Is testing with only 1000 user in AB testing, can give us a real data? or it will just be a waste of time?
Where Option A and Option B have markedly different response rates, 1000 is enough.
Popular A/B test platform Optimizely, explains it this way: https://help.optimizely.com/Analyze_Results/Statistical_significance_in_Optimizely
The big selling point of A/B test platforms is that they provide "statistically significant" results, but "statistically significant" is a relative concept. For example, Optimizely sets significance at 90%. In practice, where tested variations are minor, you may need 3000 to 5000 clicks on each variant to reach the 90% threshold and declare a winner.
BTW a 90% threshold is OK for marketing, but most scientific experiments set their mark at 95% or 98%.
Fortunately UX is not science. Most of our design debates can be settled with as few as five test subjects. See NNG's famous article on small-batch testing: https://www.nngroup.com/articles/how-many-test-users/