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Cacti vs Datadog: What are the differences?
Introduction
Cacti and Datadog are both popular monitoring tools used to gather and analyze data for various systems and applications. While they serve the same purpose of monitoring, there are key differences between them that set them apart.
Data Collection: Cacti primarily uses SNMP (Simple Network Management Protocol) for data collection. It gathers information from network devices and servers using SNMP queries. On the other hand, Datadog supports multiple methods of data collection, such as system-level statistics, application-level metrics, logs, and traces. It provides a more comprehensive approach to data collection.
Scalability: Cacti is best suited for small to medium-sized environments as it can start to encounter performance limitations when dealing with a large number of devices or data points. Datadog, on the other hand, is designed to scale horizontally and can handle a large volume of data. It can accommodate the monitoring needs of both small and large organizations.
Multi-platform Support: Cacti is primarily designed for monitoring network devices and servers. While it can be extended to monitor other devices or applications, it requires additional configuration and plugins. Datadog, on the other hand, offers out-of-the-box support for a wide range of platforms and services, including cloud platforms, containers, databases, and more. It provides more flexibility in monitoring various aspects of a system or application.
Alerting and Notification: Cacti has limited built-in alerting capabilities. It relies on plugins to provide advanced alerting functionality. Datadog, on the other hand, offers advanced alerting and notification features out-of-the-box. It allows users to set up alerts based on custom metrics, threshold violations, anomalies, and more. It also provides integrations with various notification channels like email, SMS, and Slack.
Data Visualization: Cacti provides basic graphing and visualization features, allowing users to create graphs and charts based on the collected data. While it serves the purpose of basic visualization, it lacks advanced visualization capabilities. Datadog, on the other hand, offers a robust and intuitive data visualization platform. It provides extensive options for customizing graphs, dashboards, and generating visual representations of data.
Analytics and APM: Cacti does not offer advanced analytics or application performance monitoring (APM) capabilities. It focuses more on providing monitoring and trending features. Datadog, on the other hand, offers powerful analytics and APM capabilities. It allows users to gain insights from the collected data, perform advanced analytics, and troubleshoot performance issues in their applications.
In summary, while both Cacti and Datadog are monitoring tools, Datadog offers a more comprehensive and scalable solution with support for various data collection methods, advanced alerting, extensive platform support, robust data visualization, and advanced analytics capabilities.
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 Cacti
- Free3
- Rrdtool based3
- Fast poller2
- Graphs from snmp1
- Graphs from language independent scripts1
Pros of Datadog
- Monitoring for many apps (databases, web servers, etc)140
- 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
- Simple, powerful, great for infra4
- Monitor almost everything4
- Full visibility of applications4
- Easy to Analyze4
- Cost4
- Source control and bug tracking4
- Best than others4
- Automation tools4
- Best in the field3
- Expensive3
- Good for Startups3
- Free setup3
- APM2
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Cons of Cacti
Cons of Datadog
- Expensive20
- No errors exception tracking4
- External Network Goes Down You Wont Be Logging2
- Complicated1
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