Case Studies | FinTech

Legacy to serverless, at a fraction of the cost

Platform Rearchitecture

About

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

Challenge

The engagement focused on four areas: reducing runaway infrastructure cost, modernising the database layer, improving performance and scalability, and embedding AI to lift data quality and insight.

Oversized, expensive infrastructure

The legacy platform ran on over-provisioned EC2-based compute, driving an AWS bill in excess of $20,000 per month. The client needed to eliminate that over-provisioning and move to a serverless model that only costs what it uses.

Ageing database layer

The existing database limited responsiveness and scalability. The platform needed a modern, serverless, PostgreSQL-compatible database with automatic scaling and high availability, along with schema modernisation and performance tuning.

Performance and extensibility

Latency and responsiveness needed to improve, and the architecture had to scale elastically with data volumes and user activity while remaining easy to extend with new features, rather than requiring another rebuild.

Manual effort and limited insight

Data entry involved significant manual work, and the platform did little to turn submitted data into insight. The client wanted AI embedded into the workflows to reduce that effort, improve data quality and generate actionable insights.

Solution

Users interact with a React front end hosted on AWS Amplify, which calls RESTful APIs exposed through Amazon API Gateway and implemented as Python AWS Lambda functions. Structured and unstructured submissions land in Amazon S3, while the modernised relational data is held in Amazon Aurora Serverless PostgreSQL, which scales automatically with demand and provides high availability. Route 53 handles secure DNS and routing, and CloudWatch provides logs, metrics, dashboards and alerts across the stack.

~$20K

Previous monthly AWS spend on the legacy EC2-based platform

~$5K

Projected monthly AWS run cost (MRR) for the re-architected serverless platform

~75%

Projected reduction in monthly AWS spend versus the legacy baseline

By the numbers:

  • ~$20K - Previous monthly AWS spend on the legacy EC2-based platform
  • ~$5K - Projected monthly AWS run cost (MRR) for the re-architected serverless platform
  • ~75% - Projected reduction in monthly AWS spend versus the legacy baseline
Changes

Acceptance is based on confirming that the success criteria have been met: that the client's platform has been re-architected to a modern AWS-native design, demonstrates improved performance and scalability, and achieves meaningful cost optimisation relative to the legacy environment, with final sign-off by the client's designated stakeholders. The new stack replaces oversized EC2 infrastructure with managed, serverless services throughout.

  • Serverless by default — Oversized EC2 compute is replaced with managed and serverless services including Lambda, Step Functions, Aurora Serverless and Amplify, so capacity and cost follow real demand.
  • Modern database layer — The platform now runs on Amazon Aurora Serverless PostgreSQL with automatic scaling, high availability, a modernised schema and performance tuning.
  • AI-assisted workflows — Amazon Bedrock is embedded into the workflows to reduce manual data entry, improve data quality and generate actionable insights from submitted datasets.
  • Observability and security — CloudWatch dashboards give visibility into API latency, workflow execution and error rates, with encryption in transit and at rest, IAM least-privilege access and centralised logging enforced throughout.
  • Handover — The platform is delivered with Infrastructure as Code, documentation, runbooks and a knowledge transfer session, supported through user acceptance testing to final sign-off.

With a fully serverless, AWS-native foundation in place, the client is positioned to scale with increasing data volumes and user activity without further re-architecture, to keep costs aligned

To usage, and to build on the embedded Bedrock capabilities as it expands the platform's AI-driven features.

AWS Stack

AWS Amplify

For hosting the React front end and integrating it with the backend APIs.

Amazon API Gateway

And AWS Lambda for the serverless RESTful API and Python processing logic.

Amazon Aurora Serverless

(PostgreSQL-compatible) for a modern, auto-scaling, highly available database layer.

AWS Step Functions

For orchestrating event-driven and long-running data processing workflows.

Amazon S3

For storing structured documents, unstructured files and vector embeddings.

Amazon Bedrock

For LLM-powered data extraction, summarisation and insight generation.

Amazon Route

53 and Amazon CloudWatch for secure DNS routing and end-to-end observability.

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