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. That governance is Looker's strength, and it means a developer writes LookML before anyone explores. Dataki starts from the schema as it is: ask in plain English, and it writes SQL you can read and edit.
Why Dataki?
- No modelling layer before the first answer: connect a source with a read-only user and ask.
- The SQL is shown and editable with every answer, so anyone who reads SQL can check the logic.
- Metric definitions, joins and column warnings live as plain-language notes per source, which the model reads before it writes SQL.
- Priced on data sources and AI messages, not seats. Viewers are free.
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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
Where it gets in the way
- 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
What each one asks of you, what it keeps, and where your data is queried.
| Row | DATAKI | Looker |
|---|---|---|
| How you ask a question | DATAKI Type the question in plain English. Dataki writes the SQL, runs it and draws the chart, and the SQL is shown with the answer for you 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, whether or not they write SQL. Anyone can view a dashboard for free. | 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 On your database or warehouse, through a read-only connection, when the question is asked or the dashboard refreshes. Uploaded CSV and Excel files are snapshots. | 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. No modelling layer: optional notes per source (metric definitions, joins, column warnings) that the model reads 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 draws the answers as charts. Credentials never reach the model. | 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 limited to their own rows. | 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, not seats. Everyone on the team can build, and viewers are free. | Looker Editions (Standard, Enterprise, Embed), with users licensed by type: Developer, Standard or Viewer. |
Which one to choose
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 one Dataki does not connect to, such as 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 change faster than a semantic model can be updated.
- 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?
- Not for teams whose main need is governed metrics. Looker's LookML model defines each measure and join once and generates every query from it; Dataki has no modelling language. Dataki keeps plain-language notes per source — metric definitions, joins, column warnings — that its model reads before writing SQL, and shows the SQL with every answer so it can be checked. For teams without a LookML model, Dataki answers questions without building one first.
- Can Dataki connect to the same databases as Looker?
- Some of them. Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files. Looker supports many more SQL dialects, including Snowflake, which Dataki does not. Where both reach the same warehouse, each queries it in place rather than copying the data out.
- 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.
Ready to move on from Looker?
Connect a data source, ask a question in plain English, and share the dashboard. No modelling layer to build first.