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Datadog vs ELK: What are the differences?
Introduction:
In this article, we will discuss the key differences between two popular monitoring and log management tools, Datadog and ELK (Elasticsearch, Logstash, and Kibana).
Architecture and Components: Datadog is a cloud-based monitoring and analytics platform that provides a unified view of various metrics and log data. It offers an all-in-one solution with integrated infrastructure monitoring, APM (Application Performance Monitoring), and log management. On the other hand, ELK is an open-source stack that consists of Elasticsearch, Logstash, and Kibana. Elasticsearch handles data storage and retrieval, Logstash helps in data ingestion and transformation, and Kibana is used for data visualization.
Ease of Use and Scalability: Datadog provides a user-friendly and intuitive interface, making it easier for users to navigate and configure. It offers a quick and effortless setup process, and its cloud-based nature eliminates the need to manage infrastructure. ELK, being open-source, requires more expertise and effort for setup and maintenance. It also requires users to manage their own infrastructure and scale as per their requirements.
Monitoring Capabilities: Datadog offers comprehensive monitoring capabilities for infrastructure, applications, and logs. It provides pre-built integrations for various technologies and services, allowing users to easily collect and analyze relevant metrics. It also has advanced alerting and anomaly detection features. ELK, on the other hand, provides powerful log management capabilities but may require additional plugins or configurations for specific monitoring needs. It can handle large volumes of log data but may require more customization and fine-tuning.
Pricing and Cost: Datadog follows a subscription-based pricing model, with different tiers based on the number of hosts and services monitored. The pricing is transparent and predictable, allowing users to easily estimate their costs. ELK, being open-source, is free to use, but users need to consider the costs associated with infrastructure, storage, and maintenance. ELK may require more resources and expertise to manage, increasing the overall cost of ownership.
Community and Support: Datadog has a strong and active community, with extensive documentation and resources available. It has a dedicated support team to assist users and address their queries. ELK also has a vibrant community, with various forums and resources for assistance. However, the level of support may vary as it depends on community contributions and self-help resources.
Customization and Flexibility: Datadog provides a wide range of pre-built integrations and integrations with popular technologies. It also allows users to create custom metrics and dashboards. ELK, being open-source, offers high customization and flexibility. Users can extend and modify its components based on their specific needs. It provides a powerful query language to search and analyze log data, giving users more control over their data analysis.
In summary, Datadog is a cloud-based platform that offers an all-in-one integrated solution for monitoring and log management, with a user-friendly interface and comprehensive monitoring capabilities. ELK, being open-source, provides more customization and flexibility but requires additional expertise and effort for setup and maintenance. Users should consider factors such as ease of use, scalability, monitoring needs, pricing, community support, and customization requirements while choosing between Datadog and ELK.
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 ELK
- Open source14
- Can run locally4
- Good for startups with monetary limitations3
- External Network Goes Down You Aren't Without Logging1
- Easy to setup1
- Json log supprt0
- Live logging0
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Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of ELK
- Elastic Search is a resource hog5
- Logstash configuration is a pain3
- Bad for startups with personal limitations1
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