Deciphex Scales AI Pathology With Predictable Storage Costs
Wasabi complements Deciphex’s AWS infrastructure, helping the company scale its AI data with more predictable storage costs.
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6.5 PB
Digital pathology data stored and managed
€0
Wasabi egress fees for the workloads described
~50ms
Global round-trip time for pathologist image review
Overview
Deciphex builds AI-powered digital pathology software used by top pharmaceutical companies and national health services to review and share digitized tissue samples. As its image library grew into the petabytes, Deciphex added Wasabi alongside AWS to support data-intensive workloads and improve storage cost predictability.
INDUSTRY:
Healthcare & Life Science
USE CASE:
AI/ML
Cloud Computing
Data Lakes & Analytics
Disaster Recovery
Challenges
Deciphex uses AWS as part of its cloud infrastructure, moving large digital pathology images between regions and institutions as cases pass through multiple review stages. As data volumes grew, the team evaluated complementary storage options for these workloads.
Managing data growth
Increasing image volumes and frequent data transfers made storage cost optimisation a priority.
Budget predictability
The team wanted a more predictable storage cost model to support its growing image library.
Multicloud flexibility
Deciphex wanted storage that supported its cloud-agnostic architecture and worked alongside its existing AWS services.
Solution
Deciphex adopted Wasabi as the hub for its research storage roughly four years ago, based on its pricing model and performance for these workloads. Wasabi stores research data, while archival data remains in AWS. The team is building a large research data lake in Wasabi to support next-generation algorithms. Deciphex is also extending Wasabi’s role toward production clinical workloads, with a UK rollout planned by year-end, as part of a multicloud architecture spanning AWS and Oracle.
Provisioning and management simplicity - The team uses Wasabi Account Control Manager to provision and track individual storage accounts for each environment, all managed through Terraform.
Multicloud integration - Wasabi’s pricing model helps Deciphex manage storage access costs within an architecture spanning Wasabi, AWS, and Oracle Cloud.
Global storage regions - They use Wasabi’s US and UK storage regions to support pathologists reviewing cases across North America, the UK, and India, with Wasabi’s existing regional footprint, including Japan, positioned to support the company’s planned international expansion.
A foundation for what’s next - Deciphex stores research data, including raw digital pathology images used in several research collaborations, in Wasabi today and is exploring how to extend that same foundation to support the embeddings and vector data behind its next generation of pathology AI models.
Results
By adding Wasabi alongside AWS, Deciphex improved storage cost predictability for its data-intensive workloads and expanded the flexibility of its AI-driven digital pathology platform.
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Cost predictability - Wasabi’s pricing model helped the team plan for growing storage volumes and frequent image access.
Responsive image access - Deciphex found that Wasabi supported the fast image access required for the workloads evaluated.
Multicloud flexibility - Wasabi complements AWS and Oracle Cloud within Deciphex’s architecture, allowing the team to select services suited to each workload.
Room to grow - Deciphex manages 6.5 PB of pathology data across its storage operations and continues to expand into new regions, including a planned UK rollout by year-end and future plans for the US.
Control medical imaging and life sciences costs, build multicloud flexibility
Explore Wasabi storage for healthcare and life sciences AI workloads with growing data needs.