Success Stories / Migration From GCP to AWS/ Kubernetes Implementation

Migration From GCP to AWS/ Kubernetes Implementation

A rapidly scaling e-commerce startup serving customers across Africa was experiencing infrastructure limitations that hindered its ability to support increasing demand.
CI/CD AutomationCloud MigrationKubernetes
5 min read
Calendar2025

Executive Summary

A rapidly scaling e-commerce startup serving customers across Africa was experiencing infrastructure limitations that hindered its ability to support increasing demand. The company, known for helping consumers save up to 30% on fresh produce and packaged goods, relied on a GCP-based infrastructure that lacked the scalability, automation, and operational maturity required for its next stage of growth.

To address these challenges, the client partnered with Matoffo to lead a full-scale migration from Google Cloud Platform (GCP) to Amazon Web Services (AWS), along with the design and implementation of a production-grade Kubernetes environment using Amazon EKS. The project included automated infrastructure provisioning, improved CI/CD pipelines, and high-availability deployment practices tailored to the client’s business model.

As a result, the client achieved faster deployment cycles, improved system reliability, and better cost control – empowering them to scale services across new markets with minimal operational risk and greater developer efficiency. The successful migration laid the technical foundation for continued growth while maintaining a mission-critical focus on affordability and customer access.

Client Background

The client is a fast-growing B2C e-commerce company on a mission to reduce the cost of living in urban Africa. Operating in Nairobi, Kenya, the company serves over 100,000 customers across 25 neighborhoods, offering access to affordable fresh produce and consumer packaged goods through a digital-first shopping experience.

Positioned as one of the most impactful e-commerce models for Africa’s urban majority, the platform enables consumers to access lower prices, higher-quality items, and a wider product selection – all while supporting local communities and economic empowerment.

With customer demand rising and the business rapidly expanding, the client faced growing limitations with its existing cloud infrastructure on Google Cloud Platform (GCP). Manual scaling, deployment inconsistencies, and limited automation capabilities created operational strain and slowed the company’s ability to scale efficiently. To maintain its growth trajectory and continue delivering on its mission, the client sought a robust, cloud-native solution that could deliver scalability, reliability, and faster development cycles.

Customer Challenge

As the client scaled its e-commerce operations to meet increasing demand across Nairobi’s urban neighborhoods, it became clear that its existing cloud environment could no longer support its growth. The infrastructure, hosted on Google Cloud Platform (GCP), lacked the flexibility, automation, and scalability required to maintain service quality, speed, and cost-efficiency.

 

Key Business Challenges:

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Limited Scalability:

GCP’s existing configuration could not efficiently support growing user traffic, application load, and data volume - leading to frequent resource bottlenecks.
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Manual Deployment Overhead:

Application releases and infrastructure provisioning were handled manually, increasing the risk of misconfigurations, longer downtime, and slowed delivery timelines.
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Cost Inefficiency:

As usage expanded, the client faced challenges optimizing resource allocation and managing cloud spend effectively without native cost-control tooling.
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Lack of Container Orchestration:

Without a Kubernetes-based deployment pipeline, the team struggled to run microservices reliably and scale them dynamically across environments.

These challenges not only slowed the company’s ability to serve its growing customer base but also put pressure on engineering teams – limiting their ability to innovate, iterate, and scale sustainably. Migrating to AWS with a modern, Kubernetes-powered architecture was essential to unlocking performance, efficiency, and long-term growth.

Goals and Requirements

In response to the scalability limitations, deployment inefficiencies, and infrastructure rigidity of its legacy GCP environment, the client set out to achieve a set of focused, results-driven objectives. These goals aimed to modernize the organization’s technical foundation—enabling it to support rapid customer growth, streamline development workflows, and improve cost efficiency without compromising service quality.

Performance Targets

  • Reduce Deployment Time:

    Implement automated infrastructure provisioning to cut environment setup and release cycles from hours to minutes.

  • Enhance Platform Stability:

    Ensure zero-downtime deployments with built-in fault tolerance and service health monitoring.

  • Boost Developer Velocity:

    Enable faster code-to-production delivery through CI/CD automation and containerized microservices.

Financial Targets

  • Lower Cloud Operating Costs:

    Improve resource utilization through autoscaling and right-sizing—targeting a reduction in overall cloud spend.

  • Minimize Manual Overhead:

    Reduce the time and effort required for infrastructure management and system maintenance by at least 40%.

Scalability & Reliability

  • Handle Service Growth:

    Design a platform capable of scaling to support tens of thousands of active users and frequent transaction surges across regions.

     

     

  • Ensure Business Continuity:

    Establish highly available, production-ready Kubernetes environments with multi-zone redundancy.

  • Enable Seamless Migration:

    Provide a replicable, low-risk migration framework from GCP to AWS with minimal service disruption.

By setting these strategic goals, the client sought not only to resolve immediate operational pain points but to lay the groundwork for long-term expansion, engineering efficiency, and cloud cost optimization across its fast-growing e-commerce platform.

The Solution

To resolve the limitations of the client’s GCP-based infrastructure, Matoffo implemented a structured, multi-phase migration and modernization strategy – rebuilding the client’s cloud environment on AWS and deploying a production-grade Kubernetes platform using Amazon EKS. The solution was designed for scalability, security, and zero-downtime deployment – empowering the client to support growing user demand while improving operational agility and cost efficiency.

  1. 1

    Infrastructure Blueprint and Network Design

    The engagement began with a deep analysis of the client's infrastructure and application dependencies, followed by the creation of detailed architecture diagrams and migration blueprints. A secure, production-ready VPC was deployed with public and private subnets distributed across multiple Availability Zones. This layout supported Amazon EKS, Amazon RDS, and all supporting services, while isolating compute and database components in private subnets. Developers were granted access through a VPN, while public users accessed services via ALB/NLB endpoints and an NAT Gateway.
  2. 2

    Kubernetes Deployment and Helm Optimization

    To modernize and streamline the container platform, Matoffo leveraged Amazon EKS for Kubernetes orchestration and Helm charts to manage application releases. The client’s existing manifests were converted to reusable Helm templates - enabling faster deployments, simplified rollback procedures, and consistent configuration across environments. This shift reduced release errors and made it easy to scale individual microservices on demand.
  3. 3

    GitOps and ArgoCD Integration

    To ensure safe, auditable, and automated application deployment, Matoffo implemented a GitOps model using ArgoCD. All Kubernetes manifests were stored in a dedicated Git repository, with ArgoCD continuously syncing changes to the cluster. This approach turned Git into a single source of truth, eliminated manual interference, and allowed developers to visually monitor and manage deployments through ArgoCD’s UI - with role-based permissions limiting access as needed.
  4. 4

    Observability and Cost-Optimized Monitoring

    To provide real-time insight into system health and application performance, Matoffo deployed a Prometheus/Grafana/Alertmanager monitoring stack - integrated with Loki for log aggregation and AWS CloudWatch as a supplemental data source. This gave the client a unified observability layer across all services, helping engineers detect and resolve issues without toggling between tools. Network traffic was optimized using PrivateLink and VPC peering for secure, cost-efficient communication with external SaaS components.
  5. 5

    Production Switch and Rollout

    The final and most critical phase involved executing the production cutover with zero disruption. Thorough testing was conducted in staging environments, and the cutover involved cross-functional collaboration between DevOps, QA, and development teams. With automated CI/CD pipelines and observability tools in place, the switch was executed smoothly and validated through controlled traffic monitoring.

This carefully planned migration and Kubernetes implementation delivered a scalable, secure, and developer-friendly cloud platform – laying the groundwork for future innovation, cost efficiency, and operational maturity.

Results and Impact

The migration from GCP to AWS and the implementation of a production-grade Kubernetes environment delivered substantial improvements across operational performance, scalability, cost efficiency, and developer productivity. By introducing Infrastructure as Code (IaC), GitOps workflows, and an autoscaling EKS cluster, the client was able to modernize its cloud infrastructure and support long-term growth with minimal manual intervention.

Quantitative Outcomes

  • 90% reduction in manual infrastructure tasks by automating environment provisioning and application deployments with Terraform, Helm, and ArgoCD.

     

  • Up to 60% savings in compute costs for non-production workloads through intelligent use of spot instances and autoscaling.

  • Accelerated deployment cycles by 4×, with GitOps pipelines enabling reliable, automated rollouts within minutes.

Qualitative Outcomes

  • Improved scalability, allowing the client to onboard new services and feature updates without architecture rework or manual effort.

  • Enhanced developer efficiency, as engineers now focus purely on application logic without managing deployment or CI/CD complexity.

  • Operational consistency, with centralized monitoring, unified logging, and alerting across services for faster issue detection and resolution.

This transformation gave the client a flexible, secure, and cost-optimized foundation that supports rapid iteration while keeping infrastructure complexity and overhead low.

Key Learnings

The success of the GCP-to-AWS migration and Kubernetes platform implementation was driven by a combination of architectural foresight, automation-first thinking, and close collaboration between development, DevOps, and business teams. The following principles were critical to achieving a smooth transition and building a scalable, future-ready platform:

  • GitOps as a Foundation for Deployment Consistency

    Adopting ArgoCD and a GitOps model transformed the deployment workflow. Every change was traceable, auditable, and automatically synced to Kubernetes, removing human error and streamlining operations. This method also empowered developers to contribute without needing full access to infrastructure, reinforcing both speed and security.

  • Cost-Conscious Engineering

    Strategic use of spot instances, autoscaling, and lifecycle policies across dev/staging environments led to meaningful reductions in compute cost. This practice showed that performance, reliability, and cost-efficiency can be achieved simultaneously when infrastructure is built with optimization in mind from day one.

  • Unified Monitoring and Observability from the Start

    By deploying Prometheus, Grafana, Loki, and CloudWatch as part of the initial rollout, the team ensured every service had performance, availability, and error tracking from day one. This proactive observability model minimized post-migration troubleshooting and made it easier to continuously optimize the platform.

These learnings demonstrate how modern cloud infrastructure – when implemented with automation, security, and business alignment in mind – can enable startups to scale with confidence while keeping control over complexity and cost.

Next Steps

Building on the success of the cloud migration and Kubernetes modernization initiative, the client is now well-positioned to enhance platform capabilities, extend operational impact across teams, and accelerate delivery of new services. The following next steps will ensure continued optimization, scalability, and business value:

  1. 1

    Expand Kubernetes Workloads Across Business Units

    With the EKS foundation in place, the client plans to onboard additional internal tools, services, and APIs to Kubernetes - enabling centralized operations and reducing the management burden of fragmented infrastructure.
  2. 2

    Enhance Cost Governance and Autoscaling Strategy

    The next iteration of infrastructure optimization will focus on deeper cost insights and smarter workload scheduling, including predictive autoscaling, fine-grained resource requests, and scheduled downscaling of non-critical services.
  3. 3

    Prepare for Multi-Region and Disaster Recovery Readiness

    As service usage scales beyond its initial regional footprint, the client will architect for multi-region availability - enabling failover, data replication, and high availability across zones with minimal downtime risk.

These next steps are aimed at unlocking the full strategic potential of the AWS ecosystem – allowing the client to scale faster, operate smarter, and innovate with confidence as it expands its services across new markets.

Conclusion

The implementation of Matoffo’s intelligent bill processing platform marked a transformative step in the client’s financial operations. By replacing fragmented, manual workflows with a scalable, AI-powered solution, the client not only resolved long-standing inefficiencies but also positioned itself for sustained growth and innovation.

The platform’s impact extends beyond operational improvements – it reinforces the organization’s commitment to accuracy, compliance, and technological leadership in a fast-evolving fintech landscape. With automated decision-making, seamless integration, and real-time visibility, the client now operates with greater agility, reduced overhead, and enhanced control.

Most importantly, this transformation opens the door to broader digital initiatives – from cross-department automation to future service offerings – laying a strong foundation for long-term competitive advantage in the financial technology space.

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