[ampersandy]

Don't forget that Twitter has a comprehensive advertising platform. The partnerships, sales, tooling, and ad auction system themselves are likely just as complicated (or more so) than the basic Twitter product.

Instagram has only recently started to monetize the product -- it's almost unfair to compare the size of the engineering teams supporting the systems.

[johnymontana]

In addition to advertising and the engineering required around that Twitter has many data product offerings and has recently pivoted to end simple data products to partners (such as selling direct access to the firehose) to more complicated data analysis product offerings (consider the purchase of Gnip last year). Lots of engineering required around building these products.

[hugh4]

It still seems bizarre to need two thousand software engineers for something whose functionality is as simple as twitter.

[devonkim]

I believe the primary difference can be summarized in two words - data analytics. Twitter has a ton more analytics options for its users than WhatsApp appears to have mostly because, like Google, Twitter is more of an advertising platform than a utility platform as far as its actual business model goes. WhatsApp is potentially b2c as a business while I'd argue Twitter is really b2b. My experience with enterprise businesses makes it hard for me to believe that going b2b is actually efficient as much as it is about revenue assurance and scaling for sales / marketing culture organizations.

[cthulha]

The scale and reliability is the challenge. You could probably hack out a client and server on the weekend, but simulate 10 million concurrent users and every single part of your stack will break.

Also, I suspect that their strategic plan is not about minimising the number of engineers. They may want to be able to set up non-public projects which iterate for years or months before becoming visible: frameworks, app ideas, you name it. Their Fabric platform gives them excellent insights into what people are doing on mobile apps, for example. The kind of insights and understanding they have into personal and aggregate app usage is quite frightening when you realise just how much they have instrumented.