AWS Retail Türkiye

Migros One

Application Refactoring

On the journey of growing its business and optimizing operations, Migros One wanted its small engineering team to focus on accelerating innovation and delivering customer value quickly rather than on operations. The company sought to speed up scaling, adopt new technologies, and deploy services to minimize the bureaucratic processes that constrained its on-premises environment.

1 million+
10x
For instance, since July 2024, it has used AWS Enterprise Support , which provides a comprehensive suite of resources.
We used automated testing and validation to reduce our 2-week deployment cycle for services to minutes.

Reported impact

Business outcome

By accelerating innovation, Migros One can now better meet customer needs. The company recently developed a feature called “What should I cook?” using Amazon SageMaker , which lets businesses build, train, and deploy machine learning models. Key reported metrics include traffic handled with virtually seamless scaling 10x; 10x; reduction in costs 20%.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

Learn how online food retailer Migros One accelerated growth and innovation and reduced operational overhead by migrating to AWS. To grow its business, online food retailer Migros One needed modern and scalable infrastructure without the operational overhead of managing it. The company chose to migrate to Amazon Web Services (AWS) so that it could focus on innovation while AWS managed the infrastructure. Migros One’s infrastructure continues to scale seamlessly, as it did during COVID-19, when its traffic increased by 10 times. The company has also optimized costs as compared with its on-premises setup because it no longer needs expensive infrastructure maintenance. After the migration, Migros One modernized its architecture by using microservices. Migros One’s microservices architecture is deployed on Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed Kubernetes service.

Data flow

Source data is ingested through AWS Glue, stored or indexed in Amazon RDS, processed with Amazon EKS, Amazon Bedrock, Amazon EC2 Spot Instances, coordinated by Amazon SQS, and delivered through Amazon Bedrock Knowledge Bases.

Solution architecture

7 components · 5 layers
  1. Ingest
    • AWS Glue Moves and transforms source data into analytics-ready formats.
  2. Compute
    • Amazon EKS Runs containerized workloads and Kubernetes-based application services.
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Amazon EC2 Spot Instances Provides interruptible compute capacity for lower-cost scalable workloads.
  3. Storage
    • Amazon RDS Stores structured application or analytical data in a managed relational database.
  4. Serving
    • Amazon Bedrock Knowledge Bases Connects enterprise content to retrieval-augmented generation workflows for grounded answers.
  5. Orchestration
    • Amazon SQS Buffers asynchronous jobs between distributed services.

Architecture clues

  • After the migration, Migros One modernized its architecture by using microservices.
  • It uses Amazon Simple Queue Service (Amazon SQS)—fully managed message queuing for microservices, distributed systems, and serverless applications.
  • Migrating to a microservices architecture also helped Migros One develop more services in less time.
  • Migros One’s microservices architecture is deployed on Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed Kubernetes service.

Evidence from the source

1 million+
10x
After the migration, Migros One modernized its architecture by using microservices.
For instance, since July 2024, it has used AWS Enterprise Support , which provides a comprehensive suite of resources.
On the journey of growing its business and optimizing operations, Migros One wanted its small engineering team to focus on accelerating innovation and delivering customer value quickly rather than on operations. The company sought to speed
We used automated testing and validation to reduce our 2-week deployment cycle for services to minutes.
Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Migros One customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/migros-case-study/
    Source type
    Vendor-published customer story
    Published
    Feb 28, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.