DBD
Deploy models in hours, not days 1 substrate for dev & prod
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DBD - Deploy models in hours, not days 1 substrate for dev & prod

by•
Most ML teams run dev on GCP, prod on AWS. Every release = manual migration, weights break CI/CD, dev/prod drift. DeployByDesign removes the gap. One managed substrate for both. Promote models by reference, not file pushes. Days → hours. ✓ Zero-setup workspaces (Jupyter, VS Code, RStudio) ✓ Drive/OneDrive sync ✓ Full audit trail ✓ Managed backend & DevOps ✓ Auto-stop + one flat invoice

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Maker
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We kept watching the same failure mode: teams build on GCP, deploy to AWS, and every release turns into days of manual hand-off with drift risk baked in. DBD collapses that by running dev and prod on the same managed substrate — promotion becomes a pointer swap, not a migration. Happy to talk through the phased rollout (landing → dev migration → artifact store → prod unification) if anyone's mid-way through something similar.

Promote by reference is genuinely a smart angle, the dev/prod weight drift problem has bitten our team before. Curious how the auto-stop billing actually works in practice across both clouds without weird surprises on the invoice.

Maker

Hi , thanks for the thoughtful question! The auto-stop billing is designed to ensure you're only charged while workloads are actively running, with consistent behavior across both clouds to help avoid unexpected costs. There are a few implementation details worth walking through, though. I'd be happy to give you a demo and answer all your questions—feel free to email us at .

The cross-cloud story is genuinely useful, but I'd love to see a built-in cost dashboard that breaks down spend per workspace per cloud. Teams lose hours in tagging reconciliation when someone forgets to stop a GPU instance on the other side, and a single pane that shows AWS and GCP charges side by side would make the one flat invoice promise actually trustworthy.