RCH Achieves AWS Life Sciences Competency

Challenge 

In fast-paced Life Sciences environments, cloud resources are often rapidly scaled to meet the demands of data-intensive research and analytics. Without proactive governance, this flexibility may lead to overprovisioned resources, unchecked service sprawl, poor cost visibility, and unexpectedly high cloud spending.

Solution  

The team at RCH Solutions assists Life Sciences teams in modernizing their cloud environments with intelligent cost controls. By integrating policy-driven governance, event automation, and FinOps visibility, we empower computational and data scientists to innovate freely, without compromising on cost discipline.  Whether your organization is building on AWS or looking to regain control over cloud spend, RCH’s proven approach delivers results that scale.

Solutions and Outcomes

Below are several recent examples of how the team of specialists at RCH Solutions identified and established comprehensive, scalable cost control frameworks while ensuring alignment with the client’s operational and compliance needs.

Example 1

To control costs while still allowing end-users the flexibility to launch instances as needed—based on client requirements and workflows—engineers at RCH Solutions recommended and implemented Service Control Policies (SCPs) within the client’s AWS environment. These policies restricted users to specific, approved instance types, helping to prevent over-provisioning and unnecessary cloud spend. Additionally, budgets were configured for each account to provide management with clear visibility of spending.

Example 2

The Data Sciences team at a clinical-stage biotechnology company was extensively utilizing Amazon SageMaker services, which can become costly without proper oversight—due to factors such as incorrect instance sizing or idle resources left running unnecessarily. To address this, RCH engineers developed a solution that:

• • • Restricted users to approved instance types
    • Sent notifications when SageMaker resources were created, started, or stopped
    • Identified idle resources and automatically shut them down after a client-defined idle threshold

Additionally, daily spend alerts were configured to report both the current day’s costs and the cumulative month-to-date spending. These measures not only helped control costs but also provided leadership with direct visibility into usage and spending—delivered straight to their inbox, eliminating the need to log into a separate provider console or dashboard.

Example 3

During a cost and configuration review, RCH engineers discovered that Amazon S3 versioning had been enabled on a bucket containing large volumes of data, despite version retention not being required for the client’s workflows. Over time, this resulted in a significant accumulation of non-current object versions and incomplete multipart uploads, quietly inflating storage costs.

RCH brought this finding to the client’s attention and recommended disabling versioning unless explicitly needed. After confirming that versioning was unnecessary, the team implemented lifecycle policies to remove legacy object versions and stale uploads, and disabled versioning on the bucket.

This targeted optimization led to a 78% reduction in Standard storage class costs for the affected bucket, achieved without impacting operations or compromising data integrity.