Case Studies | PropTech

Breaking the single-host bottleneck

AWS Modernisation

About

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

Challenge

The challenge had four focus areas, each stemming from the concentration of the whole platform on a single instance.

A single-instance bottleneck

Six containers, the database, VPN, certificate renewal and all scan storage lived on one EC2 host. This concentrated operational risk on a single point of failure and prevented the web, CPU, GPU and ROS2 workloads from scaling independently.

Local storage as the source of truth

The EBS volume, not S3, was authoritative for scan data, and the daily sync to S3 was only a backup. The disk had grown to 3.3 TB, and because all processing code assumed local filesystem access, the design could not support stateless, horizontally scalable workers.

Inefficient GPU processing

A single always-on GPU instance polled the portal every 10 seconds over SSH, processed one job at a time, and copied files back and forth, leaving expensive GPU hardware idle around 95 percent of the time with no way to scale to demand.

No resilience or clear operational boundaries

With no autoscaling, no multi-AZ deployment and host-level tooling for VPN, certificates, secrets and logging, the platform lacked the availability, security control points and observability expected of a production system.

Solution

Rather than move the whole platform at once, Cloud Combinator built a repeatable AWS staging foundation as infrastructure as code and introduced services one at a time, validating each before the next, so migration risk stayed low and rollback paths remained open throughout.

3.3 TB

Scan data moved off single-host EBS to S3 as the source of truth

95%

GPU idle time targeted for elimination via AWS Batch scale-to-zero

2+

Availability Zones for a multi-AZ, resilient design

By the numbers:

  • 3.3 TB - Scan data moved off single-host EBS to S3 as the source of truth
  • 95% - GPU idle time targeted for elimination via AWS Batch scale-to-zero
  • 2+ - Availability Zones for a multi-AZ, resilient design
Changes

The engagement is measured against clear success criteria: the target architecture removes the single-instance bottleneck, matches AWS services to workload type rather than selecting them generically, supports phased validation and rollback instead of a high-risk cutover, and delivers clearer operations, scaling boundaries and security control points than the current state. The figures below capture the shift the modernisation delivers.

  • The bottleneck removedWeb, data and processing tiers are separated across a multi-AZ VPC, so the platform no longer depends on a single host and each workload scales on its own terms.
  • S3 as the source of truthScan data moves off the portal's EBS volume to S3, unlocking stateless workers, horizontal scaling and stronger backup and recovery, with the daily sync retired only once S3 is authoritative.
  • Efficient, queue-driven processingCPU, GPU and ROS2 workloads run as decoupled workers fed by SQS and AWS Batch, replacing the polling GPU instance and letting GPU capacity scale to zero when idle.
  • Resilience and operational clarityMulti-AZ RDS, autoscaling, centralised CloudWatch logging and alarms, and AWS-native secrets and certificate management replace host-level tooling and single points of failure.
  • HandoverA target-state architecture narrative, a service-by-service AWS mapping and a phased migration plan are delivered, with the legacy device path preserved until replacement flows are validated.

With a validated, infrastructure-as-code foundation and a proven service-by-service migration path, the client can move from a fragile single-host platform to a scalable, resilient AWS architecture, and expand its device fleet without the constraints of the original design.

AWS Stack

Amazon ECS

Behind an Application Load Balancer for the containerised web, API and worker services.

AWS Batch

For GPU-accelerated processing with scale-to-zero when the queue is empty.

Amazon SQS

For decoupled, queue-driven CPU and ROS2 worker processing with dead-letter queues.

Amazon S3

As the authoritative storage layer, with pre-signed URLs and Transfer Acceleration for device uploads and downloads.

Amazon RDS

For PostgreSQL and Amazon ElastiCache for Redis for managed data and caching.

AWS CloudFormation

For the repeatable, environment-parameterised infrastructure-as-code foundation.

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