Looker Gemini

Looker Gemini is Google Cloud’s generative AI capability for Looker, designed to combine conversational AI with Looker’s business intelligence, semantic modeling, data exploration, visualization, and reporting tools. It uses Google’s Gemini models to help business users and analysts interact with business data through natural language while keeping Looker’s semantic layer at the center of the analytics experience. Google introduced Gemini in Looker at Google Cloud Next in April 2024, initially with capabilities including Conversational Analytics, report generation, formula assistance, LookML assistance, visualization assistance, and automated Google Slides generation.

One of its main capabilities is Conversational Analytics. Users can ask questions about business data using natural language instead of manually building queries. Gemini interprets the question using Looker’s semantic model and can return data tables, charts, and explanations. Users can continue with follow-up questions, making the experience more conversational than traditional dashboard-based analytics.

The LookML semantic layer is an important part of Looker Gemini. Looker uses centrally defined metrics, dimensions, relationships, and business logic to provide context for AI-generated answers. This helps organizations maintain consistent definitions across teams and reduces the risk of different users receiving answers based on conflicting business logic. Google says its internal testing found that the semantic layer can reduce errors in generative-AI natural-language queries by as much as two thirds.

Gemini in Looker also assists analysts and developers with LookML development. LookML Assistant can generate dimensions, dimension groups, and measures from natural-language descriptions. Instead of manually writing every part of a LookML model, developers can describe what they want to create and review the generated code. This can speed up semantic-model development while keeping the resulting work inside Looker’s governed modeling environment.

Formula Assistant provides similar assistance for calculations. Analysts can describe a desired calculation in natural language, and Gemini can generate a formula for calculated fields and other supported Looker expressions. This is useful for ad-hoc analysis where users need a calculation without remembering the exact expression syntax.

Visualization Assistant allows users to describe how they want a visualization customized. Gemini generates the required JSON configuration for supported visualizations, allowing analysts to make changes using natural-language instructions instead of manually editing visualization configuration.

Automatic Slide Generation connects Looker analytics with Google Slides. Gemini can transform Looker reports into presentations and generate summaries explaining important findings in charts. Because the presentations are connected to the underlying reports, the information can remain connected to current analytical content instead of requiring users to manually rebuild every slide.

Gemini in Looker also supports AI-assisted report creation and data storytelling. Google describes capabilities that can help users generate reports, visualizations, summaries, and narratives from natural-language instructions. These features are designed to reduce the amount of manual work required to move from raw business data to understandable reports.

The platform has an agentic architecture behind its conversational analytics experience. Google describes the Looker Conversational Analytics API as an agentic backend that uses a reasoning agent and multiple tools to answer analytical questions. It can use conversation history for multi-turn questions and can work with Looker queries and the Explore experience. Google also describes a dynamic knowledge graph for retrieval-augmented generation and models fine-tuned for SQL and Python generation.

Privacy and governance are important aspects of the product. Gemini in Looker works with Looker’s existing semantic modeling and governance approach, while Google states that customer prompts and generated outputs are not used to train Google’s generative AI models. Organizations can also control Gemini features through Looker administration and permissions.

Looker Gemini is part of the broader Google Cloud data and AI ecosystem. It can work with databases and data warehouses supported by Looker, including BigQuery, Snowflake, Amazon Redshift, Databricks, PostgreSQL, MySQL, SQL Server, Oracle, SAP HANA, Teradata, Trino, and many other database technologies. Looker also integrates with Google services such as BigQuery, Google Analytics, Google Ads, Google Drive, Google Sheets, Gmail, Cloud Storage, Cloud SQL, and Google Spanner.

The product is particularly suited to organizations that already use Looker for governed business intelligence and want to add generative AI without abandoning their existing semantic models, metrics, permissions, and analytics workflows. It can support business users who want easier access to data as well as analysts and developers who need assistance building models, formulas, reports, and visualizations.

Gemini in Looker is therefore better understood as an AI layer within Looker rather than a standalone chatbot. Its strongest value comes from combining natural-language interaction with governed enterprise data, semantic modeling, visualization, reporting, and Google Cloud integrations.

Pricing

Looker Gemini does not have a separate standalone public subscription price. Gemini in Looker is part of Looker’s platform and its conversational analytics capabilities are subject to the applicable Looker platform subscription and data-token usage structure.

Current Looker (Google Cloud core) editions include Standard, Enterprise, and Embed. Platform pricing is generally provided through annual commitments and custom quotes. Looker subscriptions include monthly data-token allocations for conversational analytics.

Pricing Note

As of October 2026, Looker’s pricing documentation states that conversational analytics usage is measured using input and output data tokens. Standard includes 60 million input and 1.2 million output data tokens per month, Enterprise/Advanced includes 300 million input and 6 million output tokens, and Embed/Elite includes 1.2 billion input and 24 million output tokens. Additional usage is billed at $3 per million input tokens and $20 per million output tokens.

Review

Looker Gemini is a strong option for organizations that want generative AI directly connected to governed business intelligence. Its biggest advantage is the combination of Gemini with Looker’s semantic layer. Rather than relying solely on an AI model to interpret raw database information, the system can use centrally defined business metrics and relationships as analytical context.

Conversational Analytics is particularly useful for business users because it reduces the need to understand SQL or complex dashboard structures. Analysts also benefit from LookML Assistant, Formula Assistant, Visualization Assistant, and automated presentation capabilities.

Another major strength is its Google Cloud ecosystem. Organizations already using BigQuery, Google Workspace, Google Slides, Google Sheets, Google Analytics, or other Google Cloud services can integrate Gemini in Looker into an existing data environment.

However, Looker Gemini is not designed primarily as a standalone consumer AI assistant. It is an enterprise analytics capability that depends on Looker, properly modeled data, permissions, and supported infrastructure. The quality of its answers can also depend heavily on the quality and completeness of the underlying semantic model.

Pricing can also become more complex because customers pay for the Looker platform and must consider conversational analytics data-token consumption. Organizations with significant AI usage therefore need to monitor token consumption and potential overage charges.

For businesses already invested in Looker, Gemini provides a compelling way to make governed analytics more conversational and accessible. For organizations without a Looker environment, the broader platform commitment may make it less attractive than standalone AI analytics products.