This guide will eventually be a stand alone explanation for developers on using and producing Cloud Optimized GeoTIFF's. If you are interested in contributing then please make pull requests on the Github Project. In the meantime this document will just link to a few other resources
Reading a Cloud Optimized GeoTIFF
The easiest way to read a COG in a way that takes advantage of its unique properties is to use GDAL and its VSI Curl functionality. See the GDAL Wiki on Cloud Optimized GeoTIFF in its How to read it with GDAL section. A majority of geospatial software in the world uses GDAL as a library, so incorporating it is likely the fastest way to incorporate COG reading in to your software.
Planet produces all its data as COG, and has a little tutorial on how to download part of an image using VSI Curl. Most of the tutorial is specific to working with the Planet API, but it shows the use of GDAL Warp with a COG to extract a single farm field from a larger image.
How to generate a Cloud Optimized GeoTIFF
Also on the GDAL wiki page on COG is a section on How to generate it with GDAL. For quick reference the one liner is:
gdal_translate in.tif out.tif -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=DEFLATE
Many other geospatial software should also be able to add the proper overviews and tiles. Please contribute examples!
Testing your Cloud Optimized GeoTIFF's
The validate_cloud_optimized_geotiff.py script can be used to check that a (GeoTIFF) file follows the above described file structure
$ python validate_cloud_optimized_geotiff.py test.tif
$ python import validate_cloud_optimized_geotiff.py validate_cloud_optimized_geotiff.validate('test.tif')
See COG Performance Testing on GDAL wiki for some of the initial performance testing
Digital Globe has also done extensive performance testing with their IDAHO project, and found the format as performant as their previous custom scheme.