I rate the tool’s scalability a three or four out of ten. It is not scalable.

Scalability-wise, I rate the solution a six out of ten.

I don't have plans to increase the usage of the tool.

The scalability is very good. However, the best way to use it is to understand the underlying data science and to know the various techniques. 

I'm not sure how many people are using it in my organization at this time. It changes every once in a while. It's used to a moderate extent, however, it's not a solution that's for everyone. It may not be more than 20% of the staff as users have to have a certain level of expertise. 

The solution can scale well. 

It is a scalable solution.The scalability is maybe a problem now because I think within the last four years, IBM wasn't so excited to promote the software, and they just did brand maintenance and no new features were added. They are not doing anything to modernize the contracts but just updating it. I rate the scalability an eight out of ten.

The scalability of the IBM SPSS Modeler is great. It is a professional tool and can scale as much as your hardware will allow.

Scalability is good although we use a limited amount of data. It is not like millions of records and it is based on the speed of the computer, the personal computer itself. I think it can handle huge data. 

The product's scalability could be better. I rate its scalability a five out of ten.

I do not know, that's more on the developer side.

The scalability has been pretty good so far. 

It scales. I have not run into any challenges where it will not perform.

It's the same situation as the stability.

The server is not cheap and not scalable enough.

SPSS Modeler should meet our needs going forward. It is very scalable for non-technical people. The challenge for the very technical data scientists: It is constraining for them.

C&DS will not meet our scalability needs.

It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it, market it. Even in terms of processing, it’s easier.

I have no issues with scalability. It's pretty scalable. It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler. 

One of the things that I have not done in Modeler, and I'm not sure if the capability is there, is to run things in parallel. I'm pretty sure they have it but I haven't used it.

The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool.

Scalability is infinite, because it can just spit out straight to our enterprise data warehouse, and we can use that to deploy anywhere.

We haven't had any performance problems. The product runs every data volume performantly and produces results.

I don't use it for any high performance applications.

It is hard to define at this stage.

The scalability is okay but has some limitations.

I think that it is pretty scalable, based on what I have heard from the team. However, we don't really have a need for that. We have two main uses who take on different directions of research and insight into different areas of study.

A small enterprise might not need this product because the cost for the renews of the license is pretty high. Because of this, smaller organizations would be better off with an open-source or free online tool to do their work. For large organizations, because you need stability and you need the reliability of data, I think this product is definitely required.

It is pretty scalable because you can have an SPSS server that we can work to offload, and it seems like we could deploy it to many people if we had the money. It is a little bit costly, but that is with any product like this. Compared to SAS, FICO, or any of their competitors, I think it is comparable.

It will scale up to anything we need.

The work can be easily scaled even without additional components offered by IBM, but it really depends on each organization. Some research is necessary in order to understand how to bypass those components, but in the end a substantial amount of money would be saved. IBM provides documentation regarding each component that SPSS Modeler could interact with.

It is pretty scalable.

The scalability is good.

It was scalable.

Server installation was too hard and ineffective. You need at least four to five concurrent users with huge data to benefit from a server, but the salespeople never tell you that and just try to oversell.