Compare
MLGatee vs.
MLGatee vs.
running it yourself.
The same model, two paths to production. Here is what changes.
Do it yourself
A server, Docker, a key store, a dashboard.
Plus loading pickles you didn’t write on a machine you care about.
With MLGatee
One upload. Or one line.
mlgatee.deploy(model, name="iris")Inspection, an isolated service, a key, versions and monitoring, handled for you.
| Area | Do it yourself | MLGatee |
|---|---|---|
| Packaging | Write and maintain a Dockerfile | Nothing to write: each model gets its own service |
| Packages | Match library versions by hand | Exact versions read from the file itself |
| Untrusted files | Load pickles on your own server | Never loaded on our servers; unsafe files refused |
| Endpoint | Set up HTTPS and an auth layer | Key-protected HTTPS from the first deploy |
| Keys | Store and rotate secrets yourself | Shown once, stored as a hash, new key any time |
| Updates | Redeploy and hope nothing breaks | Next version at the same address; the old one answers during the build |
| Rollback | Rebuild an old artifact | One click to any of your three newest versions |
| Monitoring | Wire up separate tools | Calls, errors and answer times per version |
| From a notebook | Export, copy and deploy by hand | mlgatee.deploy(model, name="iris") |
| First answer | Hours to days of setup | Typically 1.5 to 4 minutes |
Typical MLGatee deploy time measured from upload to a live endpoint. Setup time for a do-it-yourself stack varies by team.
Get started
Your model deserves
Your model deserves
to see the real world.
Upload a file or deploy from your notebook. MLGatee handles the packages, the server and the key.
Free plan: one model, no card needed