Databricks Review: Features, Pricing & Alternatives

What is Databricks?

Databricks is a cloud data and AI platform built on the lakehouse idea: one copy of data that serves data engineering, SQL analytics, machine learning and AI apps. It runs on AWS, Azure and Google Cloud. For analytics users the AI layer includes AI/BI dashboards, Genie One for asking data questions in plain English, and Agent Bricks for building and governing AI agents on company data.

Databricks, Inc. was founded in 2013 by the creators of Apache Spark and also created Delta Lake and MLflow. In June 2025 it launched a Free Edition for learning, which more than 500,000 people have used according to the company. At its June 2026 Data + AI Summit it expanded Agent Bricks, introduced Unity AI Gateway and added Lakebase, Agent Bricks and Genie Code to Free Edition. Genie was renamed Genie One in June 2026.

What makes Databricks different is breadth combined with governance. Unity Catalog applies the same permissions to tables, files, models and agents, so AI features work on governed data rather than exported copies. That power comes with complexity: pricing is usage-based in DBUs on top of your cloud bill, and getting value usually needs data engineers. It is a platform for organisations, not a quick analysis tool.

Key features of Databricks

• Genie OneAsk questions about governed company data in plain English and get answers, tables and charts; 2026 updates added documents, memory and web search in beta.
• AI/BI dashboardsBuild dashboards with AI help, local metric views and custom visualisations, shared with business users.
• Agent BricksBuild, evaluate and govern AI agents on your data, with a choice of frontier, open-source or custom models and MCP connections.
• Databricks SQLA serverless data warehouse for BI and SQL analytics directly on lakehouse tables.
• Unity Catalog and Unity AI GatewayCentral governance for data, models and AI traffic, with access controls, lineage, budgets and tracing.
• Lakeflow and LakebaseLakeflow handles data ingestion and pipelines, while Lakebase adds a serverless Postgres database for apps and agents.

Who is Databricks best for?

• Enterprise data teams who need one platform for pipelines, warehousing, machine learning and AI.
• Business analysts because Genie One and AI/BI dashboards let them query governed data without writing SQL.
• AI and ML engineers who want to build, serve and monitor models and agents close to the data.
• Students and learners who can practise on the Free Edition without paying.

Popular ways to use Databricks

• Self-serve analytics: let sales or finance teams ask Genie One questions about curated tables instead of filing data requests.
• Data warehousing: run BI dashboards and SQL queries on Databricks SQL without copying data to a separate warehouse.
• Agents on company data: build a support or research agent with Agent Bricks that respects existing permissions.
• Machine learning: train, track and serve forecasting or recommendation models with MLflow.
• Data pipelines: ingest and transform data from many sources with Lakeflow for downstream analytics.

Databricks pricing (October 2026)

Rates differ by product, cloud, region and tier, and cloud infrastructure costs are usually billed separately by AWS, Azure or Google Cloud. Use the official pricing calculator for estimates.

Databricks pros and cons

How to get started with Databricks

1. Sign up for Free Edition to learn, or start a free trial on your cloud.
2. Load sample or real data into Unity Catalog tables.
3. Query the data in the SQL editor and build a first AI/BI dashboard.
4. Create a Genie space on curated tables and test questions in plain English.
5. Set budgets and usage alerts before moving to production workloads.

Tips to get better results from Databricks

• Add clear table and column descriptions; Genie One answers improve with good metadata.
• Use serverless compute and auto-stop settings to avoid paying for idle clusters.
• Start Genie One on a small, well-defined dataset before exposing it to wider teams.
• Talk to sales about committed use discounts once your usage is predictable.

Databricks vs alternatives

Databricks’ closest rival is Snowflake Cortex AI, which also adds AI agents and natural-language analytics to a governed cloud data platform; Snowflake often feels simpler for SQL-first teams, while Databricks is stronger for engineering and machine learning. For BI on top of either, many organisations use Microsoft Power BI, Tableau or ThoughtSpot.

Smaller teams without a data platform will get answers faster from Hex or Julius AI. Databricks makes sense when data volume, governance and AI ambitions justify a full platform. See all best AI data analytics tools.

Our verdict: is Databricks worth it?

Databricks FAQs

Pricing and features change often. Details on this page were last reviewed in October 2026. Always confirm current plans on the official Databricks website.