Jeff Beard
ai

Financial modeling: iterating toward viability with Claude Code

· 2 min read

I kind of fell into using Claude Code for financial modeling. I asked for help with infrastructure architecture, but as soon as I asked for costing it changed everything, because of how easy and fast it was.

Important: all costs were verified by finding up-to-date pricing or talking to sales staff.

The first principle: super simple and cheap

I started with an arbitrary pricing idea for what I expected to be the most popular tier: $4.67 a month.

Then I asked Claude Code: "What's the least expensive way to deploy the app as a container on AWS and be profitable at this price point?"

The AWS constraint was purposeful, but more on Claude's infrastructure-as-code skills another time.

That single question kicked off a cycle of iterative financial modeling, since I always ask the AI to interview me for detailed context.

What "iterative" means

This wasn't spreadsheet development, though it's easy to generate one. This was totally conversational:

  • Multiple infrastructure options (App Runner, Fargate, ECS, EKS)
  • Evolving feature requirements (database, Internet access, vector storage)
  • LLM cost comparisons across providers (Bedrock, Claude, GPT, Ollama)
  • Pricing structure experiments
  • Break-even analysis for self-hosted vs managed LLMs

AI lacks a lot of context, so I had to prompt it to do research first:

  • AWS and LLM provider credits
  • Vector storage options, including S3
  • Bedrock managed LLMs vs LLM provider APIs
  • Networking and data transfer costs
  • Security implications
  • Third-party auth and data services

But once I pointed it in a direction, I could easily iterate: "What if we add database migrations as part of deployment?", "Cost impacts of public Internet access", or "Model the cost at N users using x, y, and z LLMs." It would regenerate the analysis in minutes, keeping me in a flow state and driving decisions, and the roadmap.

Key insight: markdown-driven development

Early on I didn't ask for markdown docs. Then I realized that markdown was simple long-term memory storage for the robot, and executable instructions for later.

Once I started requesting markdown artifacts for every analysis, the quality and reusability improved. Each financial model became a versioned, auditable document I could reference and iterate on.

Importantly, this creates a timeline of decisions and their justification.

In the end I had a roadmap and a model for profitable growth, including an attainable break-even point.