There is no doubt that broadly deployed AI solutions that use deep learning and foundation models can deliver significant benefits to organizations. Used correctly, deep learning and foundation models enable organizations to accelerate time to market with products that better meet customer needs, enter new markets, meet compliance requirements in less time and lower costs, provide greater security, and numerous other benefits.
SambaNova Dataflow-as-a-ServiceTM already delivers production-ready, pre-trained foundation models which can be further adapted through unlimited fine-tuning or pre-training within an organization’s own environment to deliver world record accuracy. Delivered as-a-Service, this enables organizations to quickly and easily deploy state-of-the-art capabilities that are specifically optimized for their operations, while avoiding the massive complexity, financial investment and time necessary to fully train these models internally.
Yet investing in this technology for custom deep learning models, foundation models, and AI for Science in a meaningful way that will deliver broad organizational benefits, has been a challenging proposition for many. When investing in AI solutions at scale, such as for internally developed models or for custom AI for Science applications, organizations have traditionally had two choices. They could either make a significant upfront investment in infrastructure or they could subscribe to a cloud service from one of the major providers. While some organizations prefer this purchasing model, for many, neither of these options has been particularly palatable as they both come with significant costs and challenges.
Making an upfront investment in this technology involves significant initial costs. As with anything that involves a substantial capital equipment investment, there is a corresponding increase in purchase complexity. These costs are incurred far before, and in some cases years before, any benefit is realized from that initial investment. This is due to the time and complexity involved in building, training, and deploying a model and presents organizations with significant risks. These risks are from both the actual costs involved, as well as from the technology procured to power the model. Given the rapid advances of the state-of-the-art in AI technology, combined with an average of 18 months to build and train a custom model, it is possible that organizations could purchase infrastructure which is incapable of powering a state-of-the-art solution at the time it is deployed.
Choosing a cloud implementation could mitigate the associated risks of large scale initial investments, but it is not without its own set of challenges. Utilizing a cloud implementation introduces the potential for significant and unpredictable costs. These include not just the cost to utilize the computational resources to train a given model, which can be significant, but also the cost of ingesting the volumes of data that are required for that training. These are recurring costs that will increase over time as the models go through necessary re-training and fine tuning.
SambaNova has solved these challenges with a subscription based pricing model that:
By taking advantage of subscription pricing, organizations can achieve ROI faster, substantially reduce risk, and scale more cost effectively, all while benefiting from the SambaNova state-of-the-art hardware-software systems to run the most challenging deep learning, AI for Science, and foundation model workloads.
SambaNova offers customers flexibility and choice with subscription pricing for both Dataflow-as-a-Service, which includes pre-trained foundation models, and DataScale, which enables organizations to run their own deep learning, foundation models, and AI for Science workloads. For organizations that prefer to purchase systems, DataScale is also available as a CapEx model as well. Both products are available and shipping today, delivering customers rapid time to value.