Databricks alternatives

3 tools to consider instead of Databricks, shown against it.

Databricks Apache Spark Starburst Trino
Vendor Databricks, Inc. Apache Software Foundation Starburst Data, Inc. Trino Software Foundation
Pricing model Usage-based Open source + paid options Usage-based Open source + paid options
Free tier Yes Yes Yes Yes
Deployment Cloud Self-hosted Cloud, Self-hosted Self-hosted
Open source No Yes (Apache-2.0) No Yes (Apache-2.0)
Best for Organizations wanting a single managed platform spanning data engineering, SQL analytics and machine learning on Spark. Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines. Enterprises needing governed, federated SQL access across many existing data systems with vendor support. Teams needing a single SQL query layer across data already spread across multiple systems.
Pricing

Pay-as-you-go pricing metered in Databricks Units (DBUs) per second, varying by workload type and tier, plus separate underlying cloud infrastructure costs; committed-use contracts offer discounts. A limited free Community Edition exists.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

Pricing has not been verified yet — see the vendor's site.

Free tier for small workloads; paid tiers bill by compute credit consumption at increasing per-credit rates as support and governance features expand.

Free $0
Pro From $0.50/credit
Enterprise From $0.75/credit
Mission-Critical From $1.00/credit

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Free and open source; commercial managed and enterprise-supported distributions are sold separately by Starburst.

Pricing has not been verified yet — see the vendor's site.

Features
  • Managed Apache Spark clusters with the Photon execution engine
  • Unity Catalog for governance and lineage
  • Delta Lake open table format
  • Notebook-based collaborative workspace
  • MLflow for experiment tracking and model deployment
  • Databricks SQL for warehouse-style BI workloads
  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats
  • Managed (Galaxy) and self-hosted (Enterprise) Trino distributions
  • Federated queries across lakes, warehouses and databases
  • Fine-grained access control and data catalog
  • Autoscaling cluster management
  • Query result caching
  • AI-assisted query and governance tooling (AIDA)
  • Federated SQL queries across heterogeneous data sources
  • Pluggable connector architecture (Iceberg, Hive, Kafka, JDBC sources)
  • Massively parallel, in-memory distributed execution
  • ANSI SQL compatibility
  • Cost-based query optimizer
  • Fine-grained access control via connectors

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