Short version: a semantic layer defines your metrics, a data catalog documents your data for people, and a context layer serves your meaning and rules to AI tools at the moment they answer. They overlap at the edges, but they were built for different consumers, and only one was built for AI.
If you are trying to make your AI tools reliable and wondering whether the semantic layer or catalog you already own covers it, this is the comparison you want.
Semantic layer: great at metrics, built for BI
A semantic layer like dbt Semantic Layer or Cube does something genuinely valuable. It defines a metric once, "revenue," "active user," "MRR", so every BI tool returns the same number. If your dashboards used to disagree, a semantic layer is how you fix that.
Where it stops for AI: it covers metrics, and only metrics. It does not carry the rules that run your business or the institutional knowledge that isn't a number. And it serves BI tools. The fifteen AI tools that are not your dashboard cannot read it, and it was never designed to resolve context for them at inference.
A semantic layer is one slice of a context layer, the "what your data means" slice, pointed at BI instead of at AI.
Data catalog: great at governance, built for people
A data catalog like Atlan, Alation, or Collibra is the system of record for your data estate. It handles discovery, documentation, lineage, and governance. For a large organization with a big data team, it is how you keep thousands of tables understandable and compliant. That is real, and it is hard.
Where it stops for AI: a catalog was built for people to browse and for governance workflows, not to serve context to an agent at the moment it answers. Most catalogs are now adding AI features, which is genuine, but the architecture underneath is human-and-BI-first: describe the data, manage the metadata, support the governance team. It documents what your data is. It does not resolve what your company means and hand it to whatever tool is asking, filtered by who is asking, with the answer traceable.
And the practical reality of scale: catalogs tend to be longer, enterprise-shaped rollouts aimed at larger data organizations. That is often the wrong fit for a 40-to-200-person team that just needs its AI to stop getting the business wrong.
Context layer: built for AI, at inference
A context layer starts from a different question: not "how do humans understand this data," but "how does an AI tool get the right answer when it runs." (We define it in full in "What Is a Context Layer for AI?")
It carries three things, not one: what your data means, how your company works (the unwritten rules), and who can see what. It serves all of it to any AI tool over MCP at the moment the tool answers, filtered by the requester, with every answer traceable to its source.
The difference is the consumer and the moment. A semantic layer answers a dashboard. A catalog answers a person. A context layer answers an agent, while it is working.
When you need which
Be honest about your problem.
- Your dashboards disagree on a number. You want a semantic layer.
- You have thousands of tables and a governance mandate. You want a data catalog.
- Your AI tools keep getting your business wrong in production. You want a context layer.
These are not mutually exclusive. A context layer can read your semantic-layer metrics and your catalog's metadata as inputs. It sits closer to the AI tools and does the job neither was built for: resolving governed meaning and serving it at inference.
The bottom line
If the problem you are solving is "my AI doesn't know my company," a semantic layer and a data catalog will each get you part of the way and then stop, because neither was built to serve governed context to AI when it answers. That is the job a context layer exists to do.
AI that knows your company. That is what we are building Sento to be.
Frequently asked questions
What is the difference between a context layer and a semantic layer?
A semantic layer defines metrics for BI tools. A context layer carries your definitions, your unwritten rules, and your access policy, and serves all of it to any AI tool at the moment it answers. The semantic layer's metrics are one slice of a context layer.
Is a context layer the same as a data catalog?
No. A data catalog documents and governs your data for people and governance teams. A context layer resolves what your company means and serves it to AI tools at inference, filtered by who is asking and traceable to source. A catalog describes data; a context layer serves context.
Can a context layer replace my semantic layer or catalog?
Not necessarily. They solve different problems and can work together. A context layer can read your semantic-layer metrics and catalog metadata as inputs, and adds the rules, the access policy, and the inference-time serving that AI tools need.
Do I need a data catalog to make my AI reliable?
No. Catalogs are built for human discovery and governance at large scale and tend to be enterprise-shaped rollouts. If the goal is making your AI tools reliable, a context layer targets that directly and is far lighter to stand up.
