Build Your Own Cloud Management Platform

The multi-cloud management has carved out a niche for itself in the ever growing $200 billion worth cloud computing market. It is expected to grow up to USD 4,492.7 Million by 2022. The numbers speak for themselves and shows how dependent we are on cloud management tools today.

The Cloud ROI Puzzle:

We use the data from these tools as building blocks on top of the cloud provider’s primary interface data to get the context of things and tackle the real-world cloud problems.

So, we end up paying for several SaaS tools and analyzing data from multiple tools, in silos, bringing down the cloud ROI.

After talking to hundreds of cloud customers about their pain points, we realized that there were other factors too that brought down the ROI.

These cloud management tools offer a templatized set of solutions to cloud management problems. This gives businesses less leg room to maneuver and integrate their business needs.

Result: You end up spending precious man hours in performing the same tasks, writing script after script, and performing repetitive organizational tasks.

Take for example: periodically looking for publicly accessible S3 buckets among hundreds and thousands of them across an enterprise. This is not an easy task. With so many publicly accessible S3 buckets found everyday, like in the recent case where millions of Facebook records were found on a public bucket, enterprises invest enormous amount of time and effort in keeping a check on each bucket’s policy while adhering to organizations business processes. This obviously contributes to bringing down the cloud ROI.

As ardent cloud practitioners, we had to step up and join the missing pieces of this Cloud ROI puzzle. Here’s how we did it…

Unleashing the Power Within

One of the primary reasons why we could show the entire architecture with so many layers of contextual data, such as resource correlations and perspectives, on a single pane was because of our indigenous Cloud Graph engine. The engine has been running in the back end of our platform tightly integrated with a 3D visualization engine in the front end.

This Cloud Graph engine has been an integral part of our framework since inception of TotalCloud. It has been one of the driving forces of our mission to create an ‘Autonomous Cloud Engineer’ tool that can monitor and manage any type of cloud services and any number of cloud services on its own.

Today, we have opened up our powerful Cloud Graph engine to AWS cloud users in the form of a web console, where you can create smart workflows leveraging its power. You now have the flexibility to perform automation on any AWS resource aligning to your business needs, without depending on third-party solutions or AWS management console/CLI.

With this, you can AUTOMATICALLY:

  • Define ‘ANY’ AWS cloud management requirement, be it for optimization, security, governance, reliability, etc. in a workflow
  • Fetch layers of contextual data from TotalCloud Cloud Graph engine specific to this requirement
  • Perform quick analysis with isometric visualizations topped with various perspectives
  • Take remediation action with right user approvals without using the AWS console or CLI
  • Communicate the end results to the stakeholder(s) via email or slack channel

Result: Access to a powerful and versatile tool that helps to execute cloud management tasks at an accelerated pace.

Simply put: without writing a single line of code, you can define and automate several cloud management tasks in line with your organizational needs. With this, you can save several man hours and hundreds of dollars spent on multiple cloud management tools, and eventually build your own cloud management platform.

Cloud Recipes: Building Your Own Cloud Management Platform One Workflow at a Time

Take for instance, this machine learning architecture below:


To manage this architecture successfully for cost, security, and governance, you can create your own cloud recipes with the help of workflows, such as:

  • Automatically perform actions to SageMaker notebook instances that are down, and inform your team without manually typing an email/slack message. For this use case, you can easily create a workflow to monitor SageMaker notebook instances when CloudWatch Alarm fires up, restart these affected notebook instances, and automatically push email notification to SysOps team to inform them.
  • Automatically ask for your manager’s approval and modify notebook instances to a larger profile as demand scales. For this use case, you can easily create a workflow to trigger an SNS with CloudWatch when systems are running on full throttle, and further take user approval via email and upon approval scale up the system to a larger profile and get notified of the modifications.

As you create each cloud recipe, you will become less dependent on other cloud management tools. Ultimately, you will have your own cloud management platform. As you set up workflows only once and let the machines do the tasks, the amount of time saved is innumerable.

To ensure safe access to all cloud resources and apt governance, each workflow gets its own customized policy via our Policy Management tool.

Further, you also get our inbuilt isometric visualizations and layers of perspective data like before in the web console itself for deep contextual analysis. This makes up for debugging and mapping tools you have been using!

Conclusion: Joining the Missing Pieces of Cloud ROI Puzzle with a Higher Order Language

Do give it a try, and sign-up to get access to our beta.

To reduce the learning curve, we are offering workflow templates of common use cases to get you started quickly on TotalCloud. Do check them out.

Originally published at on April 12, 2019.

TotalCloud helps cloud engineers indulge in no-code AWS automation. We enable engineers to go script-less, saving more than 95% of engineering time.