Best AI Gateways for Databricks Data Intelligence Platform
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Compare the Top AI Gateways that integrate with Databricks Data Intelligence Platform as of October 2025
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This a list of AI Gateways that integrate with Databricks Data Intelligence Platform. Use the filters on the left to add additional filters for products that have integrations with Databricks Data Intelligence Platform. View the products that work with Databricks Data Intelligence Platform in the table below.
What are AI Gateways for Databricks Data Intelligence Platform?
AI gateways, also known as LLM gateways, are advanced systems that facilitate the integration and communication between artificial intelligence models and external applications, networks, or devices. They act as a bridge, enabling AI systems to interact with different data sources and environments, while managing and securing data flow. These gateways help streamline AI deployment by providing access control, monitoring, and optimization of AI-related services. They often include features like data preprocessing, routing, and load balancing to ensure efficiency and scalability. AI gateways are commonly used in industries such as healthcare, finance, and IoT to improve the functionality and accessibility of AI solutions. Compare and read user reviews of the best AI Gateways for Databricks Data Intelligence Platform currently available using the table below. This list is updated regularly.
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LiteLLM
LiteLLM
LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple providers. It ensures robust observability by generating unique call IDs for each request, aiding in precise tracking and logging across systems. Developers can leverage pre-defined callbacks to log data using various tools. For enterprise users, LiteLLM offers advanced features like Single Sign-On (SSO), user management, and professional support through dedicated channels like Discord and Slack.Starting Price: Free -
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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
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Kosmoy
Kosmoy
Kosmoy Studio is the core engine behind your organization’s AI journey. Designed as a comprehensive toolbox, Kosmoy Studio accelerates your GenAI adoption by offering pre-built solutions and powerful tools that eliminate the need to develop complex AI functionalities from scratch. With Kosmoy, businesses can focus on creating value-driven solutions without reinventing the wheel at every step. Kosmoy Studio provides centralized governance, enabling enterprises to enforce policies and standards across all AI applications. This includes managing approved LLMs, ensuring data integrity, and maintaining compliance with safety policies and regulations. Kosmoy Studio balances agility with centralized control, allowing localized teams to customize GenAI applications while adhering to overarching governance frameworks. Streamline the creation of custom AI applications without needing to code from scratch.
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