Foundry-style context and actions
on the warehouse you already run
Palantir Foundry is built for large organisations and government: an Ontology of objects, links and actions, operational applications, and deployments up to air-gapped and classified environments. Most mid-market teams want a smaller part of that: a model of the business over their data, actions that write back to their tools, and governed AI on top. Rig does that on the warehouse you already run.
What a Foundry programme involves, and what Rig does with less
Foundry's power comes from the Ontology, and the Ontology is modelled by people. Teams build object types, link types and action types, usually with Palantir's forward-deployed engineers, then build pipelines and operational apps on top. That is the right investment for an organisation running its operations on the platform. For a team that mainly wants its warehouse answerable and actionable, most of that modelling can be drafted for you.
A Foundry programme typically means:
- →Modelling the Ontology: object, link and action types, built by your engineers and often Palantir's
- →Pipelines in Pipeline Builder and Code Repositories
- →Workshop apps for operational users
- →Private pricing, with contracts generally one to five years
With Rig, on the warehouse you already run
Rig Map drafts table meaning, joins and business terms on connection, and your team certifies them
Ingestion on open-source dlt, models in dbt Core, in your own git repository
One governed MCP endpoint for Claude, ChatGPT or Cursor
Actions that write back to your CRM, Slack and ticketing, with sign-off where you want it
Role, row and column-level access on every query, with a full audit trail
Already on Foundry?
Rig can sit alongside it. Keep Foundry for the operational programme it was bought for, and use Rig to give the wider company governed, self-serve access to the warehouse your data also lands in, without adding Foundry seats. See how Rig Map works
How Foundry's concepts map to Rig
| In Foundry | In Rig | What it means |
|---|---|---|
| Object types | Tables with meaning attached | Every table and column described: what it is, what a row means, which one is the source of truth. |
| Link types | Inferred joins | Relationships read from the schema and from the queries people run, kept current as the schema changes. |
| Properties and definitions | Business terms and certified metrics | Revenue, active customer, churn: defined once and used by every agent, dashboard and report. |
| Action types | Actions and write-backs | Agents and workflows that update the CRM, post to Slack or raise a ticket, with sign-off where you want it. |
| Ontology permissions | Row, column and role-level access | Enforced per user on every query, from a person, a dashboard or Claude over MCP. |
Common points of confusion
Both platforms put a model of the business over your data. They differ in who builds it and what it is built to run.
Foundry builds an operational platform around an Ontology your engineers model, for organisations that run their operations on it.
Rig makes the warehouse you already run answerable and actionable for people and AI agents, with the model drafted for you.
| Aspect | Rig | Palantir Foundry |
|---|---|---|
| Where the data lives | Your own Snowflake, BigQuery, Databricks or Redshift, or a warehouse Rig hosts for you | Foundry datasets, or virtual tables that query Snowflake, BigQuery and Databricks in place, governed through Foundry |
| Semantic model | Rig Map: table meaning, joins, business terms and certified metrics, drafted by an agent | The Ontology: objects, links and actions |
| Who builds it | Rig's agent drafts and maintains it; your team reviews and certifies | Your engineers, often alongside Palantir's forward-deployed engineers |
| Pipelines | 300+ managed connectors on dlt, transformations in dbt Core | Pipeline Builder and Code Repositories |
| Operational apps | Data apps and dashboards built on certified metrics, a lighter toolkit | Workshop apps built on the Ontology |
| AI | Rig agents, plus one governed MCP endpoint for Claude, ChatGPT or Cursor | AIP, working over the Ontology |
| Deployment | Rig cloud, or your own cloud account on Enterprise. No air-gapped option | Cloud, on-prem and air-gapped, with FedRAMP High, IL5 and IL6 authorisations |
| Pricing | A platform subscription, with any project scope agreed before you start | Not published. Private pricing, contracts generally one to five years |
Foundry can query Snowflake, BigQuery and Databricks in place through virtual tables, so it does not force a data move. The real differences are where the semantic model lives and who builds it.
Where Foundry is the better choice
- →Classified, government and air-gapped work. Palantir deploys into air-gapped environments and holds FedRAMP High, IL5 and IL6 authorisations. If you need that, Foundry is the answer.
- →Operational applications. Workshop builds interactive applications for operational users on the Ontology. Rig's data apps and actions are real, but they are a lighter toolkit.
- →The very largest estates. Palantir reports 1,049 customers, and its top 20 averaged $124M of revenue each over the year to June 2026. The platform is built around organisations of that size.
- →Maturity and a reference list. Palantir is a public company with a long customer list. Rig is younger, and procurement teams are right to weigh that.
Want Foundry-style context and actions on your own warehouse?
Common questions
For mid-market and growth-stage companies, yes. Rig gives you the parts of Foundry most teams buy it for: a model of your business over your data, actions that write back into your tools, and governed AI on top. It runs on your own warehouse, without a team of forward-deployed engineers. It does not replace Foundry in defence, classified or air-gapped environments.
Rig Map. It records what every table and column means, how tables join, which business terms and metrics are certified, and who can see what. Foundry's Ontology is a richer object model built to drive operational applications. Rig Map is built to make your warehouse answerable and actionable for people and AI agents, and an agent drafts it for you.
Rig's agent. On connection it reads the schema, samples values, studies the queries your team already runs and drafts descriptions, joins and metric definitions. Your data owners review and certify them, and the agent flags drift as the warehouse changes. Rig engineers can also work with your team on the parts that need a person.
No. Rig runs on the Snowflake, BigQuery, Databricks or Redshift you already have. If you have no warehouse yet, Rig can host one, and it stays a standard warehouse you can take with you.
Not a complete one that we know of. Foundry is commercial software, and Rig is too. The plumbing underneath Rig is open source: ingestion runs on dlt and transformations on dbt Core, and both run without us. Your models are SQL in your own git repository.
Rig runs in our cloud, or inside your own cloud account on the Enterprise plan. It does not run air-gapped or in classified environments. If that is a requirement, Foundry is built for it.
Yes, if your data also lands in a warehouse Rig can read. Some teams keep Foundry for the operational programme it was bought for, and use Rig to give the wider company governed, self-serve access to the warehouse without adding Foundry seats or builders.
Palantir facts checked 28 September 2026 against Palantir's product documentation and its Q2 2026 10-Q. Palantir, Foundry, AIP and Workshop are Palantir's names, used here only to describe their products.