Introduction
Who doesn't want to save money? Right?
With the rise of cloud computing solutions, the customer demand for the applications has increased widely, to serve this purpose you need to increase the supply but without proper monitoring of the resources that you are provisioning, you might end up with long bills from your cloud provider.
Let us take an example of a restaurant that started with a single outlet, it was quite popular among the localities serving great quality food. Soon the word started to spread and the restaurant owner thought of expanding the business. Now, you are provisioned to buy the same amount of groceries and other stuff monthly without monitoring or looking at what you already have and which food items have the highest sales or demand.
When you sum up your expenses and savings at the end of the year, you come up with the result that your profit margin is much less than what you expected. This fault in the system is mainly due to a lack of proper monitoring of your raw materials.
If we want to relate this similar scenario concerning the cloud, your underutilized resources might be increasing your cloud cost without you noticing. CastAI comes to the table with the solution to this problem. Before getting into the details here are some key features of CastAI:
Ease of Installation
Efficient Auto-Scaling
Real-Time Cost Monitoring
Cost Optimization
Security Checks
AI and ML support
What exactly is CastAI?
CAST AI, is an AI software company that provides computer vision, NLP, and machine learning solutions for enterprises. Founded in 2011 and is headquartered in Montreal, Canada.
CastAI is a versatile platform designed to streamline the administration of Kubernetes across various cloud providers. Kubernetes can be intricate to manage, especially when dealing with different cloud platforms, but CastAI simplifies the process by combining automation, optimization, security, and cost management into a single solution. It abstracts the technical complexities specific to each provider, making it easier to oversee Kubernetes operations on major cloud services like AWS, Azure, and GCP. With CastAI, you can automate tasks, optimize resource usage, enhance security, and control costs, all while benefiting from a unified and user-friendly interface, ultimately simplifying your Kubernetes management experience.
Features of CastAI
The Cast AI dashboard with a ton of features, some of which are:
Savings Report: The Savings Report of the CastAI dashboard helps in finding new sources of savings for the connected cluster.
Automated cost optimization: Automated cost optimization in CastAI is done using concepts such as:
Autoscaling, which means dynamically adjusting the node count to achieve an optimal state.
Downscaling indicates the removal of unnecessary nodes, that are being provisioned.
Rebalancing, a key capability within CAST AI, enables your cluster to consistently attain its optimal and current state. During this operation, outdated or inefficient nodes are automatically substituted with new ones that not only offer cost-efficiency but also align with the latest Node configuration settings, ensuring your cluster operates at its best performance and cost-effectiveness.
Node configuration is the parameters that are set to provision nodes allowed by the user to be performed by the platform.
Cost monitoring: CAST AI's monitoring feature is tailored for cloud-native teams, offering a centralized platform to oversee all expenses associated with your Kubernetes (K8s) clusters. This feature allows you to dissect these expenses according to K8s concepts such as clusters, workloads, and namespaces, providing several benefits.
Kubernetes security: This feature empowers you to perform a comprehensive scan of your cluster, uncovering potential vulnerabilities. Furthermore, it assesses your cluster against industry best practices, providing actionable recommendations to enhance its security to the highest standards.
Getting started with CastAI on your Cluster
To get started with CastAI cost optimization, you need to connect your cluster to the CastAI dashboard. Click on "Connect Cluster" to do so.
Now, CastAI provides you with a script according to the Kubernetes distribution you have chosen concerning your cloud provider. Before running the script please check by clicking on the link provided in the script to see if it allows the read-only option to CastAI. After doing so, you can run the script on your machine.
It should only take a few seconds to run the script. Click on "I ran the script" after completing the task, and the cluster should be connected.
The page will redirect to the dashboard and you see how much you can save on the running cluster. For example, you can see this cluster can save up to 60%.
It will also suggest what instance could be used to save the cost, here, we can replace 6 m5.large instances with 3 c6a.large to drastically decrease the spending. Moreover, if we use spot instances we save a total of 90.6% of what we are spending now.
Conclusion
CastAI is a robust platform designed to streamline the management of Kubernetes operations across various cloud providers. It not only helps you reduce your cloud but also suggests ways to do so by helping you optimize your workload. In summary, CastAI presents a compelling solution for enhancing Cloud Cost Optimization. Its platform offers a unified approach to simplifying Kubernetes management across various cloud providers, ultimately streamlining the process of optimizing cloud expenditures. With its suite of automation, optimization, and cost management tools, organizations have the potential to achieve more efficient resource allocation, reduce unnecessary spending, and improve their overall cloud cost management strategies. However, the extent of CastAI's effectiveness in cost optimization will depend on factors like specific use cases, an organization's cloud architecture, and the successful configuration and utilization of the platform. Therefore, businesses should carefully assess their requirements and consider whether CastAI aligns with their objectives for optimizing cloud costs.