IBM Bare Metal Servers work, but they make you work too... but that is just the way it is.https://www.trustradius.com/infrastructure-as-a-service-iaasIBM Cloud Bare Metal ServersUnspecified8.1471012019-07-02T16:33:22.254Z
July 02, 2019
IBM Bare Metal Servers work, but they make you work too... but that is just the way it is.
Score 7 out of 101
Overall Satisfaction with IBM Cloud Bare Metal Servers
We use this as a compute server and a build server for deployments and dev work. It is being used by several different groups at a single client for a lot of backend management and monthly/quarterly intensive compute jobs. It addresses the business issue that Cloud Foundry or virtual boxes do not have enough CPU power as well as a dedicated place for specific tasks.
- Good ROI for compute intensive Batch jobs
- Reliable for intensive build jobs
- Slow to provision. If you need it you have to wait.
We primarily use these for high intensity compute jobs. We have optimized our workloads to run in parallel on bare metal servers in IBM Cloud. We got roughly a 80% speed increase when using bare metal in comparison to using shared services. Our jobs use all cores at roughly 100% for 8-20 hours at a time. We saw and had to deal with a lot of failures when dealing with shared resources.
It is relatively slow, but it works. We have seen faster start times in other cloud providers. Admittedly, we have seen longer start-up times than 4 hours when using IBM cloud bare metal servers. other services like AWS or GCP have more reliable startup times that what we have experienced with IBM
- Amazon Elastic Compute Cloud (EC2)
In general we have found that the provisioning and permissions and switching between instances of bare metal and virtual machines were easier in AWS than in IBM. Also the billing in AWS has much greater transparency. The UI interface is also easier to navigate and understand. we also found that our users liked them better.
- Very good for batch processing.
- Very good for CPU intensive jobs.
- Good for reliable always up and not having to share resources.
- On the down side, it is less appropriate for scenarios where you need on demand compute.
- If budget is an issue, it is quite expensive.
- Not great if you need resources quickly.