Artificial intelligence efforts, especially agent-based AI native applications, are shaking up the convergence of storage and compute.
Traditional online transaction processing workloads still require very low latency and typically rely on rowstore databases rather than object storage. But for agent workloads, the dominant constraint shifts from speed to storage scale and cost, according to Supabase Inc. CEO Paul Copplestone (pictured).
“What’s really important is [agentic workloads] Generates large amounts of data. “What you want is extremely low-cost data storage, and that’s where S3 comes in. What we’re seeing is a big trend of shifting database workloads onto S3 itself…which means you can get petabytes, or even hundreds of petabytes, of data,” Coplestone told theCUBE.
Coplestone spoke with AWS re:Invent’s John Furrier during an exclusive broadcast on theCUBE, SiliconANGLE Media’s live streaming studio. They discussed how the need to support both traditional OLTP and new agent AI-native applications is creating a demand for open and synchronized storage environments.
Migrate to AI-native applications and open storage architectures
As organizations scale generative and agent AI, infrastructure bottlenecks are emerging as a key constraint. Much of this friction is caused by how data is distributed across different storage systems. According to Coplestone, using both traditional databases and S3-based analytical storage, there is a growing demand for open and compatible storage formats that allow multiple engines to query the same data without continuous copying.
“When you no longer need the data in the database, you can delete it and it still remains in the data warehouse,” he said. “The queries you run are precisely synchronized between the database and the data warehouse.”
Amazon Web Services Inc. is betting that the next stage of enterprise AI will involve long-running agents across huge data sets, not just a single prompt. That’s why it’s important to have low-latency databases coupled with affordable, large-scale S3 storage. Coplestone explained that Supabase is positioning its new open PostgreSQL and S3 architecture as a way to do just that.
“The way we see it is that a lot of this kind of low-latency data can be stored in Postgres,” he said. “In that case, anything that can tolerate higher latency but requires high volumes is placed in S3. With those two primitives, you have a complete platform.”
Below is the full video interview, part of SiliconANGLE and theCUBE’s coverage of AWS re:Invent.
Photo: SiliconANGLE
Support our mission of keeping content open and free by joining the theCUBE community. Join theCUBE’s Alumni Trust Networka place where technology leaders connect, share intelligence, and create opportunities.
- over 15 million viewers of theCUBE videospowering conversations across AI, cloud, cybersecurity, and more
- 11.4k+ theCUBE Alumni — Connect with over 11,400 technology and business leaders who are shaping the future through our trusted, unique network.
About SiliconANGLE Media
Founded by technology visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digital media brands that reach more than 15 million elite technology professionals. Our new, proprietary theCUBE AI Video Cloud leverages theCUBEai.com neural networks to deliver breakthrough advances in audience interaction, helping technology companies make data-driven decisions and stay at the forefront of industry conversations.
