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Datadog vs Jaeger: What are the differences?
Introduction
Datadog and Jaeger are both popular observability tools used in the field of software development. While they serve a similar purpose of providing insights into the performance and behavior of complex systems, there are some key differences between the two.
Data Collection and Storage: One of the key differences between Datadog and Jaeger is how they handle data collection and storage. Datadog provides a unified platform for collecting data from various sources such as application logs, infrastructure metrics, and APM data. It stores this data in its own proprietary format, which allows for easy correlation and analysis. On the other hand, Jaeger is specifically designed for distributed tracing and focuses primarily on collecting and storing trace data. It uses the OpenTracing API and stores the trace information in formats like JSON, Elasticsearch, or Kafka.
Tracing Granularity: Another important difference between Datadog and Jaeger lies in their tracing granularity. Datadog provides end-to-end distributed tracing, allowing developers to trace requests across different services and identify performance bottlenecks. It provides detailed insights into individual requests and captures metrics at a fine-grained level. Jaeger, on the other hand, specializes in microservices tracing and excels in capturing detailed traces within a single service. It offers high-resolution timing information within a service or application, making it a more suitable choice for fine-grained monitoring within a microservices architecture.
User Interface and Visualization: The user interface and visualization capabilities differ between Datadog and Jaeger. Datadog provides a comprehensive dashboard that allows users to visualize various monitoring data, including metrics, logs, and traces. It offers pre-built charts, graphs, and visualization widgets to analyze and correlate data effectively. Jaeger, on the other hand, is more focused on distributed tracing and offers a specialized interface for visualizing and analyzing trace data. It provides detailed trace visualizations, including timeline views, service dependency graphs, and flame graphs, to help identify performance issues within a distributed system.
Integration Ecosystem: Datadog has a wide integration ecosystem and supports a variety of technologies and platforms, including cloud providers, container orchestration tools, messaging systems, and databases. It allows users to seamlessly collect and analyze data from these different sources. Jaeger, although not as extensive as Datadog, offers integrations with popular frameworks and libraries used in microservices architectures, such as Spring Boot, Django, and gRPC. It also supports standard protocols like Zipkin, making it compatible with existing tracing instrumentation.
Scalability and Performance: Scalability and performance vary between Datadog and Jaeger. Datadog is designed for high scalability, with the ability to handle a large volume of data and provide real-time insights at scale. It leverages a distributed architecture and offers features like auto-scaling, data sharding, and indexing optimizations. Jaeger, being more focused on tracing, is optimized for capturing and storing detailed trace data efficiently. It may have limitations in terms of the sheer volume of data it can handle and the level of real-time analysis it can provide in highly demanding scenarios.
Pricing Model: Datadog and Jaeger also differ in their pricing models. Datadog follows a subscription-based pricing model, where users pay a monthly or annual fee based on the number of hosts or metrics they need to monitor. It offers different tiers of pricing with varying levels of features and support. Jaeger, on the other hand, is an open-source project and is available for free. However, it may require additional infrastructure resources to set up and maintain the storage and analysis components required for working with trace data.
**In Summary, Datadog provides a unified platform for collecting and analyzing various types of monitoring data, with a focus on end-to-end distributed tracing and a comprehensive integration ecosystem. Jaeger, on the other hand, specializes in detailed microservices tracing within a single service, offering a specialized visualization interface for analyzing trace data. The choice between Datadog and Jaeger depends on the specific monitoring needs and architectural requirements of the system being monitored.
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)137
- Easy setup107
- Powerful ui87
- Powerful integrations83
- Great value70
- Great visualization54
- Events + metrics = clarity46
- Custom metrics41
- Notifications41
- 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
- Source control and bug tracking4
- Automation tools4
- Cute logo4
- Monitor almost everything4
- Full visibility of applications4
- Simple, powerful, great for infra4
- Easy to Analyze4
- Best than others4
- Expensive3
- Best in the field3
- Free setup3
- Good for Startups3
- APM2
Pros of Jaeger
- Easy to install6
- Open Source6
- Feature Rich UI5
- CNCF Project4
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Cons of Datadog
- Expensive19
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1