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Open-Source Palantir Foundry Alternative for Ontologies
The closest open-source Palantir Foundry alternative for ontology work is Helix Foundry: an Apache 2.0 app that connects your databases, tools and documents, drafts one ontology over them, loads it into HelixDB as a connected graph, and lets you explore it and ask questions with every value traced to its source. It runs on your machine with docker compose. It is also much narrower than Palantir Foundry, and this post is about exactly how much narrower.
If you searched for a free or open-source Foundry replacement, you probably want one of two things: a way to make scattered company data read as one model, or a data layer you can point AI agents at. Helix Foundry targets that second job and the ontology half of the first. It is not a pipeline builder, an app studio or a governance suite. Know that before you clone anything.
What you are really replacing when you look for a Palantir Foundry alternative
Most people searching for a Palantir Foundry alternative open source are not replacing the whole platform. They are replacing one capability: a shared model of the business sitting on top of data that lives in many systems.
Palantir Foundry is a mature, broad platform. It covers data pipelines, application building, governance and more. Palantir's own documentation describes its Ontology as the layer that models a business as object types, link types and actions over integrated data. That Ontology is the piece most "alternative" searches are really after.
The often-suggested open-source route is a stack: Airbyte for ingestion, dbt for transformation, Apache Superset for dashboards. Those are good tools, and together they cover moving data, shaping tables and charting them. What that stack does not give you out of the box is an ontology: entities and links that resolve the same customer across Stripe, your CRM and a contract PDF, with a trail back to the source of every value. You can build that on top, and many teams do, but it is a project, not a checkbox.
So decide first which job you have. If you need the full platform, an open-source stack or a narrow tool will feel like a downgrade. If you need the ontology layer and a graph agents can query, a narrow tool is the honest fit.
What an ontology does: object types, links and actions over integrated data
An ontology is a typed model of your business: the kinds of things you have, how they connect, and what can be done with them, defined once over data that arrives from many systems.
In Palantir's framing, those are object types (a Customer, an Invoice), link types (a Customer has Invoices) and actions (things that can be done to objects). Whatever tool you use, the value is the same. A question like "which enterprise customers have an overdue invoice and an open high-priority ticket" stops being a four-system join and becomes a walk across links.
Three properties make an ontology worth the effort. Types are shared, so "customer" means one thing to the whole company and to every agent. Links are first-class, so relationships are data rather than something rebuilt in each query. And provenance survives: when a number on screen is wrong, you can find the row or document that produced it.
That last property matters most for AI. An agent answering from flat tables guesses at relationships. An agent answering from an ontology follows them, and can show its work.
The approach with any tool: entities, links, and matching the same customer across sources
Whatever you build with, the work breaks into the same three steps, and the third is where projects stall.
First, pick entities. Start from the questions people actually ask, not from your table list. Customers, subscriptions, invoices and tickets are entities; a join table usually is not. Name each one and decide what makes it unique.
Second, define links. Write them as sentences: a customer has subscriptions, an invoice belongs to a subscription, a ticket is raised by a customer. If you cannot say the link in a sentence, you do not understand it well enough to model it yet.
Third, resolve identity: matching the same customer across sources. Stripe has a customer ID and an email. Your CRM has a company name and a different email. A call note mentions the company by a short name. Pick the strongest shared keys first (email, domain, external IDs), match on those, and send the ambiguous leftovers to a person instead of guessing. A wrong merge is worse than a missing one, because it silently poisons every answer built on top.
Do this in dbt models, in a notebook, or in a purpose-built tool. The steps stay the same. Only the amount of drafting done for you changes.
Helix Foundry: what it is and what it deliberately does not do
Helix Foundry turns the tools your company runs on into one connected graph, and it is Apache 2.0 across the site, backend and its three images. Helix Foundry is the app; HelixDB is the graph-vector database it loads into. HelixDB is an open-source graph-vector database built in Rust that handles graph, vector and full-text search in one engine.
The flow is: connect sources, draft an ontology, load it, then explore and ask.
Connect. Connectors cover PostgreSQL, MySQL, Amazon S3, Stripe, PostHog, WorkOS, GitHub, Linear, Granola, Cal.com, Slack, Notion and Google Workspace, plus documents (Markdown, text, PDF) and table files (CSV, TSV, JSON, JSON Lines, Parquet). Syncs read only what changed, using change capture on PostgreSQL and MySQL where the database allows it.
Draft. An ontology agent drafts types, links, rules, and the tables and documents each one comes from, based on your data and your goal. You review, edit and make a version current. Nothing is published until you say so.
Load. The ontology loads into HelixDB with the origin of every value kept: a table row or a document quote. Values that documents disagree on wait for review instead of being silently picked.
Explore and ask. The Explorer shows entities, their relationships in and out, and compares two loads. The Analyst answers questions from a load with charts, the queries it ran and a source for every claim.
What it deliberately does not do: per the Foundry docs, it never writes to your sources. It is not a pipeline orchestrator, an app builder or an enterprise governance layer. Self-hosted, the install is single-user on 127.0.0.1 with no sign-in, so it is for one person on their own computer, not a shared deployment.
On AI models: the local install uses an ANTHROPIC_API_KEY or OPENAI_API_KEY, or else a local model in Ollama or LM Studio, and also supports Azure OpenAI, Amazon Bedrock or any OpenAI-compatible server. On Helix Cloud it uses Helix's Claude, or your own key on Foundry Pro. Self-hosted, columns that look sensitive are masked before samples reach a hosted model.
Try it: run Helix Foundry locally or on Helix Cloud, step by step
You need Docker with Compose v2, ports 3000 and 8333 free, and room for a few GB of images on the first start.
Clone and start it:
git clone https://github.com/helixdb/helix-foundry.git
cd helix-foundry
docker compose up -d --waitOpen http://localhost:3000.
Give Foundry an AI provider: set ANTHROPIC_API_KEY or OPENAI_API_KEY, or point it at a local model in Ollama or LM Studio.
Click Use the sample. It loads a dataset for a fictional company, Acme Corp: customers, subscriptions, invoices, support tickets and usage, plus contracts and call notes. Once Foundry has an AI provider, the walkthrough takes about a minute.
Open the Explorer and follow a customer to its invoices and tickets. Then ask the Analyst a question and check the source it cites for each claim.
When the sample makes sense, add your own source: add it, test it, sync its tables, then generate an ontology from them. The Foundry docs walk the whole path, from synced data to a generated draft, a load, the Explorer and Ask.
Prefer not to run containers? Use the hosted app at foundry.helix-db.com and choose Set up with your agent. If you work from a coding agent, Foundry also connects to Claude Code, Codex or another agent through the Helix MCP.
Keep the self-hosted instance on localhost. It has no sign-in and is not meant to be exposed.
Where Palantir Foundry is the better fit, and where Helix Foundry is enough
Palantir Foundry is the better fit when you need a full platform: managed pipelines, application building, and organisation-wide governance around a shared ontology. Its Ontology, with object types, link types and actions, is built to sit inside that wider system. Helix Foundry does not try to match that breadth.
Helix Foundry is enough when the job is the ontology itself and what you do with it next. Concretely:
| Need | Palantir Foundry | Helix Foundry |
|---|---|---|
| Broad platform: pipelines, apps, governance | Yes, mature and broad | No, deliberately narrower |
| Draft an ontology from your tables and documents | Yes | Yes, via the ontology agent, with human review |
| Provenance on every value | Not covered here | Yes, row or document quote |
| Explore entities and ask questions with cited sources | Yes | Yes, Explorer and Analyst |
| Open source | No | Yes, Apache 2.0 |
| Run locally with docker compose | No | Yes |
| Point AI agents at the graph | Not covered here | Yes, via the Helix MCP |
Compared with an Airbyte, dbt and Superset stack, Helix Foundry does not replace your warehouse transformations or dashboards. It adds the layer that stack lacks: typed entities, links, cross-source matching and traceability, in one graph. Many teams will keep their existing ingestion and use Foundry for the model on top.
If you are a data or ops lead who wants to see whether an ontology over your own data is worth building, start narrow. If a pilot proves the value and you outgrow it, you will know exactly which broader capabilities you need.
FAQ
The questions below cover what people most often ask when they search for an open-source alternative to Palantir Foundry.
Run the Acme sample before you commit to a platform
A Palantir Foundry alternative open source will not match Palantir's breadth, and Helix Foundry does not pretend to. It gives you the ontology layer: connect sources, draft types and links, load into HelixDB, and trace every answer to its row or quote. Clone the repo, run docker compose up -d --wait, click Use the sample, and see whether following a customer across systems is the thing you were missing. Then star the Helix Foundry repo and point it at one of your own sources.