“Enterprise Software Sales” is not something I ever imagined spending as much time considering as I have over the past four years, but life is full of surprises isn’t it? At my previous gig we had changed our pricing model at least once during my time, and I learned quite a bit from the trade-off discussions which were had. Now sitting on the other side of the table, I get to enjoy a different perspective on the same underlying problem: how should enterprise software be priced? The question is important to answer, not just from a business perspective, but from a user perspective; the pricing model determines how your software will be adopted and used.

From the vendor’s side, there is an understandable desire to maximize the revenue potential from each sale, while minimizing the cost to acquire and maintain the account. From the customer’s side, there is an opposing desire to minimize the cost for each piece of software, minimize the overhead of dealing with the vendor, and maximize the utility and positive impact across the organization. Vendors can optimize their worldview by applying the same pricing model and scheme across their entire customer-base. What they can lose sight of however is that customers have to navigate and manage a complex maze of different bespoke pricing models, and somehow come to a realistic and defensible budget.

Any route has trade-offs, but I want to share my perspective on the user-behaviors different pricing models have within an organization. I will try to avoid too much naming-and-shaming with particularly counter-intuitive pricing.

Per Resource

The most common model which I have experienced is the “per resource” model, in which the vendor segments along some resource boundary and prices for N number of additional resources defined or used. I would consider the most egregious example of this pricing model from Oracle, charging per-cpu operating the software. Newer and less dastardly companies such as CloudBees and Datadog have applied similar variants of this approach. When I joined CloudBees, the flagship product at the time (“CloudBees Jenkins Platform”) was priced “per Jenkins master.” GitHub’s old pricing model followed a similar vein with per-private repository pricing. Datadog as another example, has an interesting blend of per-host and quantity-of-metrics pricing.

Depending on how the resource boundaries are defined, this is probably the most easily understood approach for both vendor and customer. We are very accustomed to applying value constructs to “finite” resources.

The most obvious consequence of this per resource pricing model are weird organizational optimizations, and in some cases, customers applying poor/counter-intuitive practices to their adoption of a piece of software. For CloudBees, we observed a number of customers would overload a single Jenkins master to avoid paying incurring the organizational pain of trying to upgrade their account/licenses. Many of those same customers would then pepper our talented support team with questions for which the answers were often: “stop putting everything on one Jenkins master!”

GitHub had similar challenges with per-repository pricing. Attaching a cost to private repositories led some organizations to try to put too many things in larger and unwieldy repositories. Or perhaps worse, some organizations I have encountered limited which projects internally were allowed to use GitHub due to cost. An ideal outcome for GitHub in an organization is most certainly widespread adoption across numerous teams and projects , all collaborating together through GitHub, but the chilling effect of the per resource model was quite real.

In both examples, I believe the pricing model encouraged bad user behavior and ultimately may have damaged adoption of the product.

A number of companies I have interacted with recently have a per resource model, and are either comfortable with, or blind to the adoption trade-off it forces their customers through.

Per user

The second common model I run into is the per user pricing model. Users for many vendors indicates a “login” to the service, or a dedicated per-person workspace. In some cases, user can be defined in rather confusing ways to try to hedge adoption and price, such as “monthly active users” which then requires the definition of “active” and “user!”

This is the current GitHub model, and in their implementation it makes quite a bit of sense to me. In order to bring value to a new user in your organization, you need to buy their ticket to the show. Makes sense.

The specific price point per user is where most vendors get themselves into trouble with this model. In the past I have been quoted per user prices ranging between $50/user per month all the way up to thousands of dollars per user, per month. Once a certain threshold is crossed, I will start to evaluate whether each user accessing the system is going to get that much value from the tool. If a user might only need to log into this system once a month for some additional insight, or to deploy some particular thing, is that one monthly possibility worth thousands of dollars? Probably not.

The resulting behavior I see from customers with this kind of model is that they contort the organization around the tool’s price gouging. Setting up a single team account, restricting access to people within certain parts of the org, or requiring budget approvals to head up to management to enable new users.

This organizational yoga can be an especially acute problem for highly specialized debugging or analysis tools. In the hands of our most senior engineers they might prove to be worth their weight in gold. But at thousands of dollars per user, I might be more reluctant to grant access to the more junior developers who don’t yet have the depth of knowledge to make effective use of the tool.

Is it better to have one specialized team in the company adopting the product, or would it be better if every team had access and saw the value of the given service or software?

Per Instance/Hour (Consumption)

My preferred model is the one adopted by all the major public cloud offerings: time-based consumption billing. While not universally applicable, consumption billing can be the easiest to understand. Your organization is going to use some amount of storage/compute/etc resources provided by the vendor, with some premium for the service overlaid on top of it. This obviously works much more effectively with Software-as-a-Service providers, but can be harder to implement.

Consumption-based billing makes it easy for customers to ramp up many different users and teams on the product, and then only those who are actually using it will incur costs. With a sane account structure in place, this model also makes it much easier for finance offices to see where the costs, and in turn the value, of any given tool is being realized.

For just about every vendor I’m alluding to in this post, consumption-based billing is within their reach, but many seem to actively avoid it. My cynical theory is because they want to pry as much money loose from their customers as possible, and are less concerned about the organizational anti-patterns their outmoded pricing models encourage.

Regardless of the pricing model chosen, if the value proposition is weak, prospects are less likely to convert and existing customers are more likely to push back against changes. With some of the vendors I have interacted with in recent years, it has taken willpower on my part not to laugh in their faces at some of the outrageous prices I have heard. “You think this pile is worth how much?” I personally have no objection to paying for software which demonstrates its value. I have paid GitHub for their outstanding product for years, and have actively encouraged the purchase of a number of other high quality/high value products at employers as well. The flip-side is that I have also encouraged cancelling of contracts which don’t show enough value for products which are poorly priced, poorly delivered, or poorly supported. The vendors which survive are those who have made it easy to adopt, recognize the value, and grow the adoption of their products inside an organization.