Genomics Pipeline: Saving 70% Per Month with a Superior Development Experience
A genomics startup with less than a year of runway needed to build a pipeline for processing cattle genomic data—merging chromosomal data from hundreds of animals to identify genetic markers. They’d been using AWS HealthOmics for other genomics tasks, but as they evaluated their options for this greenfield project, two problems emerged: the cost would be prohibitive, and the developer experience would make iteration nearly impossible.
This is the story of how they built their pipeline on Carolina Cloud’s bare metal Threadripper infrastructure and gained the development velocity they desperately needed—with 70% cost savings as a bonus.
The Setup: A Greenfield Pipeline Challenge
Our customer came to us with a problem, not a solution. They needed to process cattle genomic data—merge imputed VCF (Variant Call Format) files for 800+ cows, each with 30 chromosomes worth of genetic data. This was a greenfield project: they knew what they needed to accomplish, but they hadn’t built the pipeline yet.
The Pipeline Challenge:
- Start with one VCF file per cow containing all 30 chromosomes
- Split each cow’s data by chromosome (24,000 files, ~100MB each, 2.4TB total)
- Use
bcftools mergeto merge all 800+ cows’ data for each chromosome- Concatenate all 30 merged chromosomes into one final compressed VCF
Their options: continue with AWS HealthOmics at ~$2,000/month, or move to regular EC2 instances at $3,000-5,000/month.
The Breaking Point: Choosing the Wrong Tool for the Job
Looking at their options, the problems became clear:
Development Would Be Challenging
HealthOmics would make development challenging. HealthOmics is optimized for running production workloads, not for iterating and debugging on a greenfield pipeline. Each time they needed to test a change, they’d face long spin-up times and limited debugging visibility. For a startup with less than a year of runway, this iteration tax would be crushing.
The Cost Problem
Costs would be high. HealthOmics would run ~$2,000/month, and EC2 instances with sufficient CPU power would cost $3,000-5,000/month—neither sustainable for a startup watching every dollar.
Technical Requirements
The technical requirements were clear. bcftools merge is the industry-standard tool for merging VCF files, but it’s inherently serial—you can’t just throw more cores at a single merge operation. The linear nature of chromosomal data means you need strong single-core performance, not GPU parallelization.
But here’s the insight: while you can’t parallelize a single chromosome merge, you can run 30 chromosome merges in parallel. The winning strategy would be Python threading orchestrating parallel bcftools command-line calls—one per chromosome. For that, they needed fast single-core performance, enough cores to run ~30 merges simultaneously, fast local disk I/O, and bare metal access to rapidly iterate.
EC2 was prohibitively expensive. Regular EC2 instances with the CPU power they needed would run $2,700-4,700/month on-demand, and they couldn’t commit to reserved instances while building a pipeline from scratch.
Enter Carolina Cloud: Building the Pipeline on Bare Metal
When the customer approached us, we knew immediately what they needed: a high-core-count CPU with excellent single-threaded performance, fast local NVMe storage, and bare metal access for maximum development velocity. When you’re building a pipeline from scratch and iterating multiple times per day with limited runway, even small performance differences add up—the boost from 4.0 GHz to 5.3 GHz could save up to an hour per iteration cycle.
We set them up with Vulcan: our AMD Threadripper 7975WX server.
Vulcan Specs:
- CPU: AMD Threadripper 7975WX
- Cores/Threads: 32 cores / 64 threads
- Clock Speed: 4.0 GHz base, up to 5.3 GHz boost
- Storage: Fast local NVMe
- Perfect fit: 32 cores for 30 chromosomes (one per chromosome + headroom)
The 32-core count was almost perfect for their workload—cattle have 30 chromosomes, so they could process nearly one chromosome per core with room to spare. More importantly, it’s bare metal—they could SSH in, install bcftools, write Python scripts, and iterate on their pipeline architecture in real-time.
Because the startup was based in North Carolina, they qualified for our free credits program—allowing them to build and test their pipeline risk-free. The latency was also unbeatable. Developing in VSCode via SSH felt completely native—no lag, no waiting for remote commands.
They built their winning architecture on our infrastructure. Using Python’s threading library to orchestrate parallel bcftools command-line calls, they developed a pipeline that ran 30 chromosome merges simultaneously—one per chromosome. Using htop to monitor CPU utilization in real-time, they could see all cores pegged during merge operations, confirming their strategy was working. The local NVMe storage meant no network latency when reading and writing those 24,000 intermediate files.
Key Results
The results spoke for themselves:
- Built in days, not weeks: Bare metal access meant rapid iteration from concept to production
- 10x faster development cycles: Testing a change took minutes instead of hours
- Zero-latency development: NC-based startup connecting to NC infrastructure—VSCode felt native
- Complete control: Install, test, and debug any tool instantly without fighting managed service constraints
- 70% cost reduction: $1,200/month fixed cost vs $2,000-5,000/month on AWS
- Perfect architecture: 32 cores for 30 chromosomes with headroom to spare
- Full control: Direct access enabled both rapid pipeline development and seamless daily production runs—no deployment overhead
- Industry-standard environment: The genomics industry predominantly uses bare metal Linux schedulers (SLURM, SGE), so their team’s existing expertise translated directly
The pipeline they built processed 24,000 files across 30 chromosomes in parallel, using every core efficiently.
The Bottom Line
Here’s how the options compared for their genomics pipeline:
| Metric | AWS HealthOmics | EC2 (c7a.32xlarge) | Carolina Cloud (Vulcan) |
|---|---|---|---|
| Monthly Cost | ~$2,000 | $3,000-5,000 on-demand | $1,200 fixed |
| Iteration Speed | ⚠️ Hours (deployment overhead) | Minutes (boot + setup) | Seconds (bare metal) |
| Development Experience | ⚠️ Managed service constraints | ✅ Full Linux control | ✅ Bare metal + local latency |
| Single-Core Performance | Limited control | Good (AMD EPYC) | Excellent (up to 5.3GHz boost) |
| Storage I/O | S3 network latency | EBS network-attached | Local NVMe (instant) |
| Best For | Production genomics pipelines | Flexible cloud workloads | Fast iteration + cost control |
A Note on This Case Study
This solution worked exceptionally well for this customer because of their specific requirements: high single-threaded CPU performance, local I/O for thousands of small files, and above all, the need to iterate quickly with limited runway. Their genomic workload wasn’t GPU-compatible due to the inherently sequential nature of chromosomal merge operations.
AWS HealthOmics and EC2 both have their place—HealthOmics is excellent for teams running production genomics pipelines, and EC2 offers unmatched flexibility and global scale. We’re not saying managed services are bad; we’re saying they’re not always the right answer, especially for startups that need to move fast.
We’re also not for everyone. If you need global redundancy, automatic scaling, or prefer managed infrastructure, AWS is probably the better choice. But if you’re a startup that needs to move fast, control costs, and have complete access to your compute environment, we should talk.