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Carolina Cloud Docs

Introduction to Carolina Cloud

Carolina Cloud is a high-performance compute platform built on a simple premise: dedicated hardware shouldn’t cost a fortune. Every vCPU, every gigabyte of RAM, every GPU you provision is exclusively yours — no burstable credits, no shared tenancy, no noisy neighbors.

We started Carolina Cloud because we were tired of paying inflated cloud bills for straightforward compute. We believed we could deliver the same raw performance at a fraction of the cost, with none of the complexity. We have.

What we offer

Section titled “What we offer”
  • Dedicated resources only. When you provision 16 vCPUs and 64 GiB of RAM, that hardware is reserved for you and no one else. Performance is consistent and predictable.
  • VMs, containers, and notebooks. Launch a bare Ubuntu VM, a pre-configured data science container, a Marimo notebook, or an RStudio Server — all from the same dashboard or CLI.
  • GPU acceleration. NVIDIA GPUs available for containers and notebooks, with PyTorch, RAPIDS, and CUDA pre-installed.
  • Simple, transparent pricing. Per-hour billing with no egress fees, no hidden charges, and no minimum commitments. Stop an instance and stop paying for compute immediately.
  • API and CLI first. Everything you can do in the dashboard, you can do programmatically. Spin up infrastructure from a script, tear it down when the job finishes.

Who should use Carolina Cloud

Section titled “Who should use Carolina Cloud”

Teams migrating from EC2, Azure VMs, or GCP Compute Engine. If you’re running instances on a major cloud provider but not deeply invested in their managed services ecosystem (RDS, Lambda, SQS, etc.), switching is straightforward. You get the same dedicated compute at a lower price point without rearchitecting your stack.

Bioinformaticians and genomics researchers. This is our bread and butter. Our genomics containers ship with samtools, GATK, STAR, PLINK, Nextflow, and a full R/Bioconductor stack. RStudio Server instances include integrated Shiny Server for interactive applications. Teams running multi-omics, proteomics, and variant analysis pipelines use Carolina Cloud daily.

Data scientists who need serious hardware. Whether you’re training models on large datasets, running distributed computations, or need a persistent high-resource development environment you can SSH into from your IDE, Carolina Cloud gives you the raw compute without the AWS bill.

Production workloads that don’t need hyperscaler complexity. Real customers run production services on Carolina Cloud — GPU-accelerated inference pipelines, transcription services on RTX 5090s served over Tailscale, scheduled ETL jobs. If your workload needs reliable dedicated hardware and doesn’t require multi-region failover, we’re a compelling option.

Who should not use Carolina Cloud Carolina Cloud is not the right fit if you need multi-region high availability, auto-scaling infrastructure, or large-scale multi-GPU training clusters with InfiniBand/NVLink interconnects. For those workloads, a hyperscaler is the right choice.