Case Studies | SaaS B2B

Building the AWS foundation for the client's 100x growth

Migration and Scaling on AWS

About

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

Challenge

The challenge had four focus areas, each driven by the platform's per-user isolation model and its planned growth curve.

Elastic scale for 100x growth

The client's roadmap moves from around 100 users today to a target of roughly 100,000 within twelve months, with peak concurrency projected to rise from tens to tens of thousands. Because every active user drives around 1,500 API requests a day across IDE

Multi-tenant isolation

Each user project runs as its own Node.js process with a dedicated PostgreSQL schema and its own deploy pipeline. The architecture had to host many isolated tenant environments side by side without letting one tenant's workload or data affect another.

AI code generation at scale

The AI engine that generates complete applications is central to the product. The platform needed a managed, scalable path for large-language-model calls that could grow with usage and reduce dependence on a single external model provider.

Enterprise-grade security and reliability

To win enterprise pilots, the client needed private networking, web-application protection, managed secrets and highly available databases, the controls a single VPS simply could not provide.

Solution

On AWS, the client's shared platform services run on ECS Fargate behind CloudFront, while the isolated per-user project environments run on EC2 with Auto Scaling so capacity follows demand. An user describes an application, the AI engine generates the full stack, and the user reviews each decision before the project is provisioned and deployed into its own environment.

100K

Targeted platform users within 12 months (projected)

~$400/mo

Previous self-hosted VPS spend, now replaced by AWS

Multi-AZ

RDS PostgreSQL for platform-database resilience

By the numbers:

  • 100K - Targeted platform users within 12 months (projected)
  • ~$400/mo - Previous self-hosted VPS spend, now replaced by AWS
  • Multi-AZ - RDS PostgreSQL for platform-database resilience
Changes

The engagement gives the client a cloud-native AWS foundation designed for its per-user isolation model and its growth targets, replacing a single self-hosted VPS with an auto-scaling, Multi-AZ architecture that is ready for public launch and enterprise pilots. The build also brings AI code generation onto managed AWS services and adds the security controls enterprise customers expect. The growth and concurrency figures below are the client's own targets and are projected.

  • Elastic scale — ECS Fargate for platform services and EC2 Auto Scaling for user projects let capacity track demand rather than a fixed ceiling.
  • Tenant isolation — Per-tenant PostgreSQL schemas and isolated project environments keep users separated as the platform fills up.
  • Managed AI generation — Amazon Bedrock provides a scalable path for the large-language-model calls behind the code-generation engine.
  • Enterprise security — WAF, Shield, private VPC subnets and Secrets Manager provide the protection and secret handling enterprise pilots require.
  • Handover — The client moves off a single VPS onto a version-controlled, observable AWS platform it can operate and extend as it grows.

With the foundation in place, the client is positioned to move through public launch, paid acquisition and enterprise pilots on infrastructure that scales with each new cohort of users, no longer constrained by self-hosted limits but ready for whatever growth comes next.

AWS Stack

Amazon ECS

On AWS Fargate for serverless hosting of the shared platform services.

Amazon EC2

With Auto Scaling for isolated per-user project environments that scale with demand.

Amazon RDS

For PostgreSQL (Multi-AZ) for a resilient platform database with per-tenant schemas.

Amazon Bedrock

For managed large-language-model access behind the AI code-generation engine.

Amazon S3

And Amazon CloudFront for asset storage and global content delivery.

Amazon SQS

And Amazon SNS for the build queue, inter-service events and notifications.

Amazon CloudWatch

And AWS X-Ray for logging, metrics and distributed tracing.

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