hosted11
- Easy setup9
- Realiable7
- Provides context, and great stack trace6
- Feedback form on error pages4
- Love it baby4
- Gitlab integration3
- Filter by custom tags3
- Super user friendly3
- Captures local variables at each frame in backtraces3
- Easy Integration3
- Performance measurements1
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Cons of Datadog
- Expensive19
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of Sentry
- Confusing UI12
- Bundle size4
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