Is Dataki a replacement for Looker?

For teams that want answers without building a LookML model first, yes. Dataki writes SQL against your schema, guided by plain-language notes per source — metric definitions, joins, column warnings — and shows the SQL with every answer so it can be checked. Teams whose first priority is metrics defined once in code and enforced in every query are the natural fit for Looker's LookML model.

Can Dataki connect to the same databases as Looker?

Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files. Looker supports more SQL dialects, including Snowflake. Where both reach the same warehouse, each queries it in place, with no copy of the data.

Does Looker have an AI assistant?

Yes. Gemini in Looker includes Conversational Analytics, which answers natural-language questions using the LookML model as its source of truth, along with help writing formulas and customising visualizations, and LookML generation in preview. Because it answers through the model, it covers what the model defines. Dataki writes SQL against the schema directly, guided by the notes kept for each source.

Does Looker work with Claude or Cursor?

Yes, through MCP. A Looker-managed MCP server, in preview, lets clients such as Claude Desktop and Cursor work with a Looker instance and its LookML models, and Google's open-source MCP Toolbox for Databases has Looker tools for querying Explores. Dataki's MCP server does the same for the sources connected to Dataki, and saves the results as Dataki dashboards.

Dataki vs Looker

A Looker alternative that needs no LookML first

Looker puts a LookML model between your warehouse and your users, so each metric and join is defined once and every query is generated from it, written by a developer before anyone explores. Dataki works with your schema as it is today: ask in plain English, and it writes SQL you can read and edit.

Why Dataki?

First answer in minutes: connect a source with a read-only user and ask.
Every answer shows its SQL, editable, so anyone who reads SQL can check the logic.
Metric definitions, joins and column warnings live as plain-language notes per source, and Dataki reads them before it writes SQL.
Priced on data sources and AI messages. Viewers are free.

Where Looker stands

What it does well

LookML defines every dimension, measure and join once, in code
LookML projects are versioned in Git, with a development mode per developer
Queries run in the database; Looker generates SQL from the model rather than extracting data
Gemini in Looker answers plain-English questions from the LookML model

Trade-offs to weigh

A developer writes LookML before business users can explore
Only Developer users change the model; Standard and Viewer users work inside it
A question the model doesn't cover means a LookML change, or SQL in SQL Runner
The Looker-managed MCP server is still in preview

Dataki and Looker at a glance

How each one works, what you keep, and where your data is queried.

How Dataki and Looker compare, row by row
Row	DATAKI	Looker
How you ask a question	DATAKI Type a question in plain English. Dataki writes the SQL, runs it and charts the answer, with the SQL right there to edit and re-run.	Looker Pick dimensions and measures in an Explore. Conversational Analytics (Gemini) answers plain-English questions from the LookML model.
Who can build	DATAKI Everyone on the team, SQL or no SQL. Viewers are free and unlimited.	Looker Developer users change the LookML model; Standard users build Looks and dashboards; Viewer users view, filter and download.
What you keep	DATAKI A live dashboard on a permanent link that refreshes on its own, with filters and a date range.	Looker Looks and dashboards built on Explores, backed by the LookML model.
Where the data is queried	DATAKI Live on your database or warehouse, read-only, every time a question is asked or a dashboard refreshes. CSV and Excel uploads update when you upload a new export.	Looker In the database: Looker generates SQL from the model and runs it against the connection.
Setup before the first chart	DATAKI Connect a source with a read-only user and start asking. Add metric definitions, joins and column notes per source whenever you like; Dataki reads them before it writes SQL.	Looker A LookML project: views with dimensions and measures, a model with joins and Explores, versioned in Git.
AI assistants and MCP	DATAKI A remote MCP server on every plan. Claude, ChatGPT, Cursor and VS Code query your sources and save dashboards, and Claude shows the answers as charts. Credentials stay inside Dataki.	Looker Gemini in Looker: Conversational Analytics, formula and visualization help, LookML generation (preview). A Looker-managed MCP server is in preview, and MCP Toolbox for Databases has Looker tools.
Embedding	DATAKI A <dataki-dashboard> web component, a React component or an iframe, with each of your customers seeing only their own data.	Looker Signed embedding in an iframe, private embedding behind a Looker login, and an Embed SDK.
Pricing model	DATAKI By data sources and AI messages. Everyone on the team builds, and viewers are free.	Looker Editions (Standard, Enterprise, Embed), with users licensed by type: Developer, Standard or Viewer.

Which one fits your team

Choose Looker if…

You need metrics defined once in code and enforced in every query, with changes reviewed in Git.
You already have a LookML model and developers who maintain it.
Your warehouse is Snowflake.
You embed analytics with each user's permissions carried in a signed URL.

Choose Dataki if…

You want answers from your warehouse without building a LookML project first.
Your questions move fast, and you want answers at the same speed.
You want the SQL behind each answer visible and editable by anyone who reads SQL.
You want Claude, ChatGPT or Cursor to query the same BigQuery, PostgreSQL, MySQL or Redshift sources and save dashboards.

Questions about Looker and Dataki

Is Dataki a replacement for Looker?: For teams that want answers without building a LookML model first, yes. Dataki writes SQL against your schema, guided by plain-language notes per source — metric definitions, joins, column warnings — and shows the SQL with every answer so it can be checked. Teams whose first priority is metrics defined once in code and enforced in every query are the natural fit for Looker's LookML model.
Can Dataki connect to the same databases as Looker?: Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files. Looker supports more SQL dialects, including Snowflake. Where both reach the same warehouse, each queries it in place, with no copy of the data.
Does Looker have an AI assistant?: Yes. Gemini in Looker includes Conversational Analytics, which answers natural-language questions using the LookML model as its source of truth, along with help writing formulas and customising visualizations, and LookML generation in preview. Because it answers through the model, it covers what the model defines. Dataki writes SQL against the schema directly, guided by the notes kept for each source.
Does Looker work with Claude or Cursor?: Yes, through MCP. A Looker-managed MCP server, in preview, lets clients such as Claude Desktop and Cursor work with a Looker instance and its LookML models, and Google's open-source MCP Toolbox for Databases has Looker tools for querying Explores. Dataki's MCP server does the same for the sources connected to Dataki, and saves the results as Dataki dashboards.

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