Databricks
Data + AI lakehouse platform unifying data engineering, analytics, and ML.
- Status
- Generally available
- Last checked
- Checked · Oct 11, 2026
- License
- Proprietary
- Pricing
- Free tier
What it is
Databricks is the lakehouse platform (built on Apache Spark/Delta Lake) unifying data engineering, warehousing, and machine learning at scale.
The lakehouse unifying large-scale data engineering and machine learning.
When it fits — and when it doesn’t
A good fit for
Large-scale data engineering + ML
Lakehouse for data and AI together
Spark/ML-heavy workloads
Unified platform at scale
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Complex and costly
Powerful but heavy; spend needs governance
- 02
Steeper learning curve
More to operate than a pure warehouse
What it does well
Unified data + AI lakehouse
Engineering, analytics, and ML on one platform
Scales to massive data
Spark-based processing for huge workloads
Strong ML/AI tooling
MLflow, notebooks, and model serving
Pricing
- Free tier
- YesFree trial / Community Edition
- Paid from
- —
- Model
- Usage-based
- Enterprise plan
- Yes
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Databricks
Setup steps, common gotchas and what to check after 30 days.
Get the badge
A small badge for Databricks’s README or site: its name, “Tracked on AI Navigator”, and the availability status and check month from this page. It updates as the catalog does.
Markdown
[](https://navigateaitools.com/explorer/tool/databricks)HTML
<a href="https://navigateaitools.com/explorer/tool/databricks"><img src="https://navigateaitools.com/badge/databricks.svg" alt="Databricks — tracked on AI Navigator" height="58"></a>