Integrating GenAI for querying in human language and using chatbots for job triggers, especially in large companies with numerous batch jobs, would be beneficial. On a scale of one to ten, I would rate AWS Batch a seven.
Software Engineering Manager – Digital Production Optimization at Yara International ASA
Real User
Top 5
Mar 10, 2025
For AWS Batch to be effective, I need to understand my computational needs and accordingly analyze and configure it. AWS Batch offers a lot of flexibility, so understanding the required computation types is essential for optimization. I should consider using AWS Spot Instances to reduce costs by more than ninety percent while running batch loads. Overall, I would rate AWS Batch an eight out of ten due to its robust features and flexibility. However, it requires a good understanding of how to configure my load and choose the right underlying services like Fargate, ECS, or EKS.
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.
AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to...
Integrating GenAI for querying in human language and using chatbots for job triggers, especially in large companies with numerous batch jobs, would be beneficial. On a scale of one to ten, I would rate AWS Batch a seven.
For AWS Batch to be effective, I need to understand my computational needs and accordingly analyze and configure it. AWS Batch offers a lot of flexibility, so understanding the required computation types is essential for optimization. I should consider using AWS Spot Instances to reduce costs by more than ninety percent while running batch loads. Overall, I would rate AWS Batch an eight out of ten due to its robust features and flexibility. However, it requires a good understanding of how to configure my load and choose the right underlying services like Fargate, ECS, or EKS.
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.
I rate the product an eight out of ten.