Warehouse-native AI ships SQL generation and a model endpoint next to your tables. Powerful primitives, but not the cause engine, the vertical context, the governance, or the operating model. And it stops at the warehouse boundary.
AI functions, inside one warehouse. Building blocks you still have to assemble.
The assembled system: the full analyst, reading across your entire stack.
They are built at different layers: warehouse AI ships primitives beside your tables; Bicycle is the assembled system that reads across every source.
Cortex, Genie, and Gemini are capabilities you assemble: a text-to-SQL function, an LLM endpoint, a forecasting routine beside your tables. Excellent building blocks, but not an analyst, and they only see what is in that one warehouse.
The assumptionYour causes live in logs, gateways, tickets, and deploys, not just the warehouse.
Bicycle is the system you would otherwise build on top of those primitives: the pattern and cause engines, vertical packs, governance, and action layer, reading from every source.
The assumptionBicycle runs on top of Snowflake or Databricks, not instead of them.
The same dip, with warehouse AI and with Bicycle. The cause spans the warehouse and three systems outside it.
Detect, Explain, Act, and Learn run across your whole stack. Warehouse AI can attempt the first two, on warehouse data, if you build it.
You can schedule queries and alerts on warehouse tables, but only on data that lives there, and you build and tune all of it yourself.
Continuous, tuned detection across warehouses, streams, and operational systems, ranked by business impact out of the box.
Text-to-SQL writes the query for you, but only after you decide what to ask: which metric, which window, which drivers. No question, no analysis, and nothing outside the warehouse.
Explain fires automatically the moment movement is detected. Cause connectors test drivers across deploys, gateways, tickets, and logs, and return a ranked cause with evidence.
A warehouse computes; it does not act. There is no governed path to Slack, Jira, webhooks, or rollbacks.
A full action registry: recommend, approve, execute, roll back, with scope, TTL, and audit on every step.
No built-in notion of accepted causes, outcomes, or reusable decision context.
Decision traces and pattern signatures accumulate, so recurring issues are recognized instantly.
This is the sharpest distinction. Warehouse AI can write the SQL to investigate, but only after a human decides there is something to investigate and frames the exact question. Bicycle's Explain runs on every detected movement, with no question asked. Manual-when-you-ask and automatic-by-default are not the same capability.
Catch it, explain it, act on it, across every system. The scorecard, and how much warehouse AI does on its own.
Warehouse-native AI is genuinely useful inside the warehouse. The gap opens at the warehouse boundary, and at the next step.
This is not either or. Bicycle runs on top of Cortex, Genie, and your warehouse, using them as the compute layer while adding the cause engine, context, governance, and operating model around them.
Native AI is in our contract.
Use it. Bicycle runs on top of Cortex, Genie, and your warehouse rather than replacing them, and with BYOC your raw data stays inside your own cloud on AWS, GCP, or Azure. What Bicycle adds is the cause engine, vertical context, governance, and the operating model around the primitives you already pay for, because the cause usually spans systems the warehouse cannot see.
Ask your team how today's setup answers each of these. The honest answers usually point to the same gap.
When a KPI moves, do your causes live inside the warehouse, or across releases, logs, gateways, and tickets?
Who builds and maintains the detection, cause logic, and action layer on top of the warehouse primitives?
When the cause sits outside the warehouse, how does warehouse AI reach it today?
Once you know what moved and why, what recommends the next step to an owner?
One pays in the orchestration you build and maintain, forever. The other ships the system on top of the warehouse you keep.
Cortex is one ingredient. You still build and own the meal.
You keep your warehouse and skip building the analyst on top of it.
The status quo is not free. Every cause that lives outside the warehouse is a blind spot, and every primitive you assemble is a system you now own.
Pick a revenue-critical metric you already track. We'll show how Bicycle detects the move, explains the cause across every system, and recommends the next step, on top of the warehouse you keep.