Best AI Gateways for Databricks

Kosmoy is the AI management platform for regulated enterprises. Models, copilots and autonomous agents now run in every business unit, on every cloud, from every vendor — and the same gaps appear everywhere: shadow AI nobody registered, governance rebuilt app by app, spend that grows unseen, agents that act faster than any reviewer, and audit evidence scattered across tickets and spreadsheets. These are not five problems. They are one missing control plane. Kosmoy is that control plane, built as four layers. Inventory: four registries — AI systems, models, MCP servers and a master agent registry that pulls agents automatically from Azure AI Foundry, AWS Bedrock, Google Vertex, Salesforce and ServiceNow — each entry with an owner and a risk tier, and anything running unregistered flagged as shadow AI. Monitoring: every call observed — cost, usage and behaviour per model, application and user, with budgets that warn and then block. Governance: one OpenAI-compatible AI Gateway enforcing guardrails, RBAC, routing and logging on every LLM, MCP and agent-to-agent call; the router sends simple prompts to smaller models, and customers regularly cut spend 90% on routable workloads. Action Control: Action Capsules — kernel-enforced sandboxes in your own Kubernetes — contain autonomous agents, MCP servers and private models with just-in-time credentials and a kill switch. The same registries and gateway logs produce audit-ready evidence for the EU AI Act, ISO/IEC 42001 and NIST AI RMF. Kosmoy is self-hosted software, not a SaaS you send prompts to: single-tenant in your own Kubernetes on Azure, AWS, GCP or on-prem, air-gap capable, model-agnostic across OpenAI, Anthropic, Google, Mistral and open-source models. .

LiteLLM serves as a comprehensive platform that simplifies engagement with more than 100 Large Language Models (LLMs) via a single, cohesive interface. It includes both a Proxy Server (LLM Gateway) and a Python SDK, which allow developers to effectively incorporate a variety of LLMs into their applications without hassle. The Proxy Server provides a centralized approach to management, enabling load balancing, monitoring costs across different projects, and ensuring that input/output formats align with OpenAI standards. Supporting a wide range of providers, this system enhances operational oversight by creating distinct call IDs for each request, which is essential for accurate tracking and logging within various systems. Additionally, developers can utilize pre-configured callbacks to log information with different tools, further enhancing functionality. For enterprise clients, LiteLLM presents a suite of sophisticated features, including Single Sign-On (SSO), comprehensive user management, and dedicated support channels such as Discord and Slack, ensuring that businesses have the resources they need to thrive. This holistic approach not only improves efficiency but also fosters a collaborative environment where innovation can flourish.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.