Databricks Feature Store

6.9easy start · #6 of 17
in Feature Store Software

Free to practise onnot on record
Free trialyes
Well documentedyes
Runs where you worknot on record

Runs on api, Web.

Databricks Feature Store is ranked #6 of 17 in feature store software on The Geeks Club. It runs on API, Web. A free trial is offered.

Databricks Feature Store plans and pricing

All plans

Pay as you go Not published Monthly based on usage; per-second granularity. No up-front costs · Pay for products used · Rates vary by product, cloud provider, and region databricks.com · 30 Sept 2026

Compared on feature store software

Online store: Yes
Offline store: Yes
Point-in-time joins: Yes
Feature monitoring: Yes
Deployment model: cloud
Serving modes: both

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The record

Purpose: Databricks Feature Store is a central registry for the features used in AI and ML models.
Governance and discovery: Registering features and models in Unity Catalog provides governance, lineage, point-in-time joins, and cross-workspace feature sharing and discovery.
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.
Feature Views status: Feature Views and their feature materialization capability are in Public Preview.
Model training and inference: Models trained with Feature Store features automatically track feature lineage and look up the latest feature values at inference time.
Training and serving consistency: Databricks says using the same feature computations at inference as during training eliminates training/serving skew.
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.
Integrations: Supported third-party online stores include Amazon DynamoDB, Amazon Aurora (MySQL-compatible), and Amazon RDS MySQL, with availability differing by Feature Store mode.
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.
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.
Workspace requirement: The current Databricks Feature Store requires a workspace enabled for Unity Catalog.
Security and access: Access to Unity Catalog feature tables is managed through Unity Catalog access controls.
Pricing model: Feature Store is billed at cost for underlying serverless compute, online store, and serving infrastructure, with no premium on top.
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.

Where it ranks on The Geeks Club

Best Feature Store Software in 2026#6 of 17

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Sources

docs.databricks.com/aws/en/machine-learning/feature-store· checked 30 Sept 2026
docs.databricks.com/aws/en/machine-learning/feature-store/c· checked 30 Sept 2026
docs.databricks.com/aws/en/machine-learning/feature-store/t· checked 30 Sept 2026
docs.databricks.com/aws/en/machine-learning/feature-store/p· checked 30 Sept 2026
docs.databricks.com/aws/en/machine-learning/feature-store/l· checked 30 Sept 2026
databricks.com/product/pricing· checked 30 Sept 2026