Here's how you can control the implementation cost for your GenAI projects
If you do not want to send your data to OpenAI for privacy reasons, GPUs are rather hard to find these days in your data center, private cloud, or even your AWS or Google cloud accounts.
There is a bidding war on GPU prices and it can take a lot of time and effort to find GPUs that are cost-effective and available within the timelines of your project.
Not only could GPUs be expensive with your cloud provider(s), it may take a lot of people's cost/time to find them, and reserve them. The actual cost and lost time to an organization can be rather high.
You need an automated cost broker.
Meet DKubeX, your ops platform that can hunt for GPUs across many different clouds and cloud regions, and give you a list of them along with the costs associated to run your GenAI model.
You can then pick the most cost-effective cloud and cloud region with spot or reserved instance to schedule your GenAI models.
DKubeX acts as an automated cost broker, simplifying the process of finding cost-effective GPUs across multiple cloud providers, regions, and listing the associated costs. This allows you to select the most suitable cloud and region, whether you prefer spot or reserved instances, for scheduling your GenAI models.
DKubeX prioritizes data privacy by enabling you to keep your data within your own infrastructure or private cloud. It ensures your data remains under your control through encrypted pipelines, protecting sensitive information while still allowing you to utilize cloud resources efficiently.
There's a faster way to go from research to application. Find out how an MLOps workflow can benefit your teams.