Amazon CloudWatch vs. IBM Turbonomic

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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Measure the usage of resources across different platforms, from Kubernetes to VMs on-premises or in the cloud, to obtain current capacity metrics and plan for future scenarios. Reduce IT infrastructure costs by taking automated actions based on resource underutilization, such as shutting down VMs or changing resource definitions.

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• 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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• Presentation is nice. Its easy to understand what your looking at and the data that is being presented to you.
• Properly identify resource utilization and recommendations for action on how VMs can be improved and resources can be better utilized.
• It was also able to tell us the same information and analysis for cloud resources. I was not expecting that.

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• 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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• It would be nice if the UI included a break-down of features that are both licensed as well as un-licensed. That way, you could not only see what you have, but what you don't.
• The right-sizing recommendations are great, but very little info is given about why the recommendation is being made. More info would not only increase understanding, but would also help drive decision-making.

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We are certainly happy with Turbonomic as a whole and have invested quite a bit of time and effort into learning the ins and outs of the product. We have our reporting setup the way we want it and have gained definite value from these features. I will say though that many products nowadays are offering more native monitoring, reporting, and alerting features which may eventually steer us away from this product

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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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Excellent approach to larger VM organizational management. They have an very clean integrated dashboard that allows us to see everything in our environment and what that is doing in real-time. It works on multiple hyper-visors really well and integrates capacity planning on my local site as well as my cloud locations.

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VMTurbo has not caused any outages by not doing what we expect it to do.

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It allocates resources among applications by showing more on the cost breakdown by cloud service, with metrics on cloud provider information like Azure Management, Identity, Networking, Storage with costs per day, and total services costs. This then could facilitate and show the corresponding actions thereafter upon scaling.

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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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When I contact support I get a quick response and they are able to solve my problem quickly. I also get a sense that they want to make sure that we are getting value from the product and walk me through whatever steps are needed to accomplish my goals.

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Alex (from VMTurbo) has worked with the product for years and helped develop the product. He was very knowledgeable and was able to provide our support team with details knowledge on how to get our deployment configured correctly as well as help with another VMTurbo POC within another customers environment.

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After buying VMTurbo Operations Manager, I was invited to an online user training event. I felt this training was effective and dug just deep enough to be informative yet still keep my attention. Additionally, the webinar was free.

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The implementation was very simple. Just upload an OVA file and power on the VM. Once it comes up enter some networking information and you can then access the web interface. From there, just begin configuring the system for your environment by adding you license and the various virtual environments and storage through the inventory tab

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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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As the organization had experience of years in using IBM products, we had the confidence that they will provide us with great support. And we needed a reliable solution as a financial institute to ensure continuous operations. Even though the price was very high, we made the correct decision to go ahead with IBM Turbonomic as the feedback from existing users in the region was very positive. We needed a solution which was capable of handling our automation requirements. All these were green in IBM Turbonomic.

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It’s very scalable.

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Professional services were always there to guide us in our transformation to the cloud. They understood our business model and then were able to provide guidance on what we needed from the tool.

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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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• Application performance has been a big one. With Turbonomic keeping everything running at top performance, it can make changes when extra resources are need, quicker than somebody being notified and then making the necessary changes.
• Turbonomic has been a great cost savings for us on multiple occasions. We use it every time we are improving servers.
• With the planning feature we get the best performance form new hardware purchases

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