clariBI vs. Looker: when each tool is the better choice
Looker is built around a governed LookML model maintained by a data team. clariBI connects business apps directly and answers questions in plain English. Where each one fits, where each one falls short, and a quick way to decide.
Looker and clariBI both end in dashboards and answers, but they start from opposite ends. Looker starts from a model of your data that a data team writes and maintains. clariBI starts from the apps you already use and skips the model. Which one fits depends mostly on whether you have, or want, the people and the warehouse that Looker assumes.
What Looker is
Looker is Google Cloud's business intelligence platform. Its core is LookML, a modeling language in which developers describe tables, joins and metric definitions once. Everyone else explores data through that model, so "active customer" or "net revenue" means the same thing in every chart. Queries run in your database or warehouse, such as BigQuery, Snowflake or PostgreSQL.
It is aimed at companies with a data warehouse and at least one person whose job includes maintaining LookML. It is licensed through Google Cloud sales.
What clariBI is
clariBI connects directly to business tools: Stripe, HubSpot and PostHog as MCP apps, Google Analytics, Google Ads and Meta Ads through Google and Meta sign-in, 80 REST API connectors, file uploads, and PostgreSQL, MySQL, SQL Server and Oracle databases. Each source gets a dashboard built automatically. On the Trial and on Starter and above you can ask questions in plain English: a language model plans the calculation, the arithmetic runs as code on your synced data, and any narrated figure the chart data cannot back is removed. There is no modeling layer and no SQL.
Plans are published: Free, Lite at $19, Starter at $99, Professional at $199 and Enterprise at $999 a month, per organization rather than per user. The pricing page lists what each includes.
Where Looker is the better choice
- One governed definition of every metric across a large company. If finance, sales and marketing must agree on exactly how revenue is calculated, and many teams build on those definitions, a reviewed semantic model is the right tool. clariBI has nothing equivalent.
- You already have a warehouse and a data team. Looker is designed to sit on top of clean warehouse tables. If those exist, its model gives analysts a controlled way to expose them.
- Analytics embedded in your own product. Looker has an edition for embedding dashboards inside software you sell. clariBI does not offer an embedding product; the closest things are read-only public links (Professional and Enterprise) and a REST API.
- SQL-level control. Looker's developers can write SQL and version the model. clariBI does not let you write SQL.
- Very large tables. Looker queries the warehouse where the data lives. clariBI syncs data into its own store, with per-plan row caps on database tables, so for large event tables you connect an aggregated view rather than the raw table (the database connection guide explains how).
Where clariBI is the better choice
- No data team, no warehouse. If your numbers live in Stripe, HubSpot, Google Analytics and a few spreadsheets, clariBI connects to those directly. There is no modeling project before the first chart.
- Questions from people who do not write queries. A founder or marketer can ask "What was ad spend by channel last month, Google Ads versus Meta Ads?" and get a chart and a short explanation.
- Questions that cross sources. clariBI lines up sources by date or by a shared column such as campaign name, and can correlate a metric in one source with a metric in another. The multi-source dashboards guide explains how.
- Forecasting built in from Starter: several methods are backtested on your history, the best one is used, and the result shows 50, 80 and 95% ranges. The maths is statistics code, not a language model.
- A published monthly price you can check before talking to anyone, and a 14-day trial with no card.
The trade-off in one table
| Looker | clariBI | |
|---|---|---|
| Starting point | A LookML model of your warehouse | The apps and files you already use |
| Who builds it | Developers who maintain LookML | Whoever connects the sources |
| Metric definitions | Governed centrally in the model | Per source; you document them yourself |
| SQL | Yes, for developers | No |
| Plain-English questions | Yes (conversational analytics) | Yes, on Trial, Starter and above |
| Pricing | Through Google Cloud sales | Published, $0 to $999 a month |
For a longer feature and pricing table, see the clariBI vs. Looker comparison page.
How to decide
Pick Looker if you have a warehouse with clean tables, someone who will own the semantic model, and many teams who need the same governed definitions. The investment pays off at that scale.
Pick clariBI if your data lives in operational tools, nobody on the team maintains a data model, and you want answers from Stripe, HubSpot or GA4 this week rather than after a modeling project.
If you are unsure, the guide to picking a first BI tool for a small startup walks through the questions in more detail. Or test it directly: start a trial, connect the one tool your weekly numbers come from, and ask the question you currently answer by hand every Monday. If the answer matches your spreadsheet, you have your comparison.