Best AI Gateways for Microsoft 365

Zapier is a comprehensive AI automation platform that helps organizations transform how work gets done. It allows teams to connect AI tools with everyday apps to automate workflows end to end. Zapier supports AI workflows, custom agents, chatbots, forms, and data tables in one unified system. With over 8,000 integrations, it eliminates manual handoffs between tools and teams. Built-in AI assistance helps users design automations quickly without technical complexity. Zapier enables teams to deploy AI agents that work continuously, even outside business hours. The platform offers full visibility into automation activity with audit logs and analytics. Enterprise-grade security and compliance ensure safe AI adoption at scale. Zapier is used across departments including marketing, sales, IT, and operations. It helps teams save time, reduce costs, and scale productivity with confidence.

TensorBlock is an innovative open-source AI infrastructure platform aimed at making large language models accessible to everyone through two interrelated components. Its primary product, Forge, serves as a self-hosted API gateway that prioritizes privacy while consolidating connections to various LLM providers into a single endpoint compatible with OpenAI, incorporating features like encrypted key management, adaptive model routing, usage analytics, and cost-efficient orchestration. In tandem with Forge, TensorBlock Studio provides a streamlined, developer-friendly workspace for interacting with multiple LLMs, offering a plugin-based user interface, customizable prompt workflows, real-time chat history, and integrated natural language APIs that facilitate prompt engineering and model evaluations. Designed with a modular and scalable framework, TensorBlock is driven by ideals of transparency, interoperability, and equity, empowering organizations to explore, deploy, and oversee AI agents while maintaining comprehensive control and reducing infrastructure burdens. This dual approach ensures that users can effectively leverage AI capabilities without being hindered by technical complexities or excessive costs.

Storm MCP serves as an advanced gateway centered on the Model Context Protocol (MCP), facilitating seamless connections between AI applications and multiple verified MCP servers through a straightforward one-click deployment process. It ensures robust enterprise-level security, enhanced observability, and easy integration of tools without the need for extensive custom development. By standardizing AI connections and only exposing specific tools from each MCP server, it helps minimize token consumption and optimizes the selection of model tools. With its Lightning deployment feature, users can access over 30 secure MCP servers, while Storm efficiently manages OAuth-based access, comprehensive usage logs, rate limitations, and monitoring. This innovative solution is crafted to connect AI agents to external context sources securely, allowing developers to sidestep the complexities of building and maintaining their own MCP servers. Tailored for AI agent developers, workflow creators, and independent innovators, Storm MCP stands out as a flexible and configurable API gateway, simplifying infrastructure challenges while delivering dependable context for diverse applications. Its unique capabilities make it an essential tool for those looking to enhance their AI integration experience.

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.