How a top global investment firm standardized local AWS development with LocalStack

The challenge: Standardizing cloud development and reducing tooling fragmentation

As AWS usage grew across the organization, engineering teams adopted different tools and patterns for cloud development.

Over time, this created inconsistent practices, duplicated efforts, and friction when teams needed to collaborate or align on shared standards.

The organization set out to move from siloed, team-by-team approaches toward a consistent, reusable foundation for local AWS development and testing.

Engineering leadership identified the need for a common local AWS emulation layer to support standardized CI/CD and cloud development practices. This would empower engineering teams to validate AWS workloads, integration behavior, and recovery paths locally —before promoting changes to higher environments and without requiring each team to maintain its own specialized AWS setup.

What was at risk

At enterprise scale, fragmentation isn’t just inconvenient—it compounds. Without a standardized approach, the organization faced escalating risk across delivery speed, reliability, and governance:

• Continued fragmentation and redundant workstreams across siloed engineering teams 
• Slower adoption of standardized cloud-native practices and CI/CD workflows
• Difficulty supporting resilience goals (DR/HA, recovery validation) and comprehensive failure-mode testing
• Slower progress on AWS-centric data science and AI/ML initiatives (including migration efforts)

What mattered in the decision

To make standardization stick across multiple teams, the organization prioritized a solution that could:

• Integrate into existing CI/CD workflows rather than require a net-new toolchain
• Provide a centrally supported, reusable local AWS development foundation
• Enable reliable local validation for AWS-centric workloads (including resilience and recovery scenarios)
• Support a rollout model that drives real adoption through enablement and governance alignment

The solution: A standardized local AWS development platform

LocalStack provided a centrally supported, reusable platform for local AWS emulation.

By integrating into existing CI/CD workflows, LocalStack enables teams to build and test AWS-centric workloads locally using consistent patterns—helping reduce variability across teams and improving confidence before changes are promoted to higher environments.

Key applications

• Local development and testing for core AWS services used in CI/CD workflows
• CI/CD pipeline integration for automated testing
• Integration testing for resilience, DR/HA, and recovery validation
• Simulated outages and other disruption for failure-mode validation
• Testing AWS-centric data science and AI workloads, including migration efforts

Implementation: What made the rollout stick

Rather than treating local AWS development as a team-by-team decision, the organization approached LocalStack as a platform capability—supported by centralized ownership, enablement, and a disciplined rollout model designed to drive real adoption.

• Disciplined rollout and license strategy: Adoption was prioritized for teams most ready to implement and demonstrate value, helping to avoid low-usage patterns.
• Enablement and best-practice sharing: A core engineering/platform leader guided adoption and shared working patterns to accelerate standardization across teams.
• Governance alignment: By minimizing exposure to the public cloud, the rollout of local development aligned with the organization’s existing security and privacy governance processes.

Results and benefits

With a standardized local AWS foundation in place, the organization improved consistency across teams while enabling earlier, more repeatable validation.

• Developer velocity & standardization: Improved speed of execution across engineering groups through a shared local AWS foundation, reducing friction as teams adopted standardized CI/CD and cloud development practices.
• Cloud-native & AI readiness: Supported AWS-centric development, including data science and AI use cases.
• Resilience validation: Enabled teams to validate failure modes, integration behavior, and recovery paths locally before promotion to higher environments—supporting DR/HA and reliability initiatives.
• Organizational consistency: Accelerated the move from ad hoc tooling toward centrally endorsed, reusable platform patterns aligned with a broader standardization effort.