Palantir Alternatives: How to Get an Operational Ontology Without Palantir (and When Palantir Is the Right Call)

The problemThe Palantir demo won you over, but the proposal has no list price and your entire business model would live inside a single platform.

The demo was flawless. In two weeks, Palantir's engineers connected your ERP, your CRM and your logistics system, and showed you a dashboard where every order, customer and truck appeared with its relationships and with buttons to take action. Then the proposal arrived: no list price, a multi-year commitment and your business model living inside a single platform. Your CFO asks the right question: are there Palantir alternatives that give us the same thing, or at least what we actually need?

The short answer is yes, but not in a single product. To choose well, you first need to understand what you are buying when you buy Palantir.

Why Palantir is in every conversation

This is not a fad. Palantir reported 2025 revenue of $4.475 billion, up 56% from the prior year, and its U.S. commercial revenue grew 109% to $1.465 billion. It ended the year with 954 customers. Companies are buying because Palantir solved something most data platforms left unfinished: a model of the business that people and agents can act on.

That model is its Ontology: objects (customer, order, plant), properties, links between them and actions that change those objects under rules and permissions. If you want the concept from scratch, start with what an enterprise ontology is.

But Palantir does not sell one thing. It sells a bundle of layers plus a team of engineers who implement it. And not every company needs every layer.

What you are really buying: the layers of an operational ontology

Break the proposal into layers and compare it with each alternative path:

Read this way, the diagram shows two things. First, each alternative is strong in different layers: none of them replicates the whole bundle on its own. Second, the most underestimated layer is actions with permissions. It is what turns a data model into a system that runs operations, and it is the hardest part to rebuild if you ever leave.

Path 1: the semantic layer of your data platform

If your data already lives in Microsoft Fabric, Databricks or Snowflake, the natural move is to start there.

• Microsoft Fabric IQ was introduced in November 2025 with an ontology model that captures entities, relationships and possible actions. It launched in preview, so check its status before committing a critical project to it.
• Snowflake offers semantic views: defining them has been generally available since June 2025, and querying them since August 2025.
• Databricks launched Unity Catalog metric views in public preview in May 2025.

Pros: you do not move data, you build on the governance and security you already have, and the incremental cost is usually lower. Cons: these layers were born to define consistent metrics, not to model actions. For an agent to propose, approve and execute changes with permissions, you will have to build that part separately. And you stay tied to the data cloud you chose.

Path 2: a specialized knowledge graph

These are the platforms whose business is precisely meaning and relationships:

• Stardog works with RDF and OWL, the W3C standards for ontologies, and is designed as a semantic layer across multiple systems.
• TopBraid EDG focuses on governing ontologies, vocabularies and metadata built on semantic standards.
• Neo4j is a property graph database, widely used for GraphRAG, with a broad developer ecosystem.

The market is moving: in May 2025, ServiceNow announced an agreement to acquire data.world, a data catalog built on a knowledge graph, to give context to its agents.

Pros: the relationship model is richer than a semantic layer's, and if the platform uses open standards, you can export the ontology. Cons: they usually do not include actions or operational applications, and you need a team that knows how to model.

Path 3: open standards and custom agents

The third path separates two things Palantir bundles together: the model and the operation.

• The model is written in W3C standards: OWL for objects and relationships, SKOS for vocabularies and SHACL for validating rules. There are mature open source tools, such as Stanford's Protégé editor and Apache Jena.
• The operation lives in the agent layer: each action is exposed as a tool with permissions, human approval by role and an audit log, for example through MCP.

Pros: the exit cost is the lowest, because the model and the code are yours; you can use whichever AI model handles each task best, and you start with a process, not a platform. Cons: you need a method and a team to build it, and you will not immediately have the application catalog of a mature platform.

When Palantir is the best option

Being honest helps you decide. Palantir is often a good choice when:

• You need to integrate many systems very quickly and you have the budget for intensive support from its engineers.
• Your operation is critical and complex (defense, energy, large-scale manufacturing) and you value a single platform with everything integrated.
• You do not have, and do not plan to build, an in-house data team to maintain the model.

If instead your priority is controlling cost, keeping the freedom to switch vendors and starting with a specific use case, one of the other three paths usually fits better. It is the same logic as build, buy or commission.

Eight questions to evaluate Palantir alternatives

Put these questions to Palantir and to every alternative, in writing:

1. Can I export the complete ontology in an open standard such as RDF or OWL?
2. Where do the actions and permissions live, and what happens to them if I leave?
3. Can my team maintain the model, or does it depend on your engineers?
4. What is the total cost over three years, including consumption and services?
5. Can I use different AI models, or am I locked into one?
6. How are changes to the ontology versioned, and who approves them?
7. How do my agents connect: APIs, MCP, proprietary connectors?
8. What will be up and running in the first 90 days, on a real process?

Illustrative example: a manufacturer with three plants

Picture a mid-sized manufacturer that received a Palantir proposal for production and supply planning. This is an illustrative example, not a real client.

Before signing, the team broke the proposal into layers. Its data already lived in Databricks, so integration was not the problem. What was missing was a shared model of plant, order, material and supplier, and actions such as "reschedule order" with approval from the plant manager.

It chose path 3 for the model and the actions, connected to its existing data platform: a minimal ontology on open standards, built with the method in how to build an enterprise ontology, and two agents with permissions on top of it. It started with one plant. The model is in the company's name and can expand to the other two without renegotiating anything.

Common mistakes

• Comparing demos instead of layers. A demo shows the best of everything together; the decision is made layer by layer.
• Forgetting the exit cost. Ask what it would cost to rebuild the ontology, the actions and the applications somewhere else.
• Buying the semantic layer assuming it comes with actions. Defining metrics is not the same as governing what an agent does.
• Choosing open source without a method. Open standards are worthless without owners, versioning and clear business questions.

What to do on Monday

Take whatever proposal is on your desk, from Palantir or anyone else, and fill in the layer diagram: what it covers, what it does not and what it would cost you to leave. Then answer the eight questions. In most cases, the result is not "Palantir or nothing," but a combination you can control.

At Addabra, we build your company's ontology on open standards, connect it to the data platform you already have and put the agents on top, with their actions, permissions and audit logs, and with the code and the model in your name. You can see how we work in our company brain development service.

Palantirontologyknowledge graphdata strategy