Skip to content
Saiku Cloud

Quickstart

The fastest path from sign-up to a working cube in Saiku Cloud. Prefer to test-drive first? Skip to the No-signup walkthrough — same UI, same demo data, five screenshots, no card.

Prerequisites

  • A Postgres, Snowflake, BigQuery, ClickHouse, or MotherDuck warehouse with read access for a service user. (If you don’t have one, you can upload a file instead.)
  • A web browser. That’s it.

Five steps

  1. Sign up. Go to cloud.saiku.bi and create an account. We’ll provision your tenant, a default workspace, and an initial API key on your behalf.

  2. Connect a warehouse. Open Connections in the sidebar and click Add connection. Pick your dialect, fill in the details, and click Test connection. Once the test passes, click Save.

  3. Draft a cube. Open the Schema designer. Pick your warehouse and the table that holds your facts. Type a one-line description of the cube you want (e.g. “revenue and order count by region and date”). Claude proposes a Mondrian cube; you review measures, dimensions, and joins inline.

  4. Save the schema. Once the proposal looks right, click Render XML then Save schema. The cube appears on the Schemas page and is ready to query.

  5. Query the cube. Open Analyze and click Open in Saiku on your new cube. You land in the Saiku UI with the cube preloaded — drag dimensions and measures onto rows and columns, save the workbook, share with teammates.

What’s next

  • Wire an agent — see Authentication for the API surface.
  • Bring your team in — invite members from Members on Team and above.
  • Pick a plan — compare tiers on the What’s included page.

No-signup walkthrough

Two ways to try Saiku without signing anything:

  • Live demo: demo.saiku.bi — the bundled FoodMart cube, admin / admin, no card, no signup. Everything below happens against this instance.
  • Local Docker: docker run -p 8080:8080 ghcr.io/spiculedata/saiku:latest — pull the same image the demo runs. Full setup in the self-hosting guide.

Either path takes you to the same UI. Here’s what a first query looks like from cold start.

1. Sign in

Land on the workbench sign-in. Default demo credentials pre-fill the username; type admin in the password field.

Saiku sign-in page: Username 'admin', empty Password field, Sign in button

2. Skip the tour, land on the empty workbench

On first sign-in a four-step tour offers to walk you through the workspace. If you already know your way around cubes, hit Skip — you land on the empty workbench with a Cubes dropdown on the left and drop zones for Measures, Columns, Rows, and Filter on the right.

Empty workbench: cube picker on the left, empty Measures/Columns/Rows/Filter shelves on the right

3. Pick a cube

The demo ships with the FoodMart cube (a decades-old OLAP fixture that ships as a Mondrian sample). Open the Cubes dropdown and pick Sales. The schema tree populates on the left with measures (Unit Sales, Store Sales, Store Cost, Sales Count, Customer Count, Promotion Sales) and dimensions (Customer, Product, Time, Store, Promotion, and more).

Schema tree loaded: Measures at top, Dimensions below with expandable hierarchies

4. Build a query

Two drops:

  • Drag Store Sales from the Measures list onto the Measures shelf on the right.
  • Drag Product Family from the Product dimension onto the Rows shelf.

The Run button unlocks the moment both shelves have content.

Shelves populated: Store Sales on Measures, Product Family on Rows, Run button now enabled

5. Run

Click Run. The query compiles to MDX, executes against the Mondrian engine, and returns a grid. Drink $48,836.21 · Food $409,035.59 · Non-Consumable $107,366.33. Round-trip 46ms against the bundled H2 warehouse.

Query results: three-row grid with Product Family on rows, Store Sales values in the cells

6. Swap to a chart

Same query, different view. Click the Chart tab above the result grid. The default chart type is a bar chart, colored by series, no configuration required. Switch chart types, add comparisons, or drop new dimensions from the dimension tree to pivot instantly.

Same query as a bar chart: three bars for Drink, Food, Non-Consumable with Store Sales values

What just happened

  • The workbench built a typed Mondrian query from the dimensions and measures you dropped on the shelves. No SQL, no MDX in the request body.
  • The Mondrian engine translated the request to SQL, ran it against the connected warehouse (in the demo, an in-process H2 database seeded with FoodMart), and cached the result.
  • The grid + chart are two views of the same result — dropping another dimension or swapping measures re-runs the query and refreshes both.

You’ve just done the same thing your analysts and finance team do every day. Every downstream Saiku surface — Excel via XMLA, dashboards, MCP for AI agents, saved workbooks — reads from the same cube definition.

What’s next after the walkthrough

  • Connect Excel — the same demo instance publishes an XMLA endpoint at https://demo.saiku.bi/xmla. Excel connects the same way as any Analysis Services source; see the Excel guide.
  • Connect an AI agent via MCP — the MCP server at /rest/saiku/api/mcp gives Claude Desktop, Cursor, Cline, or any MCP-speaking tool the same six tools your query surface uses. See the MCP guide.
  • Move to your own data — sign up above, connect a real warehouse, and repeat.