Google Kubernetes Engine vs HPE Ezmeral Container Platform comparison

"The feature that I like the most is the ease of use as compared to AWS, as its ease of use is very high and I can quickly deploy clusters with a simple template."

"Google Kubernetes Engine is used for orchestrating Docker containers. We have 30 or 40 customers working with this solution now. We'll probably see 10 to 15 percent growth in the number of customers using Google Kubernetes Engine in the future."

"The main advantage of GKE is that it is a managed service. This means that Google is responsible for managing the master node in the Kubernetes cluster system. As a result, we can focus on deploying applications to the slaves, while Google handles any updates and security patches. The fact that GKE is fully integrated into the Google ecosystem, including solutions such as BigQuery and VertexAI. This makes it easier for us to integrate these tools into our process. This integration ultimately speeds up our time to market and reduces the time and effort spent on managing infrastructure. The managed aspect of GKE allows us to simply deploy and utilize it without having to worry about the technicalities of infrastructure management."

"We appreciate that it is quite easy to set up a Kubernetes cluster in Google Cloud, using the managed services within this solution."

"The deployment of the cluster is very easy."

"The logs are important for detecting problems in our clusters."

"This is a really good product for independent applications or microservices."

"The features are typical Kubernetes, but Google One offers a better GUI-based deployment. It's more sophisticated and integrates well with other services, providing a better customer experience."

"If customers focus on primary apps or if they are using them for data science, this is a good solution."

"If customers focus on primary apps or if they are using them for data science, this is a good solution."

"The stability of the HPE Ezmeral Container Platform is good, and I rate it an eight out of ten."

"Log observability could be made easier so someone from high school can use it without having technological expertise."

"There are some security issues, but it might just be because we are not up to speed yet as much as we should be and so we haven't found it in the documentation yet."

"Our critique is that we have to do too much work to get the cluster production-ready."

"Google Kubernetes Engine is less stable in some highly complex deployments with many nodes."

"An area in which Google Kubernetes Engine could improve is configuration."

"There is room for improvement in the cluster updates process. Specifically, when managing both non-production and production clusters, we need a sequential functionality."

"The user interface is a bit confusing sometimes. You need to navigate between the various consoles we have."

"t is not very stable."

"HPE Ezmeral Container Platform is not user-friendly and has many parts that are more difficult than Cloudera. It is also a complex solution and has low features for data management."

"The modernization in Ezmeral could be improved."

"If a customer is focused on process automation or API usage, I would not recommend this solution, but it is good to use for Data Science."

"Currently, it costs around $1000 per month which sorted our deployment. So once we get more clients, having a huge suffix, costs can go up."

"I would rate Kubernetes' pricing four out of five."

"I rate the product's price a six on a scale of one to ten, where one is low price and ten is high price. The product is competitively priced."

"This is an open source solution, so there is no pricing or licensing."

"The price for Google Kubernetes Engine could be lower - I'd rate its pricing at three out of five."

"The solution's price is reasonable."

"It is competitive, and it is not expensive. It is almost competitive with AWS and the rest of the cloud solutions. We are spending around 3K USD per month. There are four projects that are currently running, and each one is incurring a cost of around 3K USD."

"The pricing for GKE is dependent on the type of machine or virtual machine (VM) that is selected for the nodes in the cluster. There is a degree of flexibility in choosing the specifications of the machine, such as the number of CPUs, GPUs, and so on. Google provides a variety of options, allowing the user to create the desired cluster composition. However, the cost can be quite steep when it comes to regional clusters, which are necessary for high availability and failover. This redundancy is crucial for businesses and is required to handle an increase in requests in case of any issues in one region, such as jumping to a different region in case of a failure in the Toronto region. While it may be tempting to choose the cheapest type of machines, this may result in a limited capacity and user numbers, requiring over-provisioning to handle additional requests, such as those for a web application."