Case Study

Jenkins to GitLab Migration Success

Discover how a Jenkins to GitLab Migration Success enhanced our clients CI/CD processes for better efficiency and security

9 min read · DevOps · DevSecOps · Migration

Donovan Mulder

Donovan Mulder, Author

What you'll learn

  1. Understand how consolidating from Jenkins to GitLab reduces pipeline sprawl and unifies release evidence for auditors

  2. See how incremental migration preserves delivery cadence without freezing feature work for a quarter

  3. Learn why naming runner owners and retiring duplicate jobs improves deployment frequency and recovery time

Case study hero for Jenkins to GitLab migration and CI/CD consolidation

At a glance

KineticSkunk helped a delivery team consolidate from Jenkins to GitLab CI/CD, eliminating pipeline sprawl, unifying security scans with merge history, and giving auditors a single source of release evidence. The migration was incremental, preserving delivery flow while retiring duplicate tooling.

Teams maintaining Jenkins alongside GitLab face growing maintenance cost, unclear ownership of quality gates, and audit gaps where pipeline evidence lives across disconnected systems. GitLab 17 advances in CI/CD components and compliance pipelines make 2026 the year consolidation pays off fastest.

Key takeaways

  • GitLab unified pipelines, security scans, and merge requests in one platform so context stayed attached to the work.

  • Retiring duplicate Jenkins runners and assigning clear owners improved deployment frequency and mean time to recovery.

  • Incremental migration let teams keep shipping while moving jobs across, avoiding a risky big-bang cutover.

  • Flaky jobs treated as product defects, not noise, accelerated feedback loops for the whole organisation.

  • Merge request templates that forced risk, test, and rollback notes built habits that outlasted the migration project.

  • Audit evidence became traceable because scan results, approvals, and artefacts lived beside the code in GitLab.

What is it?

This case study covers how KineticSkunk migrated a delivery organisation from Jenkins to GitLab CI/CD, consolidating pipelines, security scanning, and release evidence into a single platform without halting delivery.

The client operated Jenkins and GitLab in parallel, duplicating runner infrastructure, splitting audit trails, and obscuring who owned quality gates. Teams needed one authoritative pipeline platform that satisfied both engineering velocity and compliance expectations.

Use this approach when a team has outgrown Jenkins, needs unified security and compliance evidence beside code, and wants an incremental path that does not freeze delivery for months.

Why it matters

Risks

  • Pipeline sprawl across Jenkins and GitLab hides who owns quality gates and creates audit blind spots.
  • Parallel tooling means security scan results live in disconnected systems, making compliance evidence incomplete.
  • Big-bang migrations risk delivery freezes that erode stakeholder confidence and delay feature commitments.

Costs

  • Maintaining duplicate runner fleets and plugin ecosystems multiplies infrastructure and engineering time costs.
  • Manual audit assembly from multiple systems consumes compliance team hours that could go to risk reduction.
  • Flaky Jenkins jobs that nobody owns waste pipeline minutes and developer attention on false failures.

Operational impact

  • Split tooling creates knowledge silos where only specific engineers can troubleshoot specific pipeline failures.
  • Without unified merge request context, code review misses pipeline signals that would catch issues earlier.
  • Recovery time extends when incident responders must check both Jenkins and GitLab to understand what deployed.

Strategic impact

  • Competitors who consolidate on one platform ship faster because context switching between tools disappears.
  • Unified release evidence strengthens the compliance story for regulated industries and partner audits.
  • Teams that treat migration as a continuous improvement programme build habits that outlast any single tool.

How KineticSkunk completed the Jenkins to GitLab CI/CD migration engagement

Tooling sprawl and audit gaps

  • KineticSkunk assessed the existing Jenkins estate, cataloguing every job with an owner, SLA, and retirement date.
  • Audit gaps were identified where pipeline evidence was split between systems, making compliance proof fragile.
  • The assessment established which jobs could migrate immediately and which needed dependency resolution first.

Risk during parallel run

  • A controlled parallel-run period kept both systems active so teams could validate GitLab pipelines against Jenkins baselines.
  • Merge request templates enforced risk, test, and rollback notes to build discipline before Jenkins retirement.
  • Runner sizing used real queue times rather than peak marketing demo loads to right-size the GitLab fleet.

Migration execution

  • Jobs migrated incrementally, team by team, with clear cutover dates and rollback plans for each batch.
  • Security scans moved into GitLab pipelines so results appeared in merge requests rather than external dashboards.
  • Duplicate Jenkins runners were decommissioned as each team completed validation on GitLab equivalents.

Outcomes and habits

  • Deployment frequency improved once pipeline ownership was visible and quality gates had named owners.
  • Mean time to recovery shortened because incident context, pipeline status, and merge history lived in one place.
  • Flaky job discipline became standard practice, treating transient failures as defects with the same priority as feature bugs.

Common mistakes

Attempting a weekend big-bang cutover from Jenkins to GitLab

Consequence: Teams lose delivery momentum for weeks while troubleshooting unforeseen job differences, and stakeholders lose confidence in the migration.

Avoidance: Migrate incrementally, team by team, with rollback plans for each batch so delivery never freezes.

Mirroring Jenkins jobs one-for-one without rethinking pipeline design

Consequence: Legacy complexity transfers to GitLab unchanged, preserving the sprawl and ownership gaps the migration was meant to fix.

Avoidance: Treat migration as an opportunity to consolidate, assign owners, and retire jobs that no longer serve a clear purpose.

Ignoring flaky jobs because they existed before the migration

Consequence: Transient failures erode trust in the new platform and push teams back toward manual verification gates.

Avoidance: Treat flaky jobs as product defects with sprint priority so pipeline reliability improves alongside the migration.

Best practices

  • Inventory every Jenkins job with an owner, SLA, and explicit retirement date before mirroring in GitLab.
  • Size GitLab runners using real queue time data, not theoretical peak loads.
  • Enforce merge request templates that require risk, test, and rollback documentation.
  • Move security scans into GitLab pipelines so results appear in merge request context.
  • Treat flaky jobs as product defects with the same priority as feature bugs.
  • Decommission Jenkins runners progressively as each team validates GitLab equivalents.

Tools and processes

  • GitLab CI/CD with compliance pipelines and CI/CD components
  • GitLab security scanning (SAST, DAST, dependency scanning) integrated into merge requests
  • Merge request templates with mandatory risk and rollback sections
  • Runner fleet monitoring with queue time and utilisation dashboards

How to get started

  1. Catalogue every Jenkins job with owner, frequency, dependencies, and downstream consumers.
  2. Identify jobs that can migrate immediately versus those blocked by external dependencies.
  3. Design GitLab pipeline equivalents with security scanning and compliance evidence built in.
  4. Run a parallel period where both systems execute so teams validate results match.
  5. Migrate incrementally by team, retiring Jenkins jobs and runners as each batch is validated.
  6. Establish flaky job discipline and merge request template habits as standard practice.

If the primary pain is audit evidence, start with security scan consolidation into GitLab merge requests. If the blocker is delivery speed, start with runner sizing and queue time optimisation. Both paths converge on full Jenkins retirement.

How KineticSkunk helps

KineticSkunk helps teams migrate from Jenkins to GitLab CI/CD without freezing delivery, unifying pipelines, security evidence, and release traceability in one platform.

The client achieved unified release evidence, improved deployment frequency, and faster recovery times by consolidating on GitLab with incremental migration and clear ownership disciplines.

Browse the GitLab DevSecOps series

When you are ready to sequence runners, security gates, and cutover rehearsal, contact us. You can also explore more case studies.

Frequently asked questions

Duration depends on estate size, but incremental migration typically spans 8 to 16 weeks for medium teams, with delivery continuing throughout.

Yes. Incremental migration moves jobs team by team with parallel validation periods, so feature delivery never freezes.

Most plugin functionality maps to GitLab CI/CD components or custom scripts. The inventory phase identifies gaps early so alternatives are designed before cutover.

Yes. Security scans, approvals, and pipeline artefacts live beside the code in merge requests, giving auditors a single traceable evidence source.

Sources

Related insights

Case study hero for accelerated deployments on AWS ECS

Accelerating Deployments with AWS ECS

KineticSkunk accelerates deployments with AWS ECS automation cutting release time by 80%, reducing costs by 30%, and ensuring 99.95% uptime.

Editorial hero for GitLab pipelines, flow, and runner operations

Advantages of Gitlab with Custom Runners

A case study about the advantages of Gitlab with custom runners reflecting 70% faster deployment and scalable, secure CI/CD.

Azure data platform reporting modernisation, workload separation and BI optimisation

Eliminating Reporting Load on Production Systems

How a SaaS provider reduced reporting load on production systems by implementing a scalable Azure-based data platform.