architecture aws startups

Building for Scale from Day One

Foundry24 •

Most startups build their MVP with the mindset of “we’ll rewrite it later.” This approach has a hidden cost that compounds over time.

The Rewrite Tax

Every feature you build on a shaky foundation becomes technical debt. When your Series A comes in and you need to scale, you’re not just building new features—you’re rebuilding everything from scratch.

We’ve seen this pattern dozens of times:

  1. Month 1-6: Ship fast on Heroku with a simple setup
  2. Month 7-12: Start hitting scaling issues, patch with quick fixes
  3. Month 13-18: Realize the architecture can’t support your roadmap
  4. Month 19-24: Full rewrite while competitors ship features

Our Approach

At Foundry24, we build your MVP on the same AWS serverless architecture used by companies processing millions of requests per day. Here’s how:

Start with infrastructure, not code

Before writing a single line of application code, we define your infrastructure as code using AWS CDK. This means your staging and production environments are identical, deployments are repeatable, and there’s no “it works on my machine” surprises.

Design for 10x your current needs

We don’t build for today’s traffic—we build for next year’s. That doesn’t mean over-provisioning expensive servers. It means choosing patterns that scale automatically: serverless functions, managed databases, and event-driven architectures that handle 10 requests or 10,000 the same way.

Automate from day one

Every project ships with CI/CD pipelines that run tests, lint code, and deploy automatically. No manual deployments. No “deploy scripts” that only one person understands. Push to main, and your code is live in minutes.

Build observability in, not bolted on

Dashboards, alarms, and structured logging aren’t afterthoughts—they’re part of the initial build. When something breaks at 2am, you’ll know what and why without digging through raw logs.

Picking the Right Tools

We don’t default to Postgres because it’s familiar or Lambda because it’s trendy. Every technical decision starts with your access patterns and constraints.

Need sub-millisecond reads on a known key? DynamoDB. Complex queries across relationships? Aurora. High-throughput event processing? Kinesis. Caching layer to reduce database load? ElastiCache.

The same applies to compute. Lambda makes sense for spiky, event-driven workloads where you pay per invocation. Fargate fits better for steady-state services or long-running processes. Sometimes the answer is both.

The goal isn’t to use the “best” technology—it’s to use the right one for your specific problem, so you’re not fighting your infrastructure as you grow.

Get Started

Ready to build something that lasts? Reach out and let’s talk about your project.

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