Amazon CloudWatch vs. SUSE Observability (StackState)

For out business we find that AWS Cloudwatch is good at providing real-time metrics for monitoring and analysing the performance and usage of our platform by customers. It is possible to create custom metrics from log events, such people adding items to a basket, checking out or abandoning their orders.

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StackState is suitable for 1000+ hosts. Sometimes specific applications can take higher development time. Well suited for hybrid platforms to build end to end service alarms and service views. Advanced UI navigation might require some training. It is not a simple download and deploy software. It will require development in an agile model. Where newer versions are deployed to suit exact client requirements. Support contract with the StackState Engineer for development of use-cases is required and very useful.

• It provides lot many out of the box dashboard to observe the health and usage of your cloud deployments. Few examples are CPU usage, Disk read/write, Network in/out etc.
• It is possible to stream CloudWatch log data to Amazon Elasticsearch to process them almost real time.
• If you have setup your code pipeline and wants to see the status, CloudWatch really helps. It can trigger lambda function when certain cloudWatch event happens and lambda can store the data to S3 or Athena which Quicksight can represent.

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• Giving observability of the entire IT stack
• Custom alerting options.
• Ingesting many different types of data.
• Requesting new features is encouraged and they often add them quite quickly.

• Memory metrics on EC2 are not available on CloudWatch. Depending on workloads if we need visibility on memory metrics we use Solarwinds Orion with the agent installed. For scalable workloads, this involves customization of images being used.
• Visualization out of the box. But this can easily be addressed with other solutions such as Grafana.
• By design, this is only used for AWS workloads so depending on your environment cannot be used as an all in one solution for your monitoring.

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• Can't think of anything yet.

It's excellent at collecting logs. It's easy to set up. The viewing & querying part could be much better, though. The query syntax takes some time to get used to, & the examples are not helpful. Also, while being great, Log Insights requires manual picking of log streams to query across every time.

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Some elements of the product haven't had the usability upgrade yet and can be a bit technical. This is to be expected as they are trying to solve complex problems. I am sure that in the future, steps will be made to simplify this as well for the users / administrators / developers of the platform.

Support is effective, and we were able to get any problems that we couldn't get solved through community discussion forums solved for us by the AWS support team. For example, we were assisted in one instance where we were not sure about the best metrics to use in order to optimize an auto-scaling group on EC2. The support team was able to look at our metrics and give a useful recommendation on which metrics to use.

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It's swift, they're thinking along with us. It's a "collaboration approach" rather than a (traditional) customer-supplier relation. Out new ideas are taken in concern and often ends up in enhancements of StackState

Grafana is definitely a lot better and flexible in comparison with Amazon CloudWatch for visualisation, as it offers much more options and is versatile. VictoriaMetrics and Prometheus are time-series databases which can do almost everything cloudwatch can do in a better and cheaper way. Integrating Grafana with them will make it more capable Elasticsearch for log retention and querying will surpass cloudwatch log monitoring in both performance and speed

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• Positive for alarms and alert notifications once configured/customized.
• Has upfront learning curve, and cost can increase as does the alarm activity and monitoring details you may require.
• Cost-effective for any size organization keeping with AWS and utilizing its native tools is a savings in long-term ROI.

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• Increased visibility of all resources in cloud to 100%.
• Deviated stages are able to detect problems before their occurrence. However, [it] needs tunings for false alarms.