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Six-Week OpenShift Validation Locked in $1M Annual Savings

  Ross Beard  ·  3 minute read

Summary:

AWS spend surged, so a cybersecurity company targeted $1M in annual savings in 12 months without exiting AWS completely. Red Hat and Shadow-Soft validated hybrid OpenShift in six weeks, shifted baseline workloads on-prem, and opened expansion across the Red Hat stack.

The Challenge

AWS spend climbed past what leadership could justify. As a result, the team set a hard target: cut $1M in annual AWS costs within a 12-month window and save an additional $1.5M in savings by year two.

To achieve this goal, they needed to reduce their dependency on AWS while keeping a hybrid posture for workloads that still needed cloud characteristics and burst capacity. That constraint forced a platform-level decision under a deadline.

The team built quickly, owned their own delivery, and had pockets of containers and Kubernetes. However, their engineers didn’t share a consistent approach to container management or Kubernetes practices across teams.

Meanwhile, the business needed to adopt a tool and start implementing changes.

Our Solution

The client chose Red Hat OpenShift to migrate baseline workloads out of AWS and hit a fixed-cost target without exiting AWS. OpenShift replaced the client’s prior SUSE Rancher platform as part of the shift off the AWS baseline spend.

Specifically, OpenShift ran on-premises for steady-state workloads and kept a hybrid path for cloud burst when demand spiked.

Red Hat partnered with Shadow-Soft to validate the technical approach with OpenShift and keep the program on schedule. We guided architecture across the target environment and validated that OpenShift met the client’s constraints: an aggressive savings timeline, hybrid requirements, and the need for consistent Kubernetes operations across teams.

The final approach is anchored on OpenShift Platform Plus with Ansible and RHEL, with a clear expansion path: ROSA for remaining AWS workloads and OpenShift AI as the next adoption phase once the core platform is stabilized.

Our Process

The client couldn’t afford a slow evaluation cycle, so the team treated the rollout like an accelerated validation sprint with a weekly decision cadence.

The partner and Red Hat kept the work moving by forcing clarity fast, then backing every decision with technical proof.

  1. Align on nonnegotiables: $1M annual savings target, hybrid posture, move baseline workloads on premises, keep AWS for burst capacity.
  2. Start an OpenShift trial and POC fast, with pass fail checks on speed and storage integration.
  3. Run multiple working sessions per week across the client, Red Hat, and Shadow-Soft to close technical unknowns and pick tradeoffs in real time.
  4. Pull in three Shadow-Soft technical resources to map the current environment, surface integration risks early, and build the target architecture.
  5. Bridge the commercial path alongside the technical path: Shadow-Soft joined POC and budgeting calls and carried continuity through close.
  6. After the POC, right-size the subscription to the full platform tier and roadmap growth beyond the initial six-node count.
  7. Stay engaged after close to handle the first performance friction point and keep the rollout from stalling.

The Roadblocks

Urgency compressed the learning curve. The client pushed changes quickly as they learned a new Red Hat platform. That pace led to breakages that required corrective maintenance.

Shadow-Soft and Red Hat stayed close, resolved issues as they surfaced, and kept the timeline intact without slowing the rollout.

Storage performance created the second friction point. The client ran 30 terabyte drives even though Ceph supports 16 terabyte drives, so Red Hat approved a support exception.

When storage latency hurt application performance, the teams tuned the environment and refactored query workloads until the environment held in production.

The Toolstack

  • OpenShift Platform Plus served as the supported Kubernetes baseline for a hybrid footprint and helped shift steady state workloads on-prem while keeping AWS in scope.

  • Red Hat Advanced Cluster Management (ACM) centralized cluster lifecycle and policy control across the fleet.

  • OpenShift GitOps (Argo CD) standardized deployments through declarative config and enforced drift control.

  • CloudNativePG standardized Postgres operations in Kubernetes for provisioning, scaling, and recovery.

  • Red Hat Enterprise Linux (RHEL) ran under the OpenShift nodes as the standard OS to reduce variance.

  • OpenShift Data Foundation (ODF) delivered persistent storage for OpenShift and ran on Ceph.

  • AWS remained in the architecture to support hybrid workloads while reducing baseline cloud spend.

The Results

The client moved from runaway cloud spend to a controlled cost program with a clear runway. They reached $1M in annualized savings inside the first 12 months, with a defined path to $1.5M in year two as they expand the OpenShift footprint and absorb more workloads off AWS.

They also turned a fragmented engineering operating model into a single Kubernetes standard. OpenShift gave teams a consistent deployment approach, reduced internal variance, and eliminated the lone-ranger pattern that slowed repeatability and made scaling difficult.

The work created momentum beyond the initial cost target. The client built a roadmap for AI readiness on OpenShift and a hybrid strategy for the remaining AWS workloads through ROSA, keeping burst capacity on the table as they continue shrinking baseline cloud consumption.

Key Results:

  • Achieved $1M annual savings within 12 months
  • On the path to reach $1.5M as adoption expands
  • Standardized Kubernetes deployments across teams on OpenShift
  • Restored production performance by tuning storage and refactoring workloads
  • Defined an AI readiness plan with GPU and operator validation milestones
  • Built a hybrid plan for the remaining AWS workloads via ROSA

What’s Next?

The client is shifting from proving viability to scaling usage. They’re finishing the planned Kubernetes workload rollout on OpenShift to lock in consistent deployment standards across teams, while they keep pushing down AWS use to secure the year-two savings target.

Now, they’re considering new Red Hat tools: for example, validating OpenShift for AI workloads through GPU node enablement, storage optimization, and operator deployment while revisiting ROSA to bring most remaining AWS workloads under the same OpenShift operating model.

They’re also evaluating lower-cost OpenShift clusters for preproduction and staging, so engineering can get dev environments without recreating the AWS cost creep.