While at Splunk, transitioning a monolithic on-premise product into a scalable, multi-tenant cloud platform was a transformative engineering endeavor. Among the most complex and impactful areas of change was our approach to Continuous Integration and Continuous Delivery (CI/CD). The legacy model: manual, ticket-based, and weeks-long; was incompatible with the velocity required for cloud-native innovation.

The Problem

1. Manual Releases Bottleneck Innovation

Our release process was fragmented and painfully slow. A typical release cycle involved:

  • Filing tickets and waiting up to a week for approvals
  • Another week or so to provision infrastructure
  • Manual test execution across fragmented environments
  • Redundant cycles when issues inevitably arose

This lead time was a critical blocker for developer productivity and agility. As an architect on the platform team, I saw a clear need to reimagine our release strategy — not just to automate, but to align delivery velocity with the expectations of a cloud-native engineering organization.

2. Cross-Functional Complexity

Solving this problem wasn’t confined to a single domain. It required coordination across ingest, indexing, search, platform, build, infrastructure, and test teams. We needed an architecture that unified these disparate components into a resilient, automated pipeline — without compromising flexibility or quality.

The Solution: CI/CD Reimagined

I architected a solution that revolved around four key design principles:

1. Unified Release Strategy Across Deployment Targets

We standardized on a single development branch as the source of truth for both cloud and on-premise releases. Runtime flags controlled feature toggling and environment-specific configurations. This decision eliminated branching complexity and enabled:

  • Faster, consistent releases
  • Reduced overhead from managing divergent code paths
  • Easier debugging and traceability

This was a deliberate trade-off favoring long-term maintainability and developer simplicity over optimizing for narrowly tailored pipelines.

2. Dynamic, On-Demand Kubernetes Infrastructure

To replace the static provisioning model, we introduced dynamic environment creation using Kubernetes. Key elements included:

  • Declarative service definitions for cloud builds
  • Custom load balancers for precise routing and traffic shaping
  • Just-in-time (JIT) tenant provisioning for ephemeral test environments

This setup allowed any branch or feature to be deployed into a fully isolated environment within minutes — a major leap from the previous multi-week provisioning cycle.

3. Lightweight, Isolated Container Execution

Many of our workloads required quick execution of single-container services (e.g., lightweight microservices or test runners). Rather than spinning up full virtual environments, we used:

  • Kubernetes node taints and tolerations to allocate dedicated resources
  • Isolation at the pod level for performance and security
  • Horizontal scalability for parallelized builds

This achieved faster spin-up times and better resource efficiency without sacrificing container-level isolation.

4. Custom Kubernetes Controller for Declarative Orchestration

The backbone of this system was a custom Kubernetes controller that managed:

  • Infrastructure provisioning
  • Application deployment
  • Automated teardown after successful merges

This controller introduced a declarative API for orchestrating complex release workflows. It abstracted the lifecycle management of our environments, enabling development teams to interact with CI/CD as a self-service model rather than through platform ops or ticket queues.

The Outcome: Fully Automated Developer-Centric CI/CD Pipeline

The architecture enabled several key capabilities:

  1. Commit-to-Release Deployability: Any branch could be tagged and released with no manual intervention
  2. Persistent Dynamic Sandboxes: Environments persisted across commits for consistent testing and reduced spin-up time
  3. Integrated Automated Testing: Testing teams contributed Dockerized test suites that were auto-invoked within the pipeline
  4. Automatic Teardown: Environments were cleaned up post-merge to optimize resource use and cost

Business Impact

  • 100% Cloud Release Automation
  • Weeks reduced to minutes in infrastructure provisioning
  • Significant developer velocity gains, enabling faster iteration and delivery
  • Hundreds of hours saved per release cycle, accelerating roadmap delivery

This initiative was foundational in Splunk’s cloud transformation. The engineering decisions, particularly the unified main branch strategy and investment in a custom controller, prioritized simplicity and scale. In doing so, we not only solved a technical bottleneck, but unlocked the velocity necessary for modern SaaS innovation.