
Databricks SQL
A serverless intelligent data warehouse built on lakehouse architecture that natively integrates AI.

The open-source framework for building data-driven AI applications and embedded analytics.

Latitude is a high-performance, open-source framework designed for AI teams to bridge the gap between data warehouses and large language models (LLMs). By utilizing a SQL-first approach, Latitude allows developers to build data-driven applications where the AI can query, analyze, and visualize data in real-time. The platform’s architecture focuses on composability, offering a suite of UI components for embedding analytics directly into SaaS products. In the 2026 market landscape, Latitude differentiates itself by providing a robust middle-layer that handles the complexities of data fetching, prompt engineering, and result caching. It integrates natively with modern data warehouses like Snowflake and BigQuery, and supports orchestration across major LLM providers including OpenAI, Anthropic, and Google Vertex AI. Its version-control-friendly structure ensures that data schemas and prompt templates remain synchronized, making it an essential tool for enterprise-grade AI agents that require high data accuracy and low latency execution.
Latitude is a high-performance, open-source framework designed for AI teams to bridge the gap between data warehouses and large language models (LLMs).
Explore all tools that specialize in develop ai applications. This domain focus ensures Latitude delivers optimized results for this specific requirement.
Explore all tools that specialize in visualize real-time data. This domain focus ensures Latitude delivers optimized results for this specific requirement.
Explore all tools that specialize in dashboard generation. This domain focus ensures Latitude delivers optimized results for this specific requirement.
Allows developers to embed dynamic SQL results directly into LLM prompt templates using curly-brace syntax, ensuring the AI always has the freshest data context.
Integrates with tools like dbt or Cube to leverage pre-defined business logic and metrics within the AI workflow.
An intermediate caching mechanism that stores SQL query results and LLM responses to reduce latency and API costs.
A set of headless UI components designed to render complex data visualizations generated by AI models.
Enables switching between GPT-4, Claude 3.5, and local Llama models based on task complexity or cost constraints.
Built-in logs that track the full lifecycle of a request: from the SQL query execution to the final LLM output.
All configurations, prompts, and queries are stored as code, allowing for standard CI/CD practices and peer reviews.
Install the Latitude CLI via npm or brew.
Initialize a new project using 'latitude init'.
Configure your database connection (Postgres, Snowflake, or BigQuery) in the .env file.
Create a new data source definition in the /sources directory.
Write a SQL query file to define the data fetch logic for your AI application.
Define a prompt template that references the SQL results using variables.
Configure the LLM provider and model parameters in the latitude.config.js.
Use the Latitude React/Vue components to embed the output into your frontend.
Test the end-to-end flow using the integrated Latitude local development server.
Deploy the project to Latitude Cloud or self-host via Docker.
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