Case Study 03
How a mid-size financial firm automated bare metal Kubernetes clusters with CAPI and GitOps for Day 2 operations.
A mid-size US financial firm wanted to modernize its infrastructure with bare metal Kubernetes for trading systems, analytics workloads, and compliance tooling, but manual cluster operations and weak Day 2 practices were slowing adoption and increasing risk.
Who this engagement was for.
The client was a mid-size financial firm in the US with around 200 employees, focused on asset management, trading platforms, and regulatory compliance reporting. Their internal team included DevOps engineers and application developers, but lacked deeper experience with advanced Kubernetes orchestration in bare metal environments.
They needed a platform model that could support secure, scalable containerized workloads without introducing unnecessary operational fragility.
Why manual cluster management was not sustainable.
In mid-2025, the firm wanted to adopt Kubernetes for high-frequency trading systems, data analytics, and compliance tools while running directly on bare metal to avoid virtualization overhead and retain tighter hardware control. The problem was that provisioning bare metal servers, installing Kubernetes, and configuring clusters still relied on error-prone scripts and manual effort that consumed weeks at a time.
There was no standardized way to create, scale, or upgrade clusters, which led to inconsistent environments. Day 2 operations such as deployments, updates, monitoring, and rollbacks were also handled ad hoc, creating downtime risk in a 24/7 financial environment. At the same time, FINRA and SEC expectations demanded stronger auditability, security, and recovery posture. The result was fragmented systems, rising operational risk, and a delayed Kubernetes rollout.
Automating bare metal Kubernetes with CAPI and GitOps.
- Used Cluster API to declaratively define and provision bare metal clusters through Kubernetes-native manifests, automating machine provisioning, control plane setup, and worker node scaling.
- Integrated bare metal providers for hardware discovery and management so clusters could run directly on dedicated servers with the security and performance profile needed for financial workloads.
- Implemented GitOps workflows with Git as the source of truth for clusters, applications, and policies, enabling automated reconciliation and safer rollbacks.
- Added security and compliance controls including RBAC, audit logging, and policy enforcement tied into the GitOps workflow for traceable operational changes.
- Introduced observability with Prometheus and Grafana for cluster metrics, alerting, and proactive resource management.
What the implementation looked like.
We worked with the firm to automate their bare metal Kubernetes platform using Cluster API as the declarative lifecycle layer, with provider tooling for bare metal environments such as Metal3-style patterns. This created a repeatable operating model for provisioning, scaling, and upgrading clusters without reverting to custom scripts and one-off procedures.
On top of that foundation, we implemented GitOps for Day 2 operations using tools such as Flux or ArgoCD so that clusters, applications, and policies could all be version-controlled and continuously reconciled from Git. This gave the team a much cleaner operating model for deployments, updates, rollback behavior, and compliance evidence.
The end-to-end deployment was completed in eight weeks, including training so the internal team could maintain and extend the platform independently.
What changed after the platform went live.
The firm moved from stalled manual processes to fully operational Kubernetes clusters in under two months, which accelerated the rollout of containerized trading and analytics applications. Day 2 management time dropped by 60 percent through GitOps automation, reducing human error and improving operational consistency.
Bare metal resource utilization improved enough to cut provisioning costs by roughly 30 percent while avoiding vendor lock-in from proprietary tooling. Auditability also improved because cluster and application changes were versioned in Git and aligned with a declarative control model, helping the team satisfy compliance expectations more cleanly.
Business results followed. The firm launched a Kubernetes-based analytics platform that improved trade processing speed by 40 percent and now operates more than 10 clusters reliably across its environment. More importantly, the team now has a stronger platform foundation for future growth.