Case Studies | SaaS B2B

A phased, low-risk path from Azure to AWS

Incremental Workloads Migration Azure to AWS

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The challenge had three focus areas, each about moving a live, AI-heavy platform to AWS without losing capability or uptime.

Preserving AI capability across clouds

The platform depends on Azure AI Foundry and Cognitive Services models, along with three Azure ML workspaces, some of which have no exact AWS equivalent. The migration had to map these to Amazon Bedrock and Amazon SageMaker, weighing model equivalency carefully so that AI features behaved consistently after the move.

Migrating a containerised estate without disruption

The compute estate spanned Azure Kubernetes Service and Azure Container Apps hosting a gateway, auth services, and many microservices. These needed to move to Amazon EKS and Amazon ECS on Fargate in controlled increments, with service discovery, routing, and autoscaling validated before any production traffic shifted.

Moving data and messaging safely

A high-availability PostgreSQL database, an event-driven Service Bus backbone, and a self-hosted observability stack all had to move with zero data loss and minimal downtime, re-platformed onto Amazon RDS, Amazon SQS, SNS and EventBridge, and Amazon CloudWatch and X-Ray, with a rollback path retained throughout.

Solution

The migration was structured as a sequence of phases, each building on the last so that workloads could be validated and cut over in a controlled way, with a rollback path to Azure kept open until stability criteria were met.

5 phases

Incremental migration approach with validation and rollback at each stage

>99.9%

Uptime target across the 30-day production stability period

Zero

Data-loss target, validated by row-count matching across all tables

By the numbers:

  • 5 phases - Incremental migration approach with validation and rollback at each stage
  • >99.9% - Uptime target across the 30-day production stability period
  • Zero - Data-loss target, validated by row-count matching across all tables
Changes

The engagement gave the client a complete, validated blueprint for moving the platform to AWS, together with a defined set of acceptance tests spanning deployment, resilience, data integrity, CI/CD, and performance that the migration is measured against. Cloud Combinator delivered the architecture, mapping, and guidance; the client's team executes against it phase by phase.

  • Target-state AWS architecture — A high-level and logical design aligned to security, networking, and operational requirements and to AWS Well-Architected principles.
  • Service-by-service migration mapping — Every Azure component mapped to a recommended AWS pattern, including the AI model equivalency route through Amazon Bedrock and Amazon SageMaker.
  • A minimal-downtime data plan — Amazon RDS for PostgreSQL Multi-AZ with automated backups and point-in-time restore, migrated via AWS DMS with change data capture, targeting zero data loss at cutover.
  • Messaging and observability redesign — Service Bus mapped to Amazon SQS, SNS and EventBridge, and the self-hosted tracing and metrics stack mapped to CloudWatch, X-Ray and Amazon Managed Service for Prometheus.
  • Handover — Design reviews across networking, EKS and ECS, data migration, observability, identity, and CI/CD, with Cloud Combinator facilitating the AWS migration credits process.

With a phased plan, a rollback path, and a clear set of acceptance tests in place, the client can move the platform to AWS at a controlled pace, retiring Azure only once each workload has proven itself. Uptime, downtime, and data-loss figures above are acceptance targets defined for the migration rather than measured results, and will be confirmed as each phase completes.

AWS Stack

Amazon EKS

And Amazon ECS on Fargate for containerised compute, with Karpenter for autoscaling.

Amazon Bedrock

And Amazon SageMaker for the AI services layer and model hosting.

Amazon RDS

For PostgreSQL and AWS Database Migration Service for a Multi-AZ database with minimal-downtime migration.

YOU MIGHT LIKE

Related success stories

View all case studies

Case Studies | Insights

Utilising Language Recognition, Speed, and Enhanced Security to Make Social Media a Force for Good

  • Here, we take a detailed look at how the Cloud Combinator team collaborated with another cutting-edge AI service provider that provides intelligent systems to “make social media more social” for brands and users alike.
  • Arwen AI is a UK-based startup specialising in AI solutions to manage and enhance brands’ social media interactions. Founded in 2020 by Matt McGrory, Dr. David Cole, and Joel Bailey, Arwen. AI focuses on using AI to automatically detect and remove spam, toxic comments, and other unwanted content from social media platforms.
  • The team at Arwen have three core products. ‘Moderate’ is focused on identifying and removing toxic content from social media channels. ‘Engage’ helps brands identify and engage with meaningful conversations on social media, and ‘Customize’ allows brands to apply bespoke algorithms to their channels - creating an even more effective moderation and engagement.
Read more
CONTACT US

Ready to turn AI into impact?

We'll help you spot the highest-value opportunities, reduce risk around your first AI initiative, and define a clear path to results from day one.

Why talk to us:

Outcome-driven recommendations

AWS-recognised delivery expertise

Risk-aware AI adoption

Clear next step, not a sales pitch

Start with a focused 20-minute conversation about your goals — no pressure, no commitment.

We use cookies

This website uses cookies to enhance user experience and to analyze performance and traffic on our website.

See our Privacy Policy for details.