Databricks vs Teradata

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

Teradata

Enterprise data warehouse and analytics platform that runs on major clouds or on-premises.

PaidSelf-hostable

Visit Teradata Teradata profile

The short answer

Databricks is a cloud lakehouse that keeps data in open formats in your own storage and combines Spark data engineering, SQL warehousing and machine learning, billed pay-as-you-go in DBUs. Teradata is an enterprise data warehouse that runs on major clouds or on-premises, including air-gapped sites, with Fixed + Flex pricing. Pick Databricks for engineering and ML on the public cloud; pick Teradata when regulation or existing investment demands on-premises or sovereign deployment.

Databricks or Teradata: our take

Databricks was founded by the creators of Apache Spark, and Spark-based data engineering is still its core. Data stays in open formats such as Delta Lake and Parquet in your own cloud storage, while Databricks supplies compute, SQL warehouses for BI, collaborative notebooks, MLflow for machine learning and Unity Catalog for governance. It runs on AWS, Azure and Google Cloud. Billing is pay as you go with no up-front cost, measured in Databricks Units (DBUs), and on most clouds the virtual machines are billed separately by your cloud provider, which makes costs hard to predict. A free trial is available. It has a steeper learning curve than a plain SQL warehouse.

Teradata is a long-standing enterprise warehouse vendor serving banks, retailers, telecoms and governments. Teradata Cloud runs as a managed service on AWS, Microsoft Azure or Google Cloud, in a customer's own cloud account, or on-premises, including air-gapped environments. It is mature at running many concurrent SQL workloads, and newer versions add elastic compute, open table format support, in-database analytics and AI services. Pricing uses a single Teradata Unit under a Fixed + Flex model: a committed baseline with elastic capacity on top, arranged through sales with no public price list. There is a free environment to try it. Its community and third-party ecosystem are smaller than Databricks'.

A data engineering or data science team on the public cloud that wants ETL, SQL and model training in one place, on open formats, will usually lean towards Databricks. A large regulated enterprise that needs the same warehouse across cloud and on-premises, must meet sovereignty rules, or already runs Teradata will find it the safer fit, with a more predictable baseline cost.

Choose Databricks if…

Data engineering and ML teams that want SQL, Spark and model training on one platform
Data stays in open formats in your own cloud storage
Covers ETL, SQL analytics and machine learning in one place

Choose Teradata if…

Large regulated enterprises that need one warehouse across cloud and on-premises
You need to host it on your own servers
You need Self-hosted, which Databricks doesn't list
Runs on AWS, Azure, Google Cloud or on-premises
Supports air-gapped and sovereign deployments

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 Teradata?

Teradata is a long-standing enterprise data warehouse vendor whose platform, now sold as Teradata Cloud (formerly VantageCloud), runs large-scale SQL analytics and mixed workloads for big companies in banking, retail, telecoms and government. It can be deployed as a managed service on AWS, Microsoft Azure or Google Cloud, in a customer's own cloud account, or on-premises and in air-gapped environments, which makes it a common choice where data sovereignty or regulation rules out pure public cloud. Recent versions add elastic compute, open table format support, in-database analytics and AI services. Pricing uses a single unit for compute, storage and software, sold as Fixed + Flex: a committed baseline with elastic capacity on top, arranged through sales. There is a free environment for trying it out. It fits enterprises with existing Teradata investments or strict deployment requirements, while smaller teams usually find cloud-native warehouses like Snowflake or BigQuery quicker to start with.

Side by side

Attribute	Databricks	Teradata
Pricing	Paid	Paid
Open source	No	No
Self-hostable	No	Yes
Platforms	Web	Web, Self-hosted
Best for	Data engineering and ML teams that want SQL, Spark and model training on one platform	Large regulated enterprises that need one warehouse across cloud and on-premises
Status	Active	Active

Verified facts, compared

Facts both apps publish, checked on each vendor's own site.

Fact	Databricks	Teradata
Pricing model	Pay as you go, no up-front cost	Fixed + Flex
Billing unit	Databricks Unit (DBU)	Teradata Unit