Databricks vs Trino

Updated November 2026 · AppBreakup editorial

Databricks

A lakehouse platform combining data engineering, SQL warehousing and machine learning on your cloud.

Paid

Visit Databricks Databricks profile

Trino

Open-source distributed SQL engine for querying data lakes and many databases in one query.

Open sourceSelf-hostable

Visit Trino Trino profile

The short answer

Databricks is a managed lakehouse platform covering Spark data engineering, SQL warehousing and machine learning, billed in DBUs on top of your cloud costs. Trino is a free, open-source SQL engine you run yourself to query data lakes and many databases in place. Pick Databricks for one platform spanning ETL to ML; pick Trino for federated SQL across systems without vendor fees.

Databricks or Trino: our take

Both tools let you run SQL over data stored in open formats in object storage, but they sit at different scopes. Databricks is a full commercial platform: Spark-based data engineering, Databricks SQL warehouses for BI, collaborative notebooks, MLflow for machine learning and Unity Catalog for governance, running on AWS, Azure or Google Cloud. Trino is just the query engine. It stores nothing itself and reads data where it lives, from S3 or HDFS tables in Hive, Iceberg or Delta Lake format to relational databases, Cassandra and Kafka, and a single ANSI SQL query can join across several of those systems.

That federation is Trino's main draw. If your data is spread across a lake and several operational databases and you want to query it without copying it into a warehouse, Trino is built for that, and it works with BI tools like Tableau, Power BI and Superset. Databricks is centred on its lakehouse: it shines when you want ingestion, transformation, SQL and model training in one governed place, rather than querying many outside systems.

The cost models are opposite. Trino is free under the Apache 2.0 licence and governed by the non-profit Trino Software Foundation, but you run the cluster yourself, on premises or in any cloud, and doing that well takes real tuning and operations skill. The project has no official managed service, though companies such as Starburst sell managed versions and Amazon Athena is built on it. Databricks is pay as you go with no up-front cost, billed in Databricks Units, plus the virtual machines your cloud provider charges for separately, which makes bills harder to predict. It is managed, but it is a platform to learn, with a steeper curve than a plain SQL warehouse.

Choose Databricks if your team does data engineering and machine learning and wants one vendor-supported platform. Choose Trino if you mainly need fast interactive SQL across a lake and other databases, have people to operate a cluster, and want to avoid licence fees or lock-in.

Choose Databricks if…

Data engineering and ML teams that want SQL, Spark and model training on one platform
You need Web, which Trino doesn't list
Data stays in open formats in your own cloud storage
Covers ETL, SQL analytics and machine learning in one place

Choose Trino if…

Data teams that want fast SQL over a data lake and other databases without copying data into a warehouse.
You want open-source software
You need to host it on your own servers
You want a free option (Databricks is paid only)
You need Self-hosted and Linux, which Databricks doesn't list

What is Databricks?

Databricks is a data and AI platform built around the lakehouse idea: data stays in open formats such as Delta Lake and Parquet in your own cloud storage, and Databricks provides the compute and tools on top. It was founded by the creators of Apache Spark, and Spark-based data engineering remains its core, alongside Databricks SQL warehouses for BI, collaborative notebooks, MLflow for machine learning, and Unity Catalog for governance. It runs on AWS, Azure and Google Cloud, and is aimed at data engineering and data science teams in mid-sized and large organisations. Pricing is pay-as-you-go in Databricks Units (DBUs), a normalised measure of processing, and on most clouds you also pay the cloud provider separately for the underlying virtual machines. A free trial is available. It is more of a platform to learn than a plain SQL warehouse, which makes it heavier than needed for teams that only want dashboards.

What is Trino?

Trino is an open-source, distributed SQL query engine for analytics over large data sets. Formerly called PrestoSQL, it was created by the original Presto developers and is now run by the non-profit Trino Software Foundation. Rather than storing data itself, Trino queries data where it lives: object storage such as S3 and HDFS with table formats like Hive, Iceberg and Delta Lake, plus relational databases, Cassandra, Kafka and other sources through connectors. A single query can join data across several of these systems. It speaks ANSI SQL and works with BI tools such as Tableau, Power BI and Superset. Data teams run it on their own clusters, on premises or in any cloud, for interactive dashboards, ad-hoc analysis and batch ETL. Trino is free under the Apache 2.0 licence; managed versions are sold by companies such as Starburst, and Amazon Athena is built on it. It is not a transactional database, and running a cluster well takes real operations work.

Side by side

Attribute	Databricks	Trino
Pricing	Paid	Open source
Open source	No	Yes
Self-hostable	No	Yes
Platforms	Web	Self-hosted, Linux
Best for	Data engineering and ML teams that want SQL, Spark and model training on one platform	Data teams that want fast SQL over a data lake and other databases without copying data into a warehouse.
Status	Active	Active