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From Lite to Pro

What stays the same and what changes when a DAG you ran with dexaflow lite moves to a Pro control plane.

A DAG you ran with dexaflow lite moves to a Pro control plane without a rewrite. The project is the same two files, dag.py and dexaflow.yaml, and the same workspace; what changes is where the image lives, who holds the connections and how the DAG starts. The steps are in Deploy your first Pro DAG; this page lists the differences so none of them surprises you.

What stays the same

The project. dag.py, dexaflow.yaml, the workspace file and the dag_id do not change, and python_version, dependencies and connectors mean the same thing on both.
The API and the UI. Both serve the Airflow-compatible /api/v2/ and the Airflow UI, so what you checked in Lite’s UI reads the same on Pro.
The CLI. dexaflow runs, dexaflow dags, dexaflow connections and the other server commands take --target <name> and work against either.

What changes

	Lite	Pro
Start	`dexaflow lite`, which watches the workspace and re-registers on save	`dexaflow deploy`, once per change, by hand or from CI (Redeploy on save with `--watch`)
Server	on your machine, chosen for you	a target you add once: `dexaflow target add prod <url> --registry <prefix>`
Sign-in	the admin `dexaflow setup` created (its email from the config file, `admin@dexaflow.local` by default); with no setup, Lite runs without sign-in, bound to this machine	`dexaflow auth login --target prod`: the cluster’s single sign-on, or a password
Image	built into the local cluster (none with `--executor=subprocess`), never pushed	built for the cluster’s platform (`linux/amd64` by default), pushed to your registry and registered by digest
Registry	none	one your cluster can pull from, with a credential for the CLI: a `gcloud`, `aws` or `az` session for Artifact Registry, ECR and ACR, `dexaflow registry login` for others, and the container tool’s own login when it builds (Registry credentials)
A new DAG	registered active, so its schedule runs from the first save	registered paused, as in Airflow, until someone unpauses it; `deploy --active` does it on the first deploy (New DAGs start paused)
Connections and variables	on the local server, seeded from `AIRFLOW_CONN_<ID>` in your environment when the DAG declares them	stored on the control plane: create them there before the first run, in the UI or with `dexaflow connections set --target prod` and `dexaflow variables set --target prod` (Variables & Connections)

Before the first deploy

Delete a Dockerfile that Lite wrote. The k3d mode of dexaflow lite writes a Dockerfile next to dag.py, FROM the leoflow-base image it builds on your machine, and leaves it there. A Dockerfile in the project is built verbatim by deploy, so dexaflow.yaml’s dependencies stop reaching the image, and its base exists on no other machine. Remove it, or see your own Dockerfile if you need one.
Create the connections the DAG uses on Pro. They are never copied from Lite or baked into the image. deploy prints note: this DAG expects connection(s): … for the ones the DAG declares.
Check the platform. A laptop on arm64 still builds linux/amd64 by default. For a cluster on arm64 nodes, set --platform linux/arm64 on dexaflow target add or build.platforms in dexaflow.yaml, or let the platform team set it in the cluster’s deploy defaults (Choosing the platform).

Then deploy, start the DAG and watch its first run:

dexaflow deploy <dag_id> --active --trigger --follow

Last modified October 18, 2026: [release-0.5] Pick 13 v0.5.5 changes (#2010) (ad942624)