Snowflake, Databricks, Teradata, Amazon Redshift, Google BigQuery or Microsoft Azure Synapse Analytics, to name the obvious contenders, are data warehouses whose value for building and running AI models is foundational. After all, AI models are applications that have to be housed someplace and must be queried to produce inferences.
But some might note that those differences are relatively inconsequential compared to the alternative of trying to build models and make inferences on a private enterprise data warehouse platform. The point many would argue is that building big generative AI models, for example, on a private data warehouse basis is arguably less reasonable than doing so using a cloud-based approach.
The ability to customize might be among the few areas where a private data warehouse might offer some advantages.
Different observers might evaluate performance and other aspects of each platform differently. Still, the basic capabilities of any data warehouse are functionally the same as required to support AI.
In some cases, relative strengths could be an advantage for artificial intelligence processing tasks, some might argue. But, as always, platform choices can turn on subtleties, including other choices a buyer already has made.
Such warehouses are crucial during the initial model training. Afterwards, experts say only some of the training data has to remain in the warehouse. But new data also is expected to be added over time, to update the model.
And of course the data warehouses must be used to house the model, once built. Data warehouses are essential for inference queries, addition of new data over time.
As a rule, some would say, large global enterprises, with vastly-larger amounts of data to use as part of the training, will be more costly than building models for mid-market firms with less-voluminous training mass. Small businesses with relatively limited amounts of data to parse will face smaller charges.
Most observers might tend to agree that training arguably will cost more for any entity, of any size, when conducted using private data resources, rather than engaging a cloud computing partner.
Building a model and training it are precisely the sorts of “one off” activities information technology professionals are advised to outsource, rather than doing themselves.
Small entity costs likely will fall over time as suppliers increasingly supply generic models, already trained, to the requirements of smaller entities. As always with any software, computing or application products, versions intended for small entities will not have the same robust features as provided to the largest enterprises, but will be far more affordable.
