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DBD
Deploy models in hours, not days 1 substrate for dev & prod
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
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.
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.
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.
About DBD on Product Hunt
“Deploy models in hours, not days 1 substrate for dev & prod”
DBD was submitted on Product Hunt and earned 9 upvotes and 4 comments, placing #67 on the daily leaderboard. 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
DBD was featured in Artificial Intelligence (473.7k followers) and Data Science (3.9k followers) on Product Hunt. Together, these topics include over 107.8k products, making this a competitive space to launch in.
Who hunted DBD?
DBD was hunted by Ramit Surana. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Ramit Surana