People often ask me whether mojoPortal CMS can handle x amount of traffic. Unfortunately, this is not an easy question to answer and anyone who gives you easy answers to this kind of question probably should not be believed. In general, you don't need to worry about capacity planning for your mojoPortal web site unless you are expecting really huge amounts of traffic. Generally even if you are headed for that level of success it won't happen over night and you can gradually scale up from shared hosting to a dedicated server. A sufficiently beefy single server should be able to handle quite a lot of traffic. The next step would be to move the database to a dedicated server. Usually the database server will be a bottle neck before the web server so you may need a database cluster before you need a web cluster. Beyond that you can look at scale out strategies like web farms. We have plans for an add on product to facilitate use of mojoPortal in web farm environments targeted for release in the first or second quarter of 2009.

mojoPortal content management system is designed to perform well and to make efficient use of available resources.

I do load testing on a regular basis between each major release to make sure I'm not losing performance over time, though my testing is not exhaustive of all features and all scenarios.

I've also done load testing comparisons of mojoPortal vs other .NET apps and mojoPortal compares favorably in my testing.

As far as specific numbers of user and requests of course that depends on hardware. Capacity planning requires a lot of effort. There is a very good article "How To Perform Capacity Planning" which outlines the steps involved with coming up with hard number predictions. As you can see its a lot of work to really do it right for all features. I have not found time to do all of that, so I don't make any bold claims.

What I generally do is periodically dedicate some time to performance optimising, which I do using Red Gate Ants Profiler in conjunction with load testing. Red Gate helps find code that is slow and should be optimised and load testing verifies if the change had any impact.