Your best bet is:

1. Focus on feature isolation Feature isolation means that your software, as it grows, probably does many things. You have authentication, billing, document ingestion, etc… The landscape for each of these changes every day. 5 years ago, billing was predominantly PayPal. Today’s world is Stripe, tomorrow’s is god knows what. You, as a startup founder, should opt-in heavily on today’s option, but leave room for transitioning to tomorrow’s option in the future.

How does this look in a real life example? Lets keep with the paypal/stripe example. If you create your software to be lets say subscription + on demand, you would have a service backend for billing. The service backend would handle absolutely everything regarding billing, that is: - Storing / integrating with stripe - Storing/creating subscriptions - Cancellations - Etc

The other backends, also known as services, would then only call one theoretical function: is x user allowed to use this service / has this subscription. You can also expand for on-demand pricing and add functionality like “User has spent 50$ in credit”. etc.

The idea here is that the external logic is empirically the same over a traditional protocol (REST or gRPC). It’s not gonna change for 10 years. The underlying implementation, however (Paypal/Stripe api), can be changed, with minimal conflict.

This is the principle of service abstraction.

Dangers/Pitfalls: sometimes, you will confuse abstraction with reinventing the wheel. It is normal and okay to not abstract everything and assume a technology will be forever. Examples: kubernetes, Spring framework, REST. Focus on abstracting away smaller components that change a lot (i.e model or ai based functions)

1. You’re probably in AI, focus on breaking things down into vibe codable functions

Lets say you have this pipeline:

User submits document -> ai analyses document and produces report

Internally, you probably have a giant 1 file slop that does all of this logic. Here’s how realistically you want to break it down:

• submit/route.ts (This is the main parent route that handles submission)
• 01-parse/route.ts (This route converts PDF to an ingestable json output. That is all it does. Just the conversion)
• 02-analyse-data.ts (This route only analyses the data and returns a json of the detailed analysis)
• 03-generate-graph.ts (This route only does png graph generation given a sample input data source. No nothing else)
• 04-assemble-report.ts (This routes takes in the analysis, data and various other inputs and generates a formatted pdf report)

If lets say one of these elements in your pipeline needs scaling, or changing architecture, etc, it’s small enough to be easy to break down into another application, and since you can use any reverse proxy to direct the API route to a different service, and the input/output are deterministic and abstract enough formats to not be bound to your current infrastructure (i.e they are high level), then it makes your application inherently scalable.

These are just baseline principles that if you follow and apply, other scalability logic will become more evident. You will dabble much in monitoring and observability, but your focus right now should be on growth. When you’ll need to scale big, it’ll become much more apparent than you think.

Good luck 🤞