Context layer vs semantic layer
A semantic layer standardises what your data means: metric definitions, dimensions and calculations, so every report computes revenue the same way. A context layer models the system underneath that number: what depends on what, how fresh it is, who may use it, and what an action would touch. The first makes an answer consistent. The second makes it accountable.
If you are serving dashboards to analysts, a semantic layer alone is often enough. If you are letting agents read or change real systems, you need the system-level model as well, because a consistent definition does not tell an agent what a schema change will break. Increasingly the two ship together: Snowflake and Databricks now sell products in both categories.
The difference, side by side
| Semantic layer | Context layer | |
|---|---|---|
| What it models | The metric: definitions, dimensions, calculations, joins. What a number means. | The system under the metric: what depends on what, what state it is in, and what an action would touch. |
| What the consumer needs from it | One number, computed the same way for everyone who asks. | Permission, provenance and consequence before acting on that number. |
| Typical question | "How is revenue defined here?" | "What breaks if I change this, and am I allowed to?" |
| Governance it carries | Governance of the definition: who wrote the metric, when it changed, who approved it. | Governance of the action: lineage into and out of the change, freshness, policy, blast radius. |
| Failure mode | Two teams report different numbers for the same word. | An agent acts correctly on a number and still breaks something downstream. |
| Representative products | dbt Semantic Layer, Cube, AtScale, LookML, Snowflake Semantic Views, Databricks metric views. | Atlan, Snowflake Horizon Context, Databricks Genie Ontology, Alation, Collibra, Spiderbrain. |
The rows compare layers, not companies. Snowflake and Databricks ship products in both columns, and one vendor rejects the split entirely: see the next section.
What each vendor actually calls itself
Most comparisons of these two terms, including an earlier draft of this one, characterise other people’s products. That ages badly and it is usually unfair, so here is each vendor in its own words instead, with a link to the page it came from. Every quote was checked on .
| Vendor | Calls itself | In their words |
|---|---|---|
| Atlan | context layer | "the context layer for AI", and markets 690K+ AI-generated descriptions across 50+ enterprises alongside automated column-level lineage. |
| Snowflake | context layer | "Snowflake Horizon Context: The Governed Context Layer for AI, BI and Apps". It "mines lineage from Snowflake and external query logs, BI systems and OpenLineage feeds". |
| Databricks | context layer | Genie Ontology, "a new self-improving context layer" that "continuously learns". In preview as of mid-2026. |
| AtScale | semantic layer, inside the context layer | "The context layer is a stack. An ontology is one part of the stack... The semantic layer is the load-bearing component of the context layer stack." Also claims determinism: "The semantic layer is what makes AI deterministic." |
| dbt Labs | structured context | Structured context spanning "lineage... contracts, tests, and CI signals... documentation and ownership... freshness and operational metadata... policies and business rules". |
| Cube | semantic layer for AI agents | Publishes "Semantic Layer for AI Agents" and ships an MCP server so agents introspect the model directly. |
| Alation | agentic data intelligence | Does not call itself a context layer. Frames the same capability as agentic data intelligence. |
| Collibra | AI governance | Does not call itself a context layer. Frames the same capability as AI governance and an AI Command Center. |
One vendor rejects the split, and the objection is worth taking seriously
AtScale argues the two are not neighbours at all: “The context layer is a stack. An ontology is one part of the stack... The semantic layer is the load-bearing component of the context layer stack.” On that reading, asking “context layer vs semantic layer” is a category error, like asking “engine vs car”.
It is a fair objection and it is partly right. A context layer that cannot tell an agent what a metric means is missing something, and a semantic layer is the obvious way to supply it. Where we still separate them is scope: a semantic layer models the metric, and there are whole classes of system, source code being the clearest, that have no metrics in them at all and still need a governed model before an agent touches them. Both framings are defensible. If your systems are a warehouse, treat the semantic layer as the foundation. If they include code, services and documents, the layer you need is broader than metrics.
The question neither term settles: how much of it is derived?
The distinction that changes what you can rely on is not the layer name. It is where the context comes from. There are three production methods, and the honest version of this section is that every serious vendor uses all three.
- Certified. A person, or an agent acting for one, approves the entry, and the entry is true because it was approved. Modern catalogs now generate a large share of those entries automatically and route them for approval, which solves the volume problem and leaves the trust question where it was.
- Inferred. A model proposes the context. This reaches systems nobody documented, and the output is a probability, so two runs can disagree and neither can be re-checked.
- Derived. The context is computed from the systems themselves by a fixed procedure. Same input, same output. Catalogs have done this for years for one part of the graph: Snowflake says Horizon Context “mines lineage from Snowflake and external query logs”, and mined lineage is derivation.
So “we derive it” on its own is not a differentiator, and any vendor claiming it as one, including us in an earlier version of this page, is overstating. Two narrower questions do separate products, and they are the ones worth asking on a call:
- How much of the model is derived rather than asserted? Mined lineage is one subgraph. Whether the rest of the model, the parts an agent reasons over, is derived or hand-maintained is a different question with a different answer per vendor.
- Can someone outside the vendor re-run the derivation and get the same result? This is the one almost nobody answers yes to. Reproducibility that is an internal implementation detail is not the same as reproducibility you can verify.
Where Spiderbrain sits
Plainly, so this page is useful even if you never buy anything: Spiderbrain is a context layer, not a semantic layer. It does not author or serve BI metric definitions or business glossaries, and it will not compute your quarterly revenue. If that is the problem you have, a semantic layer is the correct tool and Spiderbrain is not a substitute for it.
What Spiderbrain answers yes to is the second question above. It is a deterministic context layer: the same input produces a byte-identical brain, and any answer that brain gives can be shown with the dependency path it rests on and re-run. The transparency log publishes the fingerprint of every published brain, so the reproducibility claim is checkable by someone who does not work here rather than asserted. That is what answers with receipts means, and it is the practical form of turning what a system already knows into executable intelligence.
Substrate matters too, and it is worth being exact: what ships today is software and code, plus database schemas read from a SQL file. Spreadsheets, people, processes and industry-specific records run as proofs of concept, not as shipped capability. See what Spiderbrain is for the longer explanation, or the API to query a governed understanding from your own software.
Common questions
What is the difference between a semantic layer and a context layer?
A semantic layer standardises what your data means: metric definitions, dimensions and calculations, so every report computes revenue the same way. A context layer models the system underneath that number: what depends on what, how fresh it is, who may use it, and what an action would touch. The semantic layer answers what the number is. The context layer answers whether you are allowed to act on it and what breaks if it changes. Note that vendors disagree about the shape of this relationship: AtScale argues the semantic layer is a component inside the context layer stack rather than a neighbour to it.
What is a context layer?
A context layer is the governed model of a system that sits between your systems and the AI acting on them. It holds the relationships, ownership, freshness, constraints and decisions that make an answer safe to use, and serves them to agents over an interface such as MCP or an API. Without one, an agent has retrieval but no guardrails: it can find a fact and still have no idea whether that fact is current, load-bearing, or permitted.
Do I need both a semantic layer and a context layer?
Usually yes, and increasingly they ship together. If you are serving dashboards to analysts, a semantic layer alone is often enough. If you are letting agents read or change real systems, you need the system-level model as well, because a consistent definition does not tell an agent what a schema change will break. Snowflake and Databricks now ship products in both categories, which is the clearest signal that this is not an either-or choice.
Is a context layer the same as a data catalog?
Catalogs are the closest established category, and several catalog vendors now describe themselves as context layers: Atlan calls itself "the context layer for AI". The difference worth asking about is not the label but how much of the context is derived from the systems themselves rather than asserted, and whether that derivation can be re-run by someone who does not work for the vendor.
What is a deterministic context layer?
A context layer whose model is reproducible: the same input produces a byte-identical model, so the same question returns the same answer, and the answer can be shown with the path it rests on and re-run to check it. Determinism is a contested word in this category, and worth pinning down when a vendor uses it. AtScale uses it to mean a semantic layer removes the model’s guesswork about metric definitions, which is a real and different claim. Spiderbrain uses it to mean the model itself rebuilds byte-identically and publishes a fingerprint you can verify against.
Is a semantic layer for AI the same thing as a context layer for AI?
They are often used interchangeably in vendor marketing, which is the main reason the question gets asked so often. The useful distinction is scope: a semantic layer for AI exposes existing metric definitions to a model so text-to-SQL produces consistent numbers. A context layer for AI adds the structure and governance around those numbers, so the model knows provenance, permission and consequence. The first makes an answer consistent; the second makes it accountable.
Third-party product names are used for identification and comparison only, and every characterisation above is either a direct quote or linked to the vendor’s own page. If something here is wrong or out of date, tell us at support@spiderbrain.ai and we will correct it and say that we did.