What is the business case against a warehouse plus BI stack?
Around 80% lower total cost. A BI tool needs a warehouse, an ingestion tool and engineers underneath it; we query your systems in place, so you pay for none of them. At 250 users that is about $70K a year against $341K to $1.08M for a typical BI stack.
Two parts: what you stop paying for, and the time you get back.
Cost: around 80% lower, on the whole stack
A BI tool is only the top layer. To answer a question with one, you also pay for a warehouse to hold a copy of your data, an ingestion tool to fill it, and engineers to build and run the pipelines. Published figures put that layer at about $278,000 a year: the median Snowflake buyer pays $100,000 (Vendr, 731 purchases), the median Fivetran buyer $48,261 (Vendr, 768 purchases), and one data engineer earns $129,700 on average in the U.S. (ZipRecruiter, August 2026, base pay only).
At 250 users, dataface.ai Business is about $70,000 a year, all in. The cheapest BI stack, Power BI on top of that layer, comes to about $341,000. A Looker stack runs up to about $1.08 million. That is 79% to 94% lower, because we query the systems you already run and you pay for none of the layer underneath.
Time: no pipeline project, no queue
The other cost is time. A warehouse and its pipelines take months to set up before the first chart, and every new question then waits in an analyst's queue. There is nothing to build first with dataface.ai, and questions that take an analyst days come back in minutes.
Where the moat is, for an investor reading this
Not the model. Anyone can call a model. The defensible parts are the connectors and live schema discovery, the human-in-the-loop layer that refuses to guess when a question is ambiguous, the dual-gate authorization that makes the architecture reviewable, and the domain tuning for revenue cycle, supply chain and finance. Those compound. Prompt quality does not.
Where this comes from
See it against your own data
A pilot is scoped to one governed use case and time-boxed to eight to twelve weeks, with success criteria agreed before it starts.
Get started freeRelated questions
- What is dataface.ai?dataface.ai is on-source conversational analytics. Ask a question in plain English, and it generates read-only SQL from your schema metadata and runs it on the systems you already own. No warehouse copy, no pipeline.
- How do you run analytics without moving our data?dataface.ai reads your schema and a few example values per column, generates read-only SQL, and executes it on the source system itself. Your tables are never copied: no pipeline, no warehouse copy, no second place to secure.
- What does the LLM actually see from our data?By default the model sees your catalog: table names, column names, types, and a few example values per column. It never receives your tables, and it only sees query results if a user turns on AI analysis.
- How do you stop an AI from writing to or damaging our database?Generated SQL is SELECT-only. INSERT, UPDATE, DELETE and DDL are blocked, queries are parameterized against injection, and every request passes both a workspace gate and a datasource gate.