vsreekanti

I completely agree — open-source models and custom deployments just can't compete with the cost and efficiency here. The only exception here is if open-source models can get way smaller and faster than they are now while maintaining existing quality. That will make private deployments and custom fine-tuning way more likely.

Probably some combination of all the above! I think 1 and 2 are interlinked though — the cheaper they can be, the more they build that moat. They might be eating the cost on these APIs too, but unlike the Uber/Lyft war, it'll be way stickier.

Yep, we agree that the obvious direction of innovation for OSS models is smaller and cheaper, likely at roughly the same quality: https://generatingconversation.substack.com/p/open-source-ll...

Hi everyone!

We've been working on making data teams more productive with Aqueduct for over a year, and we're really excited to share what we've been building.

There's a large (and growing!) number of programmers in the world who understand data and can solve business problems but don't want to spend their time wrangling low-level cloud infrastructure to get their work into the cloud. The existing MLOps tools that claim to solve this problem have been built by & for software teams, and they're incredibly complicated.

With Aqueduct, we've built a tool that's designed for data teams and abstracts away the underlying infrastructure. Aqueduct has a simple Python API that allows you to define a workflow as a composition of Python functions. Those workflows can be easily connected to data sources and can be run anywhere from your laptop to a Kubernetes cluster in the cloud. Once a workflow's running, Aqueduct has lightweight hooks to compute metrics and run tests over your pipelines to ensure they're producing high-quality results.

To learn more about what we're building, check out our GitHub repo or join our community Slack:

https://github.com/aqueducthq/aqueduct https://slack.aqueducthq.com