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Amazon CloudWatch vs Datadog: What are the differences?
Introduction
Amazon CloudWatch and Datadog are both cloud monitoring services that provide businesses with real-time data and insights into their applications and systems. While they have similar functionalities, there are key differences between the two platforms that can help businesses determine which one is best suited for their needs.
Integration Options: Amazon CloudWatch primarily focuses on monitoring Amazon Web Services (AWS) resources and instances, providing extensive integration options for AWS services such as EC2, RDS, Lambda, and more. On the other hand, Datadog offers wider integration capabilities, supporting not only AWS but also other cloud platforms and various third-party applications.
Visualization and Dashboards: Amazon CloudWatch provides basic data visualization and dashboard capabilities, allowing users to create basic graphs and charts using its native capabilities. In contrast, Datadog offers a more extensive and customizable dashboard interface, enabling users to build powerful visualizations and customize their dashboards with drag-and-drop functionality.
Alerting and Notification: Amazon CloudWatch provides basic alerting and notification capabilities based on threshold values and metric conditions. However, Datadog offers more advanced alerting options, including anomaly detection, forecasting, and machine learning-based alerts. Datadog also provides integrations with various communication channels such as Slack, PagerDuty, and SMS for notifications.
Application Performance Monitoring: While both CloudWatch and Datadog offer application performance monitoring (APM) capabilities, Datadog provides more comprehensive APM features, including detailed traces, distributed tracing, and code-level visibility. These features enable efficient troubleshooting and optimization of application performance.
Pricing Structure: Amazon CloudWatch's pricing is primarily based on the number of metrics, logs, and custom events ingested, as well as the storage and retention of logs. In comparison, Datadog's pricing is more flexible, offering different plans based on the number of monitored hosts or cloud instances, with additional features available at higher tiers.
Community and Support: Both Amazon CloudWatch and Datadog have active user communities and provide comprehensive documentation. However, Datadog has gained popularity among developers and has a larger community, offering a wide range of resources, tutorials, and integrations, which can be beneficial for businesses seeking support and knowledge-sharing opportunities.
In summary, while Amazon CloudWatch is tightly integrated with AWS services and provides basic monitoring features, Datadog offers wider integration options, advanced visualization, extensive alerting capabilities, comprehensive APM functionality, flexible pricing plans, and an active community, making it a suitable choice for enterprises seeking a robust and flexible monitoring and observability platform.
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 Amazon CloudWatch
- Monitor aws resources76
- Zero setup46
- Detailed Monitoring30
- Backed by Amazon23
- Auto Scaling groups19
- SNS and autoscaling integrations11
- Burstable instances metrics (t2 cpu credit balance)5
- HIPAA/PCI/SOC Compliance-friendly3
- Native tool for AWS so understand AWS out of the box1
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
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Cons of Amazon CloudWatch
- Poor Search Capabilities2
Cons of Datadog
- Expensive19
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
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