Best Agentic AI Platforms for Azure Databricks

Find and compare the best Agentic AI platforms for Azure Databricks in 2026

Use the comparison tool below to compare the top Agentic AI platforms for Azure Databricks on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

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    NLSQL Reviews

    NLSQL

    NLSQL

    $987/month/unlimited users
    NLSQL is a B2B SaaS solution designed to enable employees to make informed and swift business decisions through an easy-to-use text interface, offering substantial advantages for enterprises. Notably, NLSQL serves as the pioneering NLP to SQL API, ensuring that no sensitive or confidential information is transmitted beyond the corporate IT environment. This feature enhances data security while facilitating efficient decision-making processes within organizations.
  • 2
    Latitude Reviews
    Latitude is a comprehensive platform for prompt engineering, helping product teams design, test, and optimize AI prompts for large language models (LLMs). It provides a suite of tools for importing, refining, and evaluating prompts using real-time data and synthetic datasets. The platform integrates with production environments to allow seamless deployment of new prompts, with advanced features like automatic prompt refinement and dataset management. Latitude’s ability to handle evaluations and provide observability makes it a key tool for organizations seeking to improve AI performance and operational efficiency.
  • 3
    Genesis Computing Reviews

    Genesis Computing

    Genesis Computing

    Free
    Genesis Computing offers an innovative enterprise AI platform centered around autonomous "AI data agents" designed to streamline complex data engineering and analytics workflows within an organization’s existing technology framework. This groundbreaking approach creates a new category of AI knowledge workers that function as self-sufficient agents, capable of executing comprehensive data workflows instead of merely providing code suggestions or analytical insights. These agents are equipped to explore data sources, ingest and transform datasets, map raw data from originating systems to structured analytical formats, generate and execute data pipeline code, produce documentation, conduct testing, and oversee pipelines in real-time production settings. By managing these processes from start to finish, the platform significantly diminishes the manual effort usually needed to construct and sustain data pipelines and analytics infrastructure. Consequently, organizations can focus more on strategic initiatives rather than getting bogged down by repetitive technical tasks.
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