AWS vs GCP vs Azure for Bioinformatics: A Head-to-Head Comparison

The Cloud Is No Longer Optional

A single whole genome sequence generates roughly 100GB of raw data. A population-scale study with 10,000 samples? That's a petabyte. Add in the computational demands of alignment, variant calling, and downstream analysis, and it becomes clear why on-premises HPC clusters are no longer sufficient for most genomics organizations.

The cloud offers elastic compute, virtually unlimited storage, and managed services that can dramatically accelerate bioinformatics workflows. But with three major providers competing for your workloads, the choice isn't straightforward. Here's our honest assessment based on hundreds of real-world deployments.

AWS: The Market Leader

Strengths

• Ecosystem maturity: AWS has the broadest set of services relevant to bioinformatics. AWS Batch for job orchestration, S3 for storage, and AWS HealthOmics (formerly Amazon Omics) as a dedicated genomics service
• AWS HealthOmics: Purpose-built for genomics — includes managed workflow engines, variant stores, and annotation services. Significantly reduces infrastructure management overhead
• Marketplace: The richest ecosystem of third-party bioinformatics tools available as pre-configured AMIs and containers
• Compliance: The most HIPAA-eligible services of any provider, with extensive documentation and compliance programs
• Spot instances: Excellent spot market with reasonable interruption rates, typically saving 60-80% on compute costs

Weaknesses

• Pricing complexity — the sheer number of services and pricing dimensions makes cost prediction difficult
• Data egress costs remain high, which can be painful for multi-cloud or hybrid architectures
• The learning curve for IAM and networking is steeper than competitors

Google Cloud Platform: The Data Analytics Powerhouse

Strengths

• BigQuery: Unmatched for large-scale genomic data querying. Variant data in BigQuery can be queried with standard SQL at petabyte scale
• Google Cloud Life Sciences API: Solid managed pipeline execution engine, deeply integrated with Google's infrastructure
• Vertex AI: Best-in-class managed ML platform for teams building custom models on genomic data
• Pricing simplicity: Sustained use discounts applied automatically, per-second billing, and more predictable pricing than AWS
• Network performance: Google's global network provides excellent data transfer speeds between regions

Weaknesses

• Smaller bioinformatics ecosystem compared to AWS
• Fewer HIPAA-eligible services (though the gap is closing)
• Less mature batch computing options — Cloud Batch is newer and less feature-rich than AWS Batch

Microsoft Azure: The Enterprise Choice

Strengths

• Enterprise integration: If your organization already runs on Microsoft 365 and Active Directory, Azure provides seamless identity management
• Azure Genomics: Microsoft Genomics service provides managed Cromwell/WDL execution on GATK best practices pipelines
• Hybrid cloud: Best-in-class hybrid connectivity with Azure Arc and Azure Stack, ideal for organizations with significant on-premises investment
• Compliance: Strong compliance portfolio including FedRAMP High, making it the go-to choice for government-funded research

Weaknesses

• The genomics-specific tooling feels less polished than AWS or GCP equivalents
• Documentation for life sciences workloads is sometimes sparse
• Spot VM (Azure Spot) pricing is less predictable than AWS spot instances

Our Recommendation

After deploying bioinformatics infrastructure across all three platforms, here's our guidance:

The truth is that all three platforms are capable of running production bioinformatics workloads. The best choice depends on your specific requirements, existing infrastructure, and team expertise. And increasingly, we're seeing organizations adopt multi-cloud strategies — running pipelines on the platform best suited for each workload type.