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Datadog vs Scalyr: What are the differences?
## Introduction
When comparing monitoring tools such as Datadog and Scalyr, it is important to understand the key differences to make an informed decision about which platform suits your needs best.
## 1. Scalability:
Datadog offers scalable solutions that allow users to grow their monitoring capabilities as their infrastructure evolves, making it suitable for businesses of all sizes. On the other hand, Scalyr excels in handling large-scale data ingestion and analysis, making it a preferred choice for organizations with massive volumes of logs and metrics.
## 2. Pricing Model:
Datadog follows a pricing model based on host count and features, which can lead to unpredictable costs for users. In contrast, Scalyr offers a simple pricing structure based on data volume, providing more cost predictability for users with fluctuating demands.
## 3. Search Functionality:
Datadog provides powerful search capabilities using its query language, making it easier for users to extract specific insights from their data. Scalyr, on the other hand, offers a unique search functionality that allows users to perform real-time searches across all their data sources without any indexing delays.
## 4. Customization:
Datadog provides a wide range of integrations and dashboards to customize monitoring experiences based on user preferences. In comparison, Scalyr offers advanced customization options through query language and alerts, allowing users to tailor their monitoring solution to unique requirements.
## 5. Deployment Options:
Datadog supports various deployment options, including cloud, on-premises, and hybrid setups, giving users flexibility in choosing the most suitable environment for their monitoring needs. Conversely, Scalyr primarily focuses on cloud-based deployments, simplifying setup processes for users who prefer cloud-native solutions.
## 6. Log Management Capabilities:
While both Datadog and Scalyr provide log management features, Datadog offers more advanced log monitoring capabilities, including log analytics, correlation, and visualization tools. On the other hand, Scalyr specializes in log aggregation, search, and analysis, providing a streamlined approach to log management for users looking to prioritize log data insights.
In Summary, understanding the key differences between Datadog and Scalyr, such as scalability, pricing model, search functionality, customization, deployment options, and log management capabilities, can help users choose the right monitoring tool for their specific needs.
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 Scalyr
- Speed of queries7
- Blazing fast logs search4
- Simple usage1
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Cons of Datadog
- Expensive19
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
Cons of Scalyr
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