# TechFabric Runway

> Runway takes the manual steps out of releasing Databricks Apps, for data teams who can build one but can't ship one after another without somebody doing it by hand. Each pull request gets a preview your reviewers can open, an app that fails its evaluation can't be promoted, and you can see who owns every app in the workspace and what it costs.

Role: Databricks App delivery
Databricks surfaces: Databricks Apps, Asset Bundles, Unity Catalog, Unity Gateway, MLflow
Status: Templates, delivery pipeline, and governance layer are implemented. A public read-only preview runs at preview.runway.techfabric.com.
Documentation: https://runway.techfabric.com
Canonical: https://www.techfabric.com/accelerators/runway

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## The problem

Building the first app is the easy part. The fourth one slows down because promotion is one person with the right access, the evaluation lives in a notebook somebody runs when they remember, and nobody can say what's running or what it costs.

## How it works

It runs inside your own Databricks workspace, with templates that start governed. A git push validates and deploys, approvals are recorded in the tool, and the evaluation gate is part of the release, so nobody has to remember to run it.

## What it changes for you

Your team ships Databricks Apps on a cadence instead of one at a time. Templates start governed, every pull request gets a preview environment, and a failed evaluation blocks promotion, so releasing gets boring in the way it should be.

## Questions

### Does Runway run in our workspace or yours?

Yours. The control plane is a Databricks App inside your own workspace. TechFabric-hosted is available as an option, but in-workspace is the default so your data never leaves your boundary.

### What does a quality gate actually block?

Promotion. An agent or model version only moves forward if its evaluation suite passes, and the gate is a policy checkpoint in the pipeline, and it holds when a release is late.

### Do we get preview environments?

Every pull request gets one, with per-environment secrets synced to secret scopes. The point is that a change gets an environment before it gets an opinion.