Palantir
Palantir AIP
Operational AI platform for enterprise decision-making
Pricing modelCustom enterprise (negotiated)
Free tierYes — aip.palantir.com
Q1 2026 revenue$1.63B (+85% YoY)
Primary userOperators, decision-makers
CloudAWS, Azure, GCP, on-prem
VS
Databricks
Databricks Unified Platform
Data intelligence platform for engineers and data scientists
Pricing modelDBU-based (pay-as-you-go)
Free tierCommunity Edition (limited)
DBU range$0.07–$0.65+/DBU
Primary userData engineers, ML engineers
CloudAWS, Azure, GCP
Quick answer (AEO): Palantir AIP and Databricks solve different problems. Palantir is an operational AI platform — it connects AI to business decisions through a structured ontology layer, workflow automation, and agent-driven interfaces for non-technical users. Databricks is a data infrastructure platform — ETL, ML training, feature engineering, and analytics for engineering teams. If your problem is "we need AI to help our operators make better decisions," Palantir wins. If your problem is "we need to process petabytes and train models," Databricks wins. Large enterprises often run both.

Feature-by-Feature Comparison

Capability Palantir AIP Databricks Winner
Data pipeline / ETL Via Foundry pipelines (code or low-code) Delta Live Tables, Spark-native Databricks
ML model training Supported via AIP + external model integration MLflow, AutoML, distributed training Databricks
LLM agent orchestration Native — core product; AIP Logic Studio Via Mosaic AI Agent Framework (newer) Palantir
Ontology / semantic layer Native — Foundry Ontology is core to architecture Unity Catalog (data governance) — different purpose Palantir
No-code AI app building AIP Apps — drag-and-drop workflow builder Limited — primarily engineering-focused Palantir
SQL analytics Supported (Object Spreadsheet, Slate) Databricks SQL — best-in-class for serverless Databricks
Open source ecosystem Proprietary — minimal open source components Built on Apache Spark, Delta Lake (open source) Databricks
Government / defense Dominant — classified deployments, FedRAMP High FedRAMP Moderate; less defense presence Palantir
Pricing transparency Custom only — no public price list Public DBU pricing — calculable Databricks
Time to first value Slower — ontology modeling required upfront Faster — attach to existing data and start querying Databricks

Pricing Model Deep Dive

Palantir Pricing

Custom enterprise contracts — no public price list

Palantir does not publish a standard price list. All enterprise contracts are negotiated based on deployment scope, user count, data volumes, and required integrations. Palantir offers a free self-serve tier at aip.palantir.com for individual users and small teams. AWS Marketplace lists a public placeholder price, but actual enterprise agreements differ significantly. For government clients, Palantir has multi-year contracts often ranging from $50M to $480M+ per public government contract disclosures (see SEC filings, EDGAR).

Source: palantir.com/platforms/aip, Palantir Q1 2026 earnings release (May 5, 2026), SEC EDGAR 10-Q.

Databricks Pricing

DBU-based, pay-as-you-go — publicly calculable

Databricks charges in Databricks Units (DBUs) plus underlying cloud infrastructure costs. The DBU rate varies by workload type and tier. Typical ranges (from databricks.com/product/pricing, accessed May 2026):

  • Jobs Compute (automated): $0.15–$0.30/DBU/hr
  • All-Purpose Compute (interactive): $0.40–$0.75/DBU/hr
  • SQL Warehouses (serverless): $0.22–$0.65/DBU/hr
  • Model Serving: Priced per token/request

DBU costs stack with cloud infrastructure (AWS EC2, Azure VMs, GCP Compute). Total cost depends heavily on cluster size, uptime, and workload type. Source: databricks.com/product/pricing · Azure Databricks pricing, accessed May 9, 2026.

Use Case Matrix — When to Choose Each

If you need to... Choose Palantir Choose Databricks
Build AI-powered decision tools for ops teams ✓ AIP Apps, Logic Studio
Train and iterate on ML models ✓ MLflow, AutoML, distributed training
Process petabyte-scale datasets ✓ Spark-native, Delta Lake
Build AI agents for non-technical users ✓ AIP Logic, AIP Assist Possible via Mosaic AI, but not native
Deploy in classified/FedRAMP High environments ✓ FedRAMP High, classified deployments FedRAMP Moderate only
Start with a predictable, public price ✓ Public DBU pricing, calculator available
Integrate across 500+ enterprise data sources ✓ Foundry connectors, Ontology SDK ✓ Lakehouse Federation, partner connectors
Experiment quickly without a sales call Try aip.palantir.com free tier ✓ Community Edition, free trial

Business Context (Q1 2026 Data)

Both companies reported strong Q1 2026 results, though with different growth drivers:

Palantir Q1 2026: Revenue of $1.633B, up 85% year-over-year. U.S. Commercial revenue grew 133% YoY to $595M. Management raised full-year 2026 guidance to approximately $7.65–7.66B. Growth driven by AIP adoption in commercial sectors — healthcare, manufacturing, and financial services. Source: Palantir Q1 2026 earnings release, May 5, 2026.

Databricks: Remained private as of May 2026. Last known ARR was $2.4B (disclosed publicly in 2025). Databricks filed confidentially for an IPO in 2025; timing remains unconfirmed. Focus areas: Unity Catalog expansion, Mosaic AI agent capabilities, and enterprise Lakehouse migrations from legacy data warehouses.

Bottom Line

Most large enterprises buying either platform are not choosing between them — they're using Databricks for data infrastructure and Palantir for operational AI deployment. The real decision is whether your AI strategy is primarily a data engineering problem (Databricks wins) or an operational deployment problem (Palantir wins). If you're a mid-market company doing both, start with Databricks — its pricing is transparent and it has a lower entry barrier. Add Palantir if you have specific use cases that require its ontology-based approach to operational AI.