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If you've worked with machine learning models on Databricks, you've probably encountered both approaches without realising they're fundamentally different things. You write a few lines in a notebook, call your model, get predictions — that works. But then someone says "let's serve this model" and suddenly there are endpoints, REST APIs, and deployment configurations involved.
So what's actually different? And when should you use one over the other? This blog breaks it down clearly — with real examples from Databricks notebooks.
Running a model in a notebook means loading the model directly into your notebook's Python session and calling it inline. The model lives in memory for the duration of the notebook run. When the session ends, so does the model.
This is what most data scientists do during experimentation — and it's completely appropriate for that stage.
Model: daIn this pattern, the model isn't "running" anywhere on your cluster. You're calling the Databricks Foundation Model API — a managed endpoint Databricks exposes for free-tier models like databricks-gpt-oss-20b. Your notebook calls it like any API call, gets a response, and moves on.
Important distinction
For custom models you've trained yourself, "running in a notebook" means loading the model weights into memory with mlflow.pyfunc.load_model() or similar. The model binary lives on the driver node for the session duration.
One issue we hit in production: the LLM was returning its full response object — including internal reasoning blocks — rather than clean text. The notebook call was working, but the output was wrong.
Model serving is a completely different concept. Instead of calling a model inline from your notebook, you deploy the model as a persistent REST API endpoint that lives outside your notebook session — and can be called by anything: notebooks, applications, dashboards, other services.
Databricks Model Serving creates a managed endpoint backed by auto-scaling compute. When a request comes in, it hits the endpoint. Your notebook session doesn't need to be running at all.
Registering and serving a custom model · MLflow
Notice how in Step 2, the notebook is just making an HTTP request. The model itself is running somewhere else entirely — on Databricks-managed serving infrastructure. The notebook could close and the endpoint would still be live.
Aspect | Running in Notebook | Model Serving |
Where model runs | Driver node / API call inline | Dedicated serving cluster |
Availability | Only while notebook is active | Always-on, independent of notebooks |
Access | Only from within the notebook | REST API — any app, tool, or service |
Scaling | Single cluster, manual | Auto-scales with traffic |
Best for | Experiments, batch jobs, dev | Production apps, real-time inference |
Cost model | Cluster DBUs while notebook runs | Per-token or per-request pricing |
Setup effort | Minimal — just call the model | Register model → configure endpoint → deploy |
Here's a real bug that hits almost every Databricks data engineer at some point — and it has nothing to do with model serving or running. It's about what happens when you mix PySpark and Python in the same notebook session.
When you do from pyspark.sql.functions import *, PySpark silently overwrites Python's built-in functions like round(), min(), max(), and sum(). PySpark's versions of these expect Column objects, not Python floats or integers.
BREAKS with AssertionError
Watch out
This affects round(), min(), max(), sum() — all standard Python builtins that PySpark also defines. The fix is always the same: import builtins and prefix any Python builtin call with builtins.
This one caught us in the Gold layer KPI computation. The original notebook was written assuming certain column names — injury, property, vehicle — but the actual Gold table had different names because the Bronze→Silver→Gold transformations renamed them.
fact_claims KPI · UNRESOLVED_COLUMN error
Best practice
Always run df.printSchema() or check your Unity Catalog table schema before writing aggregation logic. Column names change during transformations — don't assume they match your raw source files.
Use notebook execution when you're exploring data, building a pipeline, running a one-off batch job, or iterating on a model. It's fast to set up and easy to iterate on. The model doesn't need to outlive your session.
Use model serving when your model needs to be consumed by other systems — a web app, a dashboard, an automated pipeline that triggers independently, or any real-time use case. Serving is how you productionise a model.
In most real projects, you do both: you experiment and develop using notebook execution, then graduate to serving when it's time to deploy. Understanding the boundary between the two — and the gotchas in each — is what separates engineers who ship models from those who keep rerunning the same notebook.
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