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Datadog vs Loggly: What are the differences?
Introduction:
In this article, we will explore the key differences between Datadog and Loggly, two popular log management and monitoring solutions. Both tools offer valuable features and capabilities to help organizations manage and analyze their logs effectively. However, there are some notable differences between them that can influence the decision-making process for selecting the right tool for specific requirements.
Data Collection and Aggregation: Datadog provides a comprehensive platform for collecting and aggregating logs, metrics, and traces, offering a full-stack observability solution. On the other hand, Loggly primarily focuses on log management and analytics, providing centralized log collection and aggregation capabilities.
Search and Query Capabilities: Datadog offers a powerful search and query language, allowing users to explore and analyze logs in a user-friendly manner. It provides advanced filtering and aggregation options, enabling users to extract valuable insights from logs efficiently. Loggly, on the other hand, offers a simple yet effective search functionality, allowing users to search logs using keywords and basic filters.
Real-time Monitoring and Alerting: Datadog excels in real-time monitoring and alerting capabilities, enabling users to set up alerts based on log data, metrics, and custom events. It provides real-time dashboards and visualizations to track log data and metrics effectively. Loggly also offers alerting features, but it may not be as comprehensive as that of Datadog.
Integration Ecosystem: Datadog has a vast integration ecosystem, allowing seamless integration with various tools and services such as AWS, Azure, Kubernetes, and many others. It offers native integrations and APIs to collect logs from different sources effectively. Loggly also provides integration options, but the range of integrations may be somewhat limited compared to Datadog.
Scalability and Performance: Datadog is known for its scalability and performance, handling large volumes of logs and metrics efficiently. It can handle massive workloads and offers a reliable and robust infrastructure to support high-throughput log data processing. Loggly is also scalable but may not have the same level of performance as Datadog for extremely high volumes of log data.
User Interface and User Experience: Datadog provides a user-friendly and intuitive interface, making it easier for users to navigate and explore log data, metrics, and alerts. It offers interactive dashboards and visualizations, providing a holistic view of log data. Loggly also provides a user-friendly interface but may not have the same level of advanced visualizations and interactive features as Datadog.
In summary, Datadog offers a comprehensive platform for collecting, analyzing, and visualizing logs, metrics, and traces, with strong capabilities in real-time monitoring and alerting. Loggly, on the other hand, primarily focuses on log management and analytics, providing centralized log collection and search capabilities. The choice between these two tools depends on specific requirements and preferences in terms of features, integrations, scalability, and user experience.
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 Loggly
- Centralized log management37
- Easy to setup25
- Great filtering21
- Live logging16
- Json log support15
- Log Management10
- Alerting10
- Great Dashboards7
- Love the product7
- Heroku Add-on4
- Easy to setup and use2
- Easy setup2
- No alerts in free plan2
- Great UI2
- Good parsing2
- Powerful2
- Fast search2
- Backup to S32
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Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of Loggly
- Pricey after free plan3
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