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Datadog vs StatsD: What are the differences?
- Metrics Collection and Monitoring: Datadog is a comprehensive monitoring and analytics platform that allows users to collect, visualize, and alert on metrics from various sources. It provides a wide range of integrations and can collect metrics from infrastructure, applications, and logs. In contrast, StatsD is a lightweight daemon for collecting and aggregating application metrics, mainly focused on counting and timing events. It is designed to be simple and easy to deploy and can be used as a complement to Datadog for specific use cases.
- Flexibility and Customization: Datadog offers a high level of flexibility and customization options for metric collection and monitoring. Users can create custom metrics, define complex queries and alert conditions, and visualize data using various chart types and dashboards. On the other hand, while StatsD allows for some configuration options, it has limited customization capabilities compared to Datadog. It mainly focuses on collecting and aggregating basic metrics like counts and timings.
- Scalability and Performance: Datadog is designed to handle large-scale metric collection and monitoring for enterprise-grade environments. It scales with the growing needs of the infrastructure and can handle millions of metrics per second. It offers advanced functionalities like high-resolution metrics and distributed tracing. In contrast, StatsD is a lightweight solution and may not be suitable for high-scale deployments. Its performance may start to degrade when handling a high volume of metric data.
- Data Visualization and Analysis: Datadog provides powerful data visualization and analysis capabilities. It allows users to create custom dashboards with various widgets and chart types to visualize metrics in real-time. It offers built-in anomaly detection algorithms and anomaly graphs to identify and investigate unusual patterns in data. On the other hand, StatsD does not offer built-in data visualization and analysis tools. Users need to integrate it with other tools or platforms to visualize and analyze the collected metric data.
- Monitoring Infrastructure and Applications: Datadog monitors not only the infrastructure but also the applications running on it. It provides out-of-the-box integrations with popular software and services, enabling users to monitor application performance metrics, logs, and traces seamlessly. On the contrary, StatsD is primarily focused on collecting application metrics and does not have built-in capabilities to monitor infrastructure or integrate with other monitoring tools.
- Alerting and Notification: Datadog offers flexible alerting and notification capabilities to keep users informed about any anomalies or issues with the monitored metrics. Users can set up alert conditions based on various criteria and configure notifications via emails, Slack, PagerDuty, and other channels. In contrast, StatsD does not provide native alerting and notification features. Users need to rely on other tools or write custom scripts to implement alerts based on the collected metrics.
In Summary, Datadog is a comprehensive monitoring platform offering flexible customization, scalability, visualization, and alerting capabilities, whereas StatsD is a lightweight metric collection daemon with limited customization and visualization functionalities, mainly focused on application metric collection.
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 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 StatsD
- Open source9
- Single responsibility7
- Efficient wire format5
- Handles aggregation3
- Loads of integrations3
- Many implementations1
- Scales well1
- Simple to use1
- NodeJS1
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Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of StatsD
- No authentication; cannot be used over Internet1