Why S3 Vector Search Is Powerful—and Dangerous | AWS vs VAST Data AI Strategy
Transcript
How important is it to avoid using a third-party database to get your data into your AI pipeline? It's huge. Quite frankly, skipping that extra hop, whether it's Pine Cone, Wev8, or whatever, means you can iterate way faster. You don't have to sync, transform, or orchestrate across systems. You just build. That's been a superpower of companies like Vast Data. They let you keep the data and compute tightly integrated so your AI workload stay close to the source. No shuffle, just speed.
And now Amazon brought that same play to S3. You can store your data in your embeddings right in S3 cury directly. No extra plumbing. It's frictionless. But here's the thing. Frictionless isn't the same as neutral because once you start building with S3 vector then layering on Bedrock then hosting models on EC2 you're not just streamlining your stack you're committing to AWS's way of doing AI. It is a superpower. Yeah, but it's also super sticky. So is it a big deal?
Absolutely. It's fast. It's powerful. It comes with gravity. Just like Vast, AWS gives you clean paths forward. But you better know which orbit you're lacking
