Context layer vs data catalog

A context layer is a place AI tools read from. Nobody browses it. When ChatGPT or an internal agent answers a question, it queries the context layer at inference, pulls the company's real definitions and rules, and reasons on those instead of guessing. The consumer is a machine, and the moment of use is the split second a tool composes an answer. That requires the context to be served over MCP, filtered by who is asking, and traceable afterward through Lineage and Audit.

The cost and setup gap follows from this. A catalog is built for a governance team to curate metadata over a quarter, which is why it is priced and scoped for companies that have such a team. A context layer self-seeds a first draft from your existing tools so a two-person data team confirms and corrects rather than authoring from a blank page, which is why it stands up in a week and is priced for the company that has to fix this now.

When a data catalog is the right call

If you are a large or regulated enterprise, if you have hundreds of tables and a data governance team whose mandate is lineage and compliance, and if analysts search a metadata catalog every day as part of their work, buy a data catalog. That is the job Atlan, Alation, and Collibra do well, and a context layer is not a replacement for it. If your primary need is human-facing metadata governance at enterprise scale, the catalog is the better fit, and the two can run side by side.

When Sento is the right call

If you are a 40-300 person company, if you have already deployed three or more AI tools, and if leadership keeps asking why those tools get the business wrong, a context layer is what you need. The problem is not that your analysts cannot find a table. The problem is that your AI tools are guessing at definitions and have no access to the rules that actually run the company. Sento writes the company down once and serves it to every tool, in a week, at a price a mid-market company can actually buy.

What it costs to be wrong

The two mistakes point in opposite directions.

If you buy an enterprise data catalog to fix AI that doesn't know your company, you spend a quarter and six figures standing up governed metadata for humans to browse, and your AI tools still guess. The catalog was never serving them context at inference. You solved a governance problem you may not have had and left the AI problem untouched. For a 100-person company, you also bought a platform scoped for an organization ten times your size.

If you buy a context layer when what you actually needed was enterprise metadata governance and compliance lineage across hundreds of tables, you have governed context for your AI but not the full cataloging and compliance apparatus a large regulated data team requires. That gap is real, but it is a gap you grow into, and most companies in the 40-200 range are nowhere near needing it yet. The first mistake costs a quarter and a budget cycle. The second is a ceiling you reach later, if at all.

What buyers tell us

"We looked at Atlan first because it was the name everyone knew. Two calls in, the honest answer from their side was that we were too small, and that it would be a multi-month rollout. We did not have a multi-month problem. Our agents were getting renewals wrong that week."

Frequently asked questions

Isn't Sento just a data catalog with a different label?
No. A data catalog documents metadata for people to find and trust. Sento serves governed context to AI tools at inference, filtered by who is asking and traceable to source. The consumer is different (machines, not people), the moment of use is different (a tool composing an answer, not an analyst browsing), and the workload is different. A catalog can seed Sento, but it was not built to be read by an agent at the moment it answers.

How does the cost actually compare at our size?
Enterprise catalogs are quote-based and typically run low-to-mid six figures a year, with a quarter-long rollout and a governance team to run them. Most 40-300 person companies are below the size these are priced for. Sento is free during early access.

Can we run both a data catalog and a context layer?
Yes, and larger companies do. The catalog governs metadata for human BI and compliance. The context layer serves governed context to AI tools. They are different consumers of your data. If you already run a catalog, Sento can seed from it.

Does a data catalog carry our unwritten rules?
Usually not. Catalogs are strong on technical metadata and a business glossary, but the operational knowledge that runs the company (how renewals get approved, which accounts are sensitive and why) tends to live in people's heads and in Slack, not in a catalog. Sento's Knowledge Capture surface exists to encode exactly that and make it canonical.

Is Atlan adding AI features, and does that close the gap?
Catalog vendors are adding AI access on top of a product built for human browsing. The gap is architectural, not a missing feature: serving governed, audited context to arbitrary agents over MCP at inference is a different workload than cataloging metadata for people to search. The size and rollout difference also stays. The catalog is still priced and scoped for the enterprise.