Platform comparison

Databricks vs Palantir Foundry

Choose Databricks if your team wants to engineer data, analytics and AI itself on open formats, pay by usage, and keep the platform in your own cloud.

Choose Foundry if you want a packaged operational layer, the Ontology, for frontline apps and decisions, or need air-gapped or classified deployment. Databricks says more than 100 customers run both, and the two vendors have had a product partnership since March 2025.

Databricks. Code and SQL over open tablesPalantir Foundry. An Ontology of objects, links and actions

Checked: Against both sides' own documentation, October 10, 2026
Sources: 18 official pages, listed below
Written by: TechFabric engineers. We're a Databricks partner, and we say where the other one wins.

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How each is built

Two different shapes, drawn from each one's own architecture docs.

Databricks

A data, analytics and AI platform that stores data in open table formats (Delta Lake and Apache Iceberg) and runs as a managed service on AWS, Azure and Google Cloud.

Palantir Foundry

A data operations platform that maps an organization's data, logic and actions into an Ontology for operational apps, and AIP adds generative AI and agents on top of it.

Which one

When each one is the right call.

Choose

Databricks when

Your data and ML engineers want to build pipelines, warehouses and models in SQL, Python and Spark on open Delta Lake or Iceberg tables.
You want published, usage-based pricing that you can start on a pay-as-you-go plan and later move to a usage commitment.
You need governance of data and AI assets in one catalog (Unity Catalog), with lineage and auditing across tables, models and agents.
You're building data apps or agents next to the data, using Databricks Apps, Lakebase (managed Postgres) or Agent Bricks.

Choose

Palantir Foundry when

The goal is operational apps for business users, where objects, links and actions in an Ontology drive decisions and write back to systems.
You want low-code app building (Workshop) and point-and-click pipelines (Pipeline Builder) more than a code-first engineering platform.
You must deploy in on-premises, air-gapped or classified environments, which Palantir's Apollo delivery platform is built to support.
You already have Databricks and want an operational layer on top of it without copying data, using Foundry virtual tables over Unity Catalog.

Side by side

10 differences, side by side.

Dimension	Databricks	Palantir Foundry
What it is for	A platform for data engineering, warehousing, analytics, ML and AI agents, used mostly by technical teams who build on it.	A data operations platform, with AIP connecting generative AI to operations, aimed at decisions and workflows run by operational users.^10,6
Core abstraction	Tables and AI assets governed in Unity Catalog. Genie Ontology, a business-context layer for Genie, is in public preview.	The Ontology: objects, properties and links over datasets and models, plus action types and functions that capture business logic and changes.^10,14,5
Data storage and formats	Data sits in your cloud object storage in Delta Lake or Apache Iceberg tables, readable by other engines.	Source data stays in its original format, and transformed data is available by default in open formats such as Iceberg and Parquet.^10,4
Building pipelines	Lakeflow handles ingestion, pipelines and orchestration, with code in SQL, Python or Scala on notebooks and jobs.	Pipeline Builder offers point-and-click transformation including LLM steps, and Code Repositories hold code-based transforms.^10,6,4
Apps and end users	Databricks Apps hosts data and AI apps on serverless compute, and Lakebase gives apps a managed Postgres database.	Workshop is a low-code app builder, and Actions define how people or AI agents change data in the Ontology or in external systems.^10,13,6
AI and agents	Agent Bricks builds, evaluates and deploys agents under Unity Catalog governance. Several parts, such as Knowledge Assistant and Supervisor Agent, are GA.	AIP Logic builds workflow agents that act through the Ontology, and LLM calls are metered as compute-seconds per 10,000 tokens.^10,17,6,7
Deployment	A Databricks-run control plane, with compute either serverless in Databricks' account or classic in your own cloud account, on AWS, Azure or Google Cloud.	Delivered through Apollo, which deploys to major clouds, on-premises and air-gapped environments, including classified clouds without network access.^11,8
Openness and interop	Open table formats plus Unity Catalog external access let other engines, Foundry included, read and write governed tables.	The Ontology is reachable through REST APIs and an Ontology SDK, and virtual tables work with Databricks, Snowflake and BigQuery without copying data.^2,4
Pricing transparency	Usage-based in DBUs, billed per second, with a price list and calculator published and committed-use discounts on request.	No public list price. The LLM compute rates in Palantir's docs apply only to some AWS-hosted enrollments, and contract rates come from your Palantir representative.^9,7
Trying it	A free trial with credits for business evaluation, and a Free Edition for personal, non-commercial learning with usage limits.	Palantir offers a Developer Tier trial account you sign up for. Production use is by contract.^9,12,16

What it is for^10,6

Databricks: A platform for data engineering, warehousing, analytics, ML and AI agents, used mostly by technical teams who build on it.

Palantir Foundry: A data operations platform, with AIP connecting generative AI to operations, aimed at decisions and workflows run by operational users.

Core abstraction^10,14,5

Databricks: Tables and AI assets governed in Unity Catalog. Genie Ontology, a business-context layer for Genie, is in public preview.

Palantir Foundry: The Ontology: objects, properties and links over datasets and models, plus action types and functions that capture business logic and changes.

Data storage and formats^10,4

Databricks: Data sits in your cloud object storage in Delta Lake or Apache Iceberg tables, readable by other engines.

Palantir Foundry: Source data stays in its original format, and transformed data is available by default in open formats such as Iceberg and Parquet.

Building pipelines^10,6,4

Databricks: Lakeflow handles ingestion, pipelines and orchestration, with code in SQL, Python or Scala on notebooks and jobs.

Palantir Foundry: Pipeline Builder offers point-and-click transformation including LLM steps, and Code Repositories hold code-based transforms.

Apps and end users^10,13,6

Databricks: Databricks Apps hosts data and AI apps on serverless compute, and Lakebase gives apps a managed Postgres database.

Palantir Foundry: Workshop is a low-code app builder, and Actions define how people or AI agents change data in the Ontology or in external systems.

AI and agents^10,17,6,7

Databricks: Agent Bricks builds, evaluates and deploys agents under Unity Catalog governance. Several parts, such as Knowledge Assistant and Supervisor Agent, are GA.

Palantir Foundry: AIP Logic builds workflow agents that act through the Ontology, and LLM calls are metered as compute-seconds per 10,000 tokens.

Deployment^11,8

Databricks: A Databricks-run control plane, with compute either serverless in Databricks' account or classic in your own cloud account, on AWS, Azure or Google Cloud.

Palantir Foundry: Delivered through Apollo, which deploys to major clouds, on-premises and air-gapped environments, including classified clouds without network access.

Openness and interop^2,4

Databricks: Open table formats plus Unity Catalog external access let other engines, Foundry included, read and write governed tables.

Palantir Foundry: The Ontology is reachable through REST APIs and an Ontology SDK, and virtual tables work with Databricks, Snowflake and BigQuery without copying data.

Pricing transparency^9,7

Databricks: Usage-based in DBUs, billed per second, with a price list and calculator published and committed-use discounts on request.

Palantir Foundry: No public list price. The LLM compute rates in Palantir's docs apply only to some AWS-hosted enrollments, and contract rates come from your Palantir representative.

Trying it^9,12,16

Databricks: A free trial with credits for business evaluation, and a Free Edition for personal, non-commercial learning with usage limits.

Palantir Foundry: Palantir offers a Developer Tier trial account you sign up for. Production use is by contract.

Cost

How each one bills you.

Databricks

Databricks bills usage in DBUs, a normalized unit of processing power, at per-second granularity with no up-front cost on pay-as-you-go. Rates vary by product, cloud, tier and region and are on the published price list. Committed-use contracts give discounts, and cloud infrastructure for classic compute is billed by your cloud provider.

Palantir Foundry

Palantir doesn't publish Foundry or AIP list prices, so pricing is negotiated per contract. Its docs list LLM compute-second rates per 10,000 tokens but say they apply only to Foundry enrollments hosted on AWS under certain default contract terms, and existing customers should confirm rates with their Palantir representative.

List prices change and committed-use discounts are negotiated, so compare quotes for your own workload rather than these units.^9,7

Using both

You don't always have to choose.

Yes. Databricks and Palantir announced a product partnership in March 2025 that links Unity Catalog with Palantir's security model and Ontology. By October 2025 Databricks said more than 100 customers had combined the two, and in June 2026 it named Palantir its ISV Public Sector Partner of the Year.

In Foundry, the Databricks connector registers Unity Catalog tables as virtual tables (GA) for reading and writing, and Python transforms and Pipeline Builder can push compute down to Databricks so the work runs there.

Sources ^1,18,15,2,3

Moving

Moving Foundry workloads to Databricks

01
List what users depend on
List each Foundry dataset, pipeline, Ontology object type, action and Workshop app, and mark which ones operational users rely on daily.
02
Land the data in Unity Catalog
Register or land the source data in Unity Catalog first; where Foundry already uses Databricks virtual tables, the data is already there.
03
Rebuild the transforms
Rebuild Foundry transforms as Lakeflow pipelines or jobs; Python and PySpark transforms move most directly, Pipeline Builder graphs need rewriting.
04
Recreate objects and apps
Recreate Ontology objects as governed tables and metric definitions, and rebuild actions and apps in Databricks Apps with Lakebase where they write back.
05
Run both, then retire
Run both in parallel and compare outputs before switching users over, then retire Foundry pieces one workflow at a time.
06
Keep Foundry where the apps are the point
Don't move if the value is the Ontology-driven apps themselves or you need air-gapped deployment; keep Foundry on top and let Databricks do the heavy compute.^2,3,8

Databricks consulting

How we build the data, AI and app layers on Databricks, with or without Foundry on top

FAQ

Databricks vs Palantir Foundry: what people ask

Is Databricks a replacement for Palantir Foundry?

Only partly. Databricks covers the data engineering, warehousing and AI layers, but it doesn't ship an equivalent to Foundry's Ontology with actions and low-code operational apps. Teams that want that layer either build it with Databricks Apps and Lakebase or keep Foundry on top of Databricks.

Can Palantir Foundry read data from Databricks without copying it?

Yes. Foundry's Databricks connector registers Unity Catalog tables as virtual tables, which Palantir lists as generally available. With Unity Catalog external access turned on, Foundry reads and writes storage directly; otherwise it connects over JDBC.

How much does Palantir Foundry cost?

There's no public number to plan against. Palantir doesn't publish list prices for Foundry or AIP, so pricing is set by contract. Even the LLM compute rates in Palantir's docs apply only to some AWS-hosted enrollments, and existing customers are told to confirm their rates with their Palantir representative.

Are Databricks and Palantir partners?

They announced a strategic product partnership on 13 March 2025. In October 2025 Databricks said more than 100 customers had combined the two platforms, naming the US Department of Defense, bp and United Airlines among them.

Which one can run in an air-gapped or classified environment?

Palantir is built for it. Its Apollo platform deploys to cloud, on-premises and air-gapped environments, including classified clouds without network access. Databricks' documentation describes deployment on AWS, Azure and Google Cloud.