Christopher Ren has an interesting article on the cost of using the Earth observation (EO) / remote sensing cloud computing platform du jour, Google Earth Engine (GEE). In it, he compares a GEE task with building, maintaining and running a custom EO analysis pipeline:
There is of course a final option: roll your own! Yes, writing GDAL is scary, wrangling AWS Batch/Lambda is scary, scaling compute and storage access is scary, and geospatial data engineers are expensive! However you may be surprised to find that under some conditions, this might be the most economical solution.
The pricing of GEE is quite intricate: base fee with included batch credits and online credits, Earth Engine Compute Units (EECU), and tiered usage fees as well as tiered pricing.1
From the article:
It’s interesting to note that except that even with these fairly generous assumptions: that my extrapolation is off by a factor of 4, that the custom pipeline takes double the amount of time to set up and maintain, the cost of running this process of GEE very quickly outstrips the cost of the custom pipeline. This tells us that GEE is fundamentally mis-priced (…). (…) GEE is expensive, if you use it as an imagery pipeline. Of course the magic of GEE is that you can do so, so much with it.
Footnotes
As Christopher Ren points out in his article, figuring out the price of an intended usage is by no means trivial.↩︎