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Claude Sonnet 5 now available in AIP
Date published: 2026-07-07
Claude Sonnet 5 is now available in AIP for eligible commercial and US government enrollments.
Model overview
As Anthropic’s most agentic Sonnet model yet, Sonnet 5 narrows the gap with Claude Opus 4.8 on reasoning, tool use, coding, and knowledge work, while remaining available at a lower price point. It provides a strong balance of intelligence, speed, and cost for production AIP use cases. For more information, review:
Availability
Claude Sonnet 5 is available for commercial enrollments that enable Anthropic through:
- Microsoft Azure on non-georestricted enrollments
- Amazon Bedrock on non-georestricted or US georestricted enrollments
- Anthropic Direct on non-georestricted or US georestricted enrollments
- Google Vertex on non-georestricted, US georestricted, or EU georestricted enrollments
Additionally, Claude Sonnet 5 is available for US government enrollments that enable Anthropic through Google Vertex on IL2 or IL4 enrollments.
Getting started
To use this model:
- 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 ↗.
Introducing Model Evaluations: Compare and evaluate models outside of Modeling Objectives
Date published: 2026-07-07
Model evaluations is a new Python API for capturing how a model version performs against test data and visualizing the results directly on the model page. An evaluation is a collection of metrics, images, plots, and tables that you define and log yourself, allowing you to compare model performance across versions and over time.
Previously, evaluating a model in a structured way meant working inside a modeling objective. Model evaluations remove that requirement: you can now author evaluation logic anywhere you build models and attach the results to any model version, no modeling objective required.
The Evaluation tab on a model page. The model_performance evaluation set shows results logged for each model version, grouped by version so you can compare performance across runs.
Evaluate any model version
Every evaluation is tied to a single model version - the version loaded into your transform using ModelInput. As the results are attached to that specific version, you point-in-time snapshot of how the model performed, and a foundation for comparing quality as the model is retrained.
Track performance across versions with evaluation sets
Evaluation sets are a logical grouping of evaluations that share the same methodology. Each run of an evaluation transform writes a new evaluation to the same set, so you can track how a metric evolves over version as your model changes. To analyze a model in more than one way - for example, aggregate error in one set and per-segment error in another - define a separate set for each methodology.
Getting started
To get started with model evaluations, just upgrade your repository to latest, upgrade the palantir_models library to >= 0.2384.0, and then explore the documentation to get started with model evaluations.
What's next?
Over the next few months, we will introduce the following improvements to evaluations:
- AI FDE support for the AI-assisted model development loop
- Monitors that send alerts based on evaluation performance for automated drift detection
- UI/UX enhancements for comparing evaluations
Explore the documentation to get started with model evaluations.
Let us know what you think
Send feedback through Palantir Support or the Developer Community ↗ using the modeling tag ↗.
Save AIP Analyst chats as analysis resources
Date published: 2026-07-02
AIP Analyst chats can now be saved as analysis resources in Compass, allowing you to return to prior analyses, share them with collaborators, and continue iterating over time in both standalone AIP Analyst and the Workshop widget. When you reopen an analysis, AIP Analyst re-runs the agent's tools and regenerates responses against the latest state of your Ontology, so the results always reflect the current state and respect each viewer's permissions.
Choose where to save the analysis and review what will be stored.
Learn more in the AIP Analyst analysis resources documentation.
Admin controls
Platform administrators can disable analysis saving through the AIP Analyst Control Panel extension at the enrollment level. When disabled, users cannot create or open analysis resources from AIP Analyst.
Configure analysis settings in Control Panel.
Learn more about admin configuration.
AIP Analyst capabilities
AIP Analyst helps users move from natural language questions to grounded analysis across Foundry. The agent can search the Ontology, build and transform object sets, run aggregations and SQL queries, analyze uploaded files and media, and generate summaries, charts, and maps. With analysis resources, these workflows can now be revisited, shared, and extended over time.
Example analysis in AIP Analyst.
Learn more about the AIP Analyst capabilities.
Configurable user rate limits for AIP capacity management
Date published: 2026-07-02
Enrollment administrators can now view and configure per-user rate limits for LLM usage in AIP, providing more granular control over how an enrollment's capacity is consumed.
Background
LLM capacity in AIP is managed at three levels.
- Enrollment-level limits set the overall ceiling for your organization's token and request throughput.
- Project rate limits control how much of that enrollment capacity each project can use. Project rate limits are already configurable by administrators.
- Per-user rate limits govern how much capacity any single user can consume; usage can come from interactive, user-attributed workflows, from applications like AIP Assist, AIP Analyst, or AI FDE, from native assistant features (such as Pipeline Builder Explain and Generate), or from IDE integrations such as Continue and Claude Code.
Until now, per-user limits were set by Palantir as fixed defaults that administrators could not adjust. Until the introduction of configurable user rate limits, there was no self-service way to address issues like a single power user consuming a disproportionate share of capacity on a given model, or specific teams requesting more capacity.
What's new
Administrators can now manage per-user rate limits directly from the Manage rate limits tab on the AIP usage & limits page in the Resource Management application. Administrators are now able to:
- Set a custom default that applies to every user across all models, replacing Palantir's published defaults.
- Add per-model overrides to raise or lower limits for specific models without changing limits across all models.
- Create user-group overrides targeted at specific Foundry user groups, each with its own default and optional per-model configuration. This enables you to give a group of heavy builders more capacity, or restrict experimental users so that they only have high capacity on a subset of models.
The interface for managing AIP usage & limits, displaying the default user rate limits and model overrides.
Palantir's built-in defaults remain the recommended and sensible option, and will continue to apply wherever no custom override is configured. We recommend starting with the defaults and adjusting only where your usage patterns call for it, and revisiting any custom limits as new models are released.
This feature is available now in the Resource Management application for all AIP enrollments. Learn more in the LLM capacity management documentation.
Your feedback matters
We want to hear about your experiences with AIP capacity management in the Palantir platform and welcome your feedback. Share your thoughts with Palantir Support channels or on our Developer Community ↗ using the control-panel tag ↗.