I won’t go into detail in describing the differences and similarities of these platforms (at least not in this article), so I will let the picture speak for itself.
With this visualization, it’s easy to see that, depending on your needs in your deep learning development process, these individual platforms can save you a lot of time, headaches, and overhead costs. Being specialized platforms, the upside is that they tend to be very feature-rich and can support multiple scenarios. The downside is that they don’t help you with your complete end-to-end process, which is what you really need. The parts that the specialized platforms don’t cover either fall on you to sort out or you need to use more platforms until you have covered your end-to-end process, which can get expensive.
So what’s the alternative? You can naturally skip the platforms altogether and build a custom end-to-end pipeline yourself, which is going to be even more expensive and require tons of (expensive) experts to build and maintain. If you add on top that you probably want this pipeline to be robust, reliable, flexible, etc. it just means that it will be even trickier to build (essentially you would be trying to imitate the expertise and spending of the leading data companies of the world, i.e. Google, Amazon, Microsoft, Baidu, etc.)
Luckily there is a middle ground between these two extremes and that’s where the Peltarion Platform fits in.