Case Studies | SaaS B2B

A clean move for a complex AI stack

GCP to AWS Cloud Migration

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.

Migrating a multi-service platform without losing data

Kubernetes microservices, a relational database, a vector store, a graph database, and ML workloads all had to move together, with zero data loss and minimal downtime. That demanded a careful inventory of every service, pipeline, and dependency before anything moved.

Re-platforming specialised data stores at equal or better performance

A vector store and a graph database do not have simple like-for-like equivalents. Each needed the right AWS target, correctly sized, so that search relevance and graph-query performance matched or beat the Google Cloud setup, addressing a previous concern that some queries ran too slowly.

Moving ML inference and embeddings, and making them faster

The client's inference models and embedding generation were central to the product. The migration had to re-home them on AWS while directly tackling the inference-latency issues the client had experienced, rather than reproducing them.

Solution

We sequenced the work so that nothing was built or moved before it was fully understood, and so each data store was validated against its source before cutover.

Zero

Data-loss goal across all migrated workloads

4-phase

Discovery, architecture design, build, and data migration

Multi-AZ

Amazon RDS PostgreSQL for high availability on AWS

By the numbers:

  • Zero - Data-loss goal across all migrated workloads
  • 4-phase - Discovery, architecture design, build, and data migration
  • Multi-AZ - Amazon RDS PostgreSQL for high availability on AWS
Changes

The engagement delivered a complete migration blueprint and AWS build for the client's platform, structured to move every workload with zero data loss and minimal downtime, and to validate each data store against its source before going live.

  • Whole-stack move — Kubernetes, relational, vector, graph, and ML workloads were mapped and migrated together onto managed AWS equivalents rather than piecemeal.
  • Performance addressed, not inherited — Neptune and the inference layer were sized to fix the graph-query and latency issues the client had on Google Cloud, rather than carrying them across.
  • Validated data integrity — PostgreSQL moved via DMS, and vector and graph stores were benchmarked and integrity-checked against source before cutover.
  • Production-ready foundation — A multi-account landing zone, blue-green CI/CD, and CloudWatch monitoring were built in from the start.
  • Handover — Included full infrastructure documentation, an infrastructure validation report, and operational monitoring dashboards.

With its AI platform consolidated on AWS and its known performance pain points designed out, the client is positioned to scale its services on a managed, well-architected foundation, no longer split across clouds but ready to support whatever the business takes on next.

AWS Stack

Amazon EKS

And Amazon ECR for running and managing the containerised microservices.

Amazon RDS

PostgreSQL, Multi-AZ, for the relational database with high availability.

Amazon OpenSearch Service

For the migrated vector store and semantic search.

Amazon Neptune

For the knowledge graph, sized for query performance.

Amazon Bedrock

And Amazon SageMaker for embeddings, notebooks, and ML workloads.

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