Every enterprise is being offered a semantic layer. Most of what's available is a point solution with a governance gap. Here's the honest comparison, what each approach does well, where it stops, and what LazyFox does differently.
Five distinct approaches to the same problem. Each gets part of the answer right. None of them make definitions canonical, deterministic, and portable across your entire stack simultaneously.
Version control captures definitions once. It has no mechanism for detecting drift, resolving conflicting interpretations across systems, or telling your AI agents which version applies in context, and it never will.
Databricks Genie One and Genie Ontology have moved fast. Analysts moved faster: an ontology doesn't verify answers, can't fix governance debt, and locks your semantic layer inside the Databricks ecosystem.
LookML gives you governed definitions inside Looker. Langdock adds a natural language interface on top. Together they're a coherent stack, for the data that's already in Looker. Everything outside is out of scope.
Microsoft Fabric IQ and LazyFox are solving the same problem, semantic governance for enterprise AI. The architectural difference is fundamental: Fabric IQ governs inside the Microsoft boundary. LazyFox governs across all systems simultaneously.
RAG is the right architecture for unstructured knowledge. When applied to business metrics, it retrieves the most similar definition, not the canonical one. Every query is a new retrieval gamble, every answer is probabilistic, and every chunk of business logic passes through a language model at token cost.
The approaches differ. The structural limitation is consistent: some govern within a single system boundary, others retrieve without governing at all. None of them make definitions canonical, versioned, and enforced across every system simultaneously.
| Capability | LazyFox | Git / YAML | Databricks Genie | Looker + Langdock | Microsoft Fabric IQ | RAG |
|---|---|---|---|---|---|---|
| Zero migration, connects to existing systems read-only | ✓ | ✓ | ✗ Data must be in Databricks | ✗ Data must be in Looker | ✗ Data must be in Fabric | ✓ Reads from any source |
| Cross-system semantic reconciliation | ✓ | ✗ | ✗ | ✗ | Partial Microsoft stack only | ✗ |
| Living governance, drift detection over time | ✓ | ✗ Static by design | ✗ | ✗ | ✗ | ✗ Static until re-indexed |
| Conflict detection before definitions are saved | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ LLM arbitrates at query time |
| Contextual / role-based definitions (Finance vs. Sales) | ✓ | ✗ | ✗ | Partial Via LookML access filters | Partial | Partial Via document filtering |
| Versioned definitions with full change history | ✓ | ✓ Via git commit log | Partial | Partial | Partial | ✗ |
| No raw data sent to an LLM at any point | ✓ | ✓ No LLM involvement | ✗ | ✗ | ✗ | ✗ Business logic in every prompt |
| Deterministic runtime, tokens consumed once at setup | ✓ | ✓ No runtime at all | ✗ Per-query token spend | ✗ Per-query token spend | ✗ | ✗ Per-query retrieval + generation cost |
| Model-agnostic, use any foundation model | ✓ | ✓ | ✗ Databricks-hosted models | Partial Langdock multi-model | ✗ Microsoft models | ✓ Any LLM backend |
| Accessible to business users without engineering involvement | ✓ | ✗ Requires code/PR skills | Partial Genie UI yes; Metric Views no | Partial LookML requires engineers | Partial | ✓ Query yes; definition authoring no |
| Health map, real-time view of semantic coverage | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Natural language query with governed, sourced answers | ✓ | ✗ | ✓ Lakehouse only | ✓ Looker data only | ✓ Fabric data only | Partial Query yes; governed no |
We'll map your current semantic layer workarounds and show you exactly what LazyFox adds, against your actual stack.