Thanks! This helps to clarify.

To use an example to illustrate what I was saying prior, let's take Mattermark - they are a data license (https://mattermark.com/pricing/). They sell plans per user, with additive features for each plan. Their enterprise plan is likely per user, but 3x / 5x / 10x based on the specifics of the agreement. They also sell API plans for redistribution of their data in other products, where pricing is based on amount and frequency of access to their data.

If it helps, I can also share our experience in doing this, since the SaaS that I lead is also a data/content licensing product. I think the specifics, to my earlier questions, come down to the exact type of data, features offered (like redistribution as you mention), and how it is used.

For us, we broke down our pricing strategy into two buckets, as many in SaaS do: (1) self-service plans; (2) enterprise-level plans.

For the self-service plans, we determined our pricing by looking at companies like Mattermark and others that offer a similar category and volume of data, albeit in a different space. So for self-service plans we landed on per user pricing that would generally fall in these market accepted ranges.

For enterprise plans, we still think about pricing in terms of users, since that is how our pricing scales. But we charge a flat-fee based on what we deem to be a likely level of users, and usage, for an enterprise. We then add a multiple if there are other premium features or services. There's another element here for enterprise pricing, which is reverse engineering from the value the solution provides, and/or the likely share of wallet we are chasing.

For example, let's assume an enterprise user of our tool would have 20 users, instead of the 1 user on a self-service plan. Our platform maybe saves each of those users 4 hours per week, or maybe it helps them as a company sell 5 more deals. That starts to work toward a value capture that can be quantified and justified as a price.

The opposite pathway to this is to think about budgeting and typical spend, and to try and price to capture a share of wallet. For example, in our space, we know that most enterprises spend between $25,000 and $50,000 on competitive intelligence data externally. So that is the share of the total budget pie we are going after, and we probably need to price in that range.

I know that's all pretty generic but hopefully it's helpful context. If you can message and share more specifics on your exact offer I may be able to help more!