Hi,

I'm on the Hobby plan running a Python image-processing service, and I'm seeing large performance variance between deployments that I'd like to understand before deciding whether to upgrade.

The pattern: a given container is either fast or slow for its entire lifetime, and it flips at the next redeploy. Same image digest, same code, no config changes. Across roughly 25 containers over five days, about four were consistently ~3x slower than the rest on identical workloads. A slow container stays slow until it's replaced, and the replacement is usually fast again with no intervention.

The part that makes me think it's host-level rather than resource limits: during slow periods the service uses fewer total CPU-seconds than during fast ones, while wall-clock time triples. The work appears to lose parallelism — operations that normally spread across several cores seem to run on about one. The container never approaches its own CPU or memory limits in either case (peaks around 1.3 vCPU against 8 available, and about 500MB of RAM).

My questions:

1. Does host placement or CPU allocation differ between Hobby and Pro? I'm trying to work out whether Pro would reduce this variance, or whether it only raises limits I'm not currently hitting.

2. Is there anything about how CPU is allocated on shared infrastructure that would explain a workload losing effective parallelism on some hosts but not others?

3. Is dedicated compute available below the Enterprise tier, or is that the only option for consistent performance?

Thanks,

John