Google Cloud Vision API vs. National Instruments Vision Builder AI

Mostly Google Cloud Vision API is getting used for extracting values out of pdfs which are scanned images. We also tried using it in analyzing different images and their marketing campaign results based on different features it contains like color, objects, positioning, logos etc. Vision API could be used for in conjunction with LLM to provide a more detailed prompt for image generation.

Incentivized

The perfect scenario is a production environment where items are passing in front of a camera on some type of conveyor system. Vision Builder AI can be set up to inspect those items and make decisions that can be handled down the line (rejection, sorting, etc.). It's great if you have an isolated vision system as part of a larger system that needs to just pass on results. It's not as well suited if you need that code tightly coupled with other code, such as vision-guided robotics.

Incentivized

It’s pretty easy to get started and the docs are good, plus it supports many features like OCR and image labeling. The accuracy is also generally good and it scale well for bigger use.
But the setup can be a bit confusing, especially with billing and permissions. and also the pricing is also not always very clear, and debugging issues sometimes take more time then expected.
Overall, it’s powerful tool but not the easiest for beginners.

Incentivized

Incentivized

It all comes down to tradeoffs. Do you want something that is extremely flexible and full-featured or do you want something that is easy and fast to use? Sure there are things that you can't do in Vision Builder AI that you can implement in LabVIEW, however, the vast majority of the applications that we work on do not need that extra functionality so Vision Builder AI is the best choice for us. If we find out along the way that Vision Builder AI is not going to work for us at least we have all of the steps and parameters figured out that we can use when moving to LabVIEW.

Incentivized