We use AWS Batch to manage containerized workloads and dynamic scaling. The solution automatically scales computer resources based on the number of jobs and resource requirements. The solution also integrates with other AWS services.
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We use AWS Batch to manage containerized workloads and dynamic scaling. The solution automatically scales computer resources based on the number of jobs and resource requirements. The solution also integrates with other AWS services.
AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling. AWS Batch is specified as a docker container that allows us to run our applications with all their dependencies.
The solution could improve its pricing-based resources like job execution. The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources. The solution should be updated to improve overall performance, including scalability and reliability.
AWS Batch should regularly update its service and provide the most accurate and up-to-date information.
I have been using AWS Batch for one year.
Around three to four people are using the solution in our organization.
The solution’s initial setup is easy.
AWS Batch is a cheap solution.
The solution's monitoring includes checking the performance, health, and resource utilization of our AWS Batch computing jobs. AWS Batch is integrated with CloudWatch, which provides detailed metrics about our AWS Batch jobs and compute environment. I would recommend AWS Batch to other users. It is easy to learn to use the solution.
It is easy to integrate AWS Batch with AWS CloudWatch, which provides metrics and logs to monitor and troubleshoot AWS Batch jobs. AWS web supports the execution of AWS Batch jobs and docker containers. The solution allows the use of containerization applications and uses CLI.
Overall, I rate the solution ten out of ten.
We use the solution to run scripts for more than 15 minutes. We do not get builds for running the scripts. We can deploy it using containers.
Parallelism and scalability are the best features of the solution. The solution is very easy to configure and run by ourselves. We can use the security features to allow only specific service principles like the IAM role. We can easily integrate AWS container images into the product.
When we run a lot of batch jobs, the UI must show the history. The last time we used it, we faced some glitches. Explaining the tool to junior developers was difficult. They were not able to easily grasp how to link the components.
I have been using the solution for two to three years.
I rate the tool’s stability a ten out of ten.
The tool’s scalability is very good. I rate the scalability a ten out of ten. We have less than 20 users.
The support team is good. The support engineers are knowledgeable and provide solutions to our queries. The response time is quicker than Azure’s support team.
Positive
AWS Batch is easier to use than Azure Batch. I prefer AWS over Azure because AWS has good documentation and community support.
The initial setup of AWS Batch is easier than Azure Batch. I rate the ease of setup a ten out of ten. The end-to-end deployment took 25 to 30 minutes.
The pricing is very fair. The price was negligible compared to the number of tasks we run.
It is a very good solution to use. AWS Batch is a suitable solution if users want to run multiple containers of the same image with multiple variables. It is efficient and scalable. The issues are solved much more quickly. Overall, I rate the tool a nine out of ten.