For a technology startup, selecting the technology stack is important. Startups operate under severe budget and time constraints. And they do not have the reputation to hire good developers. For large companies, it is easy to procure developers based on product requirements. But that is not the case with startups.
Technology stack possibilities
At a minimum, the startup might have a web application and mobile apps for iOS and Android. These apps connect to an API for data retrieval and update. Here are some of the options for each part of your product.
- Web application – React (JavaScript) or Angular (TypeScript) or something niche like Elm.
- iOS app – Swift (Xcode) or React Native or Native Script or Cordova
- Android app – Java (Android Studio) or React Native or Native Script or Cordova
- API – Node with Express (JavaScript) or ASP.NET (C#) or Ruby on Rails
- Database – MongoDB or RDBMS (MySQL, Postgres, etc)
For each option that you choose, there is a programming language. For each programming language, a dedicated developer should be hired.
Technology stack for established companies
Established companies might pick a technology stack like the following:
- Web application – Angular (TypeScript)
- iOS app – Swift (Xcode)
- Android app – Java (Android Studio)
- API – ASP.NET (C#)
- Database – MySQL (SQL)
Such companies may have the budget and reputation to hire five good developers. In the long run, the investment will pay off.
Technology stack for startups
For a start-up, it is difficult to hire developers. The technology stack that I advise (blindly) is using some sort of JavaScript.
- Web application – React (JavaScript)
- iOS app – React Native
- Android app – React Native
- API – Node with Express
- Database – MongoDB
The startup can hire a dedicated React front end engineer and a full-stack JavaScript engineer, who can act as a backup for each other.
Of course, there are areas like Search, Machine Learning that require investment in other areas. For Search, I recommend Elastic. For Machine Learning, I recommend Tensor Flow. But, things like Machine Learning can be implemented after Series A funding and not for the initial MVP.
