Hi, OpenAI community.
We built our “DevOps Copilot” on the OpenAI platform.
Overall, we’re happy with the results, except automating deployments was challenging because our Ops team wanted to use Terraform to configure / provision all our services, but we didn’t see an easy way to do this.
These are the problems we’re trying to solve are:
- OpenAI configurations cannot be diff’d, so you can’t easily track changes, versions, or rollback configurations.
- It’s all about the “latest” tag. There is no built-in versioning, snapshots, or disaster recovery plan.
- Piecemeal changes are the norm. With multiple people making tweaks, keeping track of what’s changed or reverting to a stable state is challenging.
- Moving from development to staging to production environments is manual and error-prone. Data scientists have to adjust model configurations manually, slowing down deployment.
Have you encountered similar issues?
We solved this problem using our own product (kind of meta, right? 
), a config data and templating solution.
If you are trying to find a better way to integrate OpenAI deployments into your workflow, message me, and I will send you a link to a technical blog post describing how the solution works.