A response to the Palantir vs Microsoft Fabric IQ discourse:
TLDR: Palantir Technologies has been 10 years ahead because…
Nicolas Daveau's recent post on Palantir's decade-long head start sparked the predictable response: Microsoft is playing catch-up, Fabric IQ copies the airline ontology example, and traditional enterprise software vendors can't replicate Palantir's Forward Deployed Engineering model.
He's right. And he's missing the bigger picture.
The Market Has Fractured
Every major data platform now offers some form of semantic layer or ontology capability. Snowflake launched Semantic Views. Databricks embedded Metric Views in Unity Catalog. Microsoft announced Fabric IQ with operational ontologies and autonomous agents. Google opened Looker's LookML to external tools.
They're all addressing the same problem: raw data is useless without business context. A column named amt_usd_net means nothing until someone defines it as "Net Revenue after discounts, excluding returns, in reporting currency."
But here's what Daveau’s narrative slightly misses: these platforms aren't all solving the same problem.
Three Distinct Problem Spaces
1. Analytics Consistency Snowflake, Databricks, and Looker primarily solve metric drift. When Finance calculates revenue differently from Sales, you get boardroom arguments instead of decisions. These semantic layers enforce a single definition, queryable by any tool. Snowflake's Semantic Views and Intelligence features are now generally available; Databricks' Unity Catalog Metric Views provide governed metrics across AI and BI workloads.
2. Workflow Automation Palantir's Ontology goes further. It includes Actions and Functions: the ability to trigger workflows, not just read data. So called “Kinetics”. When Airbus uses Skywise to manage maintenance schedules, the ontology describes both the aircraft fleet and crucially, it also it operates on it.
Microsoft is now competing directly in this space. At Ignite 2025, they announced Fabric IQ with an Operations Agent that continuously monitors business data in real time, reasons over live conditions, evaluates trade-offs, and automatically takes actions to advance business outcomes. Fabric IQ is in preview; Foundry is production-proven (I should of course clarify that I mean Palantir’s Foundry). But the gap is closing.
The Deployment Model Is The Product
Daveau's core insight holds: Palantir's success comes from Forward Deployed Engineers who embed with clients, learn the domain, and build ontologies that reflect actual operations rather than idealised data models.
Microsoft, Snowflake, and Databricks sell software. Palantir sells transformation delivered through software. These are different businesses.
But that distinction cuts both ways. The FDE model doesn't easily scale to mid-market and nor do Palantir really want it to. It requires a new level of involvement from operational decision makers that’s not been required before or in other paradigms. And it assumes the vendor's embedded engineers will eventually leave, transferring knowledge to client teams who may not have the depth to maintain what was built.
For enterprises that need analytics consistency without operational workflow integration, the lighter-touch semantic layers from Snowflake or Databricks may be the right tool. For those who want the full operational ontology, Palantir remains unmatched. That being said, the implementation cost and commitment are naturally, proportionally higher.
What this means for enterprise buyers
The ontology and semantic layer market is no longer a Palantir monopoly. It's a spectrum:
Analytics consistency: Snowflake Semantic Views, Looker, dbt Semantic Layer, Databricks Metric Views
Operational intelligence (emerging): Microsoft Fabric IQ
Operational intelligence (mature): Palantir Foundry
Choosing between them isn't about who invented ontologies first. It's about matching the solution to your actual problem, and being honest about your deployment capacity.
Next: A comprehensive comparison of semantic layer and ontology platforms. I dig into what each actually does, where they overlap, and how to choose.
FAQs
How do Snowflake, Databricks, Microsoft Fabric IQ and Palantir compare?
They sit on a spectrum, not in a single category. Snowflake Semantic Views, Databricks Metric Views, Looker and dbt solve analytics consistency, one trusted definition of a metric across every tool. Microsoft Fabric IQ is emerging operational intelligence, reasoning over live conditions and taking action, though still in preview. Palantir Foundry is mature operational intelligence, proven in production. What matters is matching the tier to the problem you are trying to solve.
Is Palantir still ahead in ontologies?
For full operational ontologies, yes. Palantir Foundry is production-proven and still the benchmark when you want the ontology to run the work, not just describe it. But the market has moved. Snowflake, Databricks, Google and Microsoft all now ship semantic capability, and Microsoft's Fabric IQ competes directly on operational intelligence. Palantir's real advantage is its Forward Deployed Engineering model, embedding engineers who learn the domain and build ontologies that reflect how operations actually run. That is harder to copy than the technology.
What's the difference between a semantic layer and an ontology?
A semantic layer is one facet of a wider ontology, where the ontology is the digital twin of the entire enterprise, spanning the domain model, the data that fills it and the rules and definitions on top, while the semantic layer is only that rules-and-definitions part, enforcing one governed definition of each business metric. So every tool that queries "net revenue" returns the same number. The full ontology goes further. It also operates on the data, triggering actions and workflows rather than only reading them. When Airbus runs Palantir's Skywise, the ontology describes the aircraft fleet and acts on it, scheduling maintenance rather than just reporting it.
Which semantic layer or ontology should an enterprise choose?
Start with the problem, not the vendor. If you need consistent metrics without operational workflow integration, the lighter-touch semantic layers from Snowflake or Databricks are likely the right call, and cheaper to run. If you want an ontology that operates on your business, Palantir remains the benchmark, with proportionally higher cost and commitment. Weigh the exit, too. The embedded-engineering model that makes Palantir powerful assumes those engineers eventually leave, handing over to a client team that has to maintain what was built, so plan for the deployment capacity and the lock-in before you commit.
