Skip to content
SPECIFICTION
Get listed

Databricks Feature Store

docs.databricks.com

Free trial WebAPI
Visit docs.databricks.com
Spec score 7.6 #4 of 20 in Feature Store Software

What it is

Databricks Feature Store is a central registry for features used in AI and machine-learning models. Registering features and models in Unity Catalog provides governance, lineage, point-in-time joins, and discovery and sharing across workspaces. Teams can create features as declarative Feature Views managed through Databricks pipelines or as feature tables populated in Delta tables; Feature Views and their materialization capability are in Public Preview.

Models trained with Feature Store features track lineage and retrieve the latest feature values at inference. Databricks says reusing training computations at inference avoids training/serving skew. Feature serving endpoints and online stores support real-time applications, with the overview describing millisecond latency. Supported third-party online stores include Amazon DynamoDB, Amazon Aurora (MySQL-compatible) and Amazon RDS MySQL, with availability depending on Feature Store mode. This paid cloud service has a free trial and usage-based monthly billing, with rates varying by product, cloud provider and region. It requires a Unity Catalog-enabled workspace; its APIs cannot be called from local or other non-Databricks environments, though local IDE development and unit testing are supported.

Spec sheet

Purpose
Databricks Feature Store is a central registry for the features used in AI and ML models.Read 30 Sep 2026 · docs.databricks.com
Integrations
Supported third-party online stores include Amazon DynamoDB, Amazon Aurora (MySQL-compatible), and Amazon RDS MySQL, with availability differing by Feature Store mode.Read 30 Sep 2026 · docs.databricks.com
Headquarters
San Francisco, California, United StatesRead 28 Sep 2026 · docs.databricks.com
Client limitation
Feature Engineering and Feature Store APIs cannot be called from local or other non-Databricks environments, although local IDE development and unit testing are supported.Read 30 Sep 2026 · docs.databricks.com
Feature authoring
Features can be authored as declarative Feature Views managed through Databricks pipelines or as feature tables populated by writing values to Delta tables.Read 30 Sep 2026 · docs.databricks.com
Feature Views status
Feature Views and their feature materialization capability are in Public Preview.Read 30 Sep 2026 · docs.databricks.com
Governance and discovery
Registering features and models in Unity Catalog provides governance, lineage, point-in-time joins, and cross-workspace feature sharing and discovery.Read 30 Sep 2026 · docs.databricks.com
Model training and inference
Models trained with Feature Store features automatically track feature lineage and look up the latest feature values at inference time.Read 30 Sep 2026 · docs.databricks.com
Pricing components
Materialization uses serverless compute, feature serving endpoints use the Model Serving SKU, and online stores use Lakebase compute priced by capacity units and replicas.Read 30 Sep 2026 · docs.databricks.com
Pricing model
Feature Store is billed at cost for underlying serverless compute, online store, and serving infrastructure, with no premium on top.Read 30 Sep 2026 · docs.databricks.com
Python client
The Feature Engineering Python client is distributed as databricks-feature-engineering on PyPI and is pre-installed in Databricks Runtime 13.3 LTS ML and above.Read 30 Sep 2026 · docs.databricks.com
Real-time serving
Feature serving endpoints and online stores support serving features for real-time applications, with the main overview describing endpoints as providing millisecond latency.Read 30 Sep 2026 · docs.databricks.com
Security and access
Access to Unity Catalog feature tables is managed through Unity Catalog access controls.Read 30 Sep 2026 · docs.databricks.com
Training and serving consistency
Databricks says using the same feature computations at inference as during training eliminates training/serving skew.Read 30 Sep 2026 · docs.databricks.com
Workspace requirement
The current Databricks Feature Store requires a workspace enabled for Unity Catalog.Read 30 Sep 2026 · docs.databricks.com

Plans and prices

Pay as you go Priced on request Monthly based on usage; per-second granularity. No up-front costs · Pay for products used · Rates vary by product, cloud provider, and region Read 30 Sep 2026 · databricks.com

Every Databricks Feature Store plan and price →

Strong points and limits

Strong points

  • Provides governance and lineage through Unity Catalog.
  • Supports point-in-time joins and cross-workspace discovery.
  • Tracks features used in model training.
  • Online stores support real-time applications.
  • A free trial is listed.

Limits

  • Requires a Unity Catalog-enabled workspace.
  • Feature Views are in Public Preview.
  • APIs cannot be called from non-Databricks environments.
  • Usage rates vary by product, cloud provider and region.

Who it suits

It suits teams building AI or machine-learning models that need governed feature registration, lineage and serving. Teams should already have, or be prepared to use, a workspace enabled for Unity Catalog.

Verdict

Databricks Feature Store connects feature governance and lineage with model training and online serving. Check the Unity Catalog requirement, preview status and usage-based billing details before adopting it.

Where Specifiction places it

Alternatives to Databricks Feature StoreAll alternatives →