Palantir Release Notes

Date published: 2026-10-01

AIP Analyst now gives teams more ways to configure shared defaults and workflow guidance. New model categories simplify model selection, while compact view keeps conversations focused on your questions and the agent’s responses.

Configure organization defaults

Administrators can now set default search scopes and a default model category through the AIP Analyst extension in Control Panel. Users starting a new analysis directly in AIP Analyst inherit defaults from their primary organization for settings they have not configured. They can override these defaults in Settings.

Organizations can also require watermarks on PDF exports.

Users receive their primary organizations settings by default user defaults can be updated in the settings dialog.

Users receive their primary organization's settings by default, user defaults can be updated in the settings dialog.

Share workflow guidance through group skills

Administrators can now assign AIP skills to user groups from Control Panel. Assigned skills automatically appear in members’ Skills settings, making shared guidance available without each user having to add it individually. …

Date published: 2026-10-01

The following models are now available in AIP through Fireworks for US georestricted commercial enrollments:

• Moonshot AI — Kimi K3 ↗
• Z.ai — GLM-5.3 ↗
• Z.ai — GLM-5.3 Flash ↗
• DeepSeek — DeepSeek V4.1 Flash ↗

Existing availability for non-georestricted (GLOBAL) commercial enrollments continues.

Getting started

To use these models:

• Confirm that your enrollment administrator has enabled the relevant model family.
• Review token costs and pricing.
• See the complete list of models available in AIP.

Your feedback matters

We want to hear about your experiences using language models in the Palantir platform and welcome your feedback. Share your thoughts with Palantir Support channels or on our Developer Community ↗ using the language-model-service tag ↗.

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Date published: 2026-10-01

We're excited to announce support for reading and writing Foundry Iceberg tables directly in Pipeline Builder's Faster pipelines. You can use Iceberg tables as inputs, perform custom logic and transforms, and output Iceberg tables. This new capability provides greater flexibility and performance when building data transformation pipelines.

A Faster pipeline with Iceberg inputs and an Iceberg output

A Faster pipeline with Iceberg inputs and an Iceberg output

What's new

Use Iceberg alongside existing Foundry datasets in Faster Pipelines. You can read datasets and write Iceberg outputs, or use Iceberg tables as inputs to downstream transformations.

Key Features

• Encrypted Iceberg table support: Read and write both encrypted and unencrypted Iceberg tables in both Spark and Faster pipelines.
• Overwrite or append to Iceberg outputs: Rebuild the table’s contents in a snapshot run, or append new results from an incremental run. Note that reading from incremental Iceberg inputs in Faster pipelines is not yet supported.
• Data quality expectation support: Apply data quality expectations to Iceberg outputs with checks that can fail the build before publishing invalid results, ensuring data integrity. …