In my first blog in this series of six articles, I offered five reasons why open source will rule scientific computing. In this post, I discuss reason #5: Business Model. (Click through to see parts one, two, three, four and five of this discussion.)
It’s hard for me to believe that we are still talking about open source business models, particularly since Kitware has been profitable every year since its founding in 1998, and continues to grow at a 30% clip. But I get enough perplexed looks and questions that I can see people still don’t understand how we make a living. It’s simple really: Kitware provides services and solutions. Services may include the usual suspects such as documentation resources, training and support, but we also serve as co-creators of advanced technology, frequently taking the role as software engineering leads, and collaborating with our customers and partners to develop advanced research solutions or leading edge technological solutions. A very common business arrangement is for us to team with our customers, and here I am talking about big (and little) players in the pharma, medical device, simulation, and oil & gas businesses; or with academic research organizations, to tackle large projects that may run years in length and cost millions of dollars to execute. (BTW the book Wikinomics is a great resource to understanding open source business models.)
Having made a successful living for almost twelve years now, albeit (in the early years) with a chip on my shoulders trying to answer the embarrassing question “what is Kitware’s business model?” I have become a true believer in the Way of Open Source. In particular, in the way it benefits business and the public good. Beginning with authentic technologies based on Open Science, it’s easy to sell our advanced technical solutions combined with a low-cost business model built around our scalable, agile, flexible software development environment which supports inter-organizational collaboration to a customer. Our customers know that our technology is real because they can evaluate it; they recognize the importance of a quality-inducing software process; and they understand the importance of being able to collaborate in an iterative, community fashion to deliver solutions that actually meet their needs.
One curious business practice that our customers have pointed out to me recently is that many of the commercial simulation businesses have gained positions of semi-monopolies, and are actively ratcheting licensing costs to reflect their market power. Further, these vendors often do not provide licensing relief as the number of CPUs on computing hardware increases, resulting in burdensome software maintenance costs. Hence, many customers have told me confidentially, of course, that they are livid over vendor abuses and are actively seeking alternatives such as open source solutions. Typically, what we are seeing is that companies begin with open source as a cost savings measure, and once they gain experience with the collaborative, agile, and rapid technology development process that open source engenders they realize, to their delight I might add, that what began as a cost saving measure turns into an engine of rapid innovation and process improvement.
There is an important psychology to the business model at play here as well. In the purchase-license-based business model, companies pay up front for software solutions. Of course the vendors develop these solutions in a generic way to address a broad market; hence the software is never optimized for a company’s workflow, and generally requires significant, additional resources to integrate. Further, vendors respond slowly to the bugs and feature requests, as they must maintain them across the demands of the broader market. Under this current environment, the whole process of paying excessive licensing fees feels like profound betrayal to many of our customers since they pay up front to buy access to a technology, have to pay more to customize it (which the vendor owns), and then pay yet again as licensing fees ratchet up due to monopolistic practices or CPU additions. And as a last straw these same customers that were often instrumental in the success of the vendor end up trapped in a proprietary cage which is too expensive to leave. Ouch!
In comparison, an equivalent open source solution may cost as much (we pay Kitware staff well), but when the dust settles the customer ends up owning what it paid for, has shouldered the financial and technical integration burden over time, and has directed most of the expended funds towards integrating the technology into a customized workflow, yielding a superior end product. With the right licensing model (avoid GPL and reciprocal licenses), a company can choose to hold back proprietary features, or open source selected technology so that the maintenance burden is taken up by the open source community. Not only this, but these companies realize that they can change vendors, provide support themselves, and/or find alternative solutions for future development and maintenance—companies are no longer trapped by decisions made by an outside vendor.
Another important psychological aspect is the freedom that companies realize when they deploy their customized, open source-based technology solutions across their enterprise–the whole onerous process of negotiating IP is gone, and as a result open source software solutions have much wider impact and can be disseminated much faster.
Conclusion. I believe that within the next decade or two there will be an open source alternative to every proprietary software product, especially those aimed at scientific computing. Eventually the scalable nature of open source development will swamp anything any single vendor can do. Those that do not start collaborating with their community and customers, including providing open source solutions, will likely see severe business impacts even to the point of going out of business.
I’m sure that there are some that are skeptical of this claim. However, to me it seems clear that the needs of 1) Open Science, 2) authenticity, 3) agile, high-quality software process, 4) scalability and 5) collaborative, customer friendly business models will lead the way. In the end, I am confident that open source will rule scientific computing.