Compare the Top AI Gateways that integrate with LiteLLM as of December 2025

This a list of AI Gateways that integrate with LiteLLM. Use the filters on the left to add additional filters for products that have integrations with LiteLLM. View the products that work with LiteLLM in the table below.

What are AI Gateways for LiteLLM?

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 LiteLLM currently available using the table below. This list is updated regularly.

  • 1
    OpenRouter

    OpenRouter

    OpenRouter

    OpenRouter is a unified interface for LLMs. OpenRouter scouts for the lowest prices and best latencies/throughputs across dozens of providers, and lets you choose how to prioritize them. No need to change your code when switching between models or providers. You can even let users choose and pay for their own. Evals are flawed; instead, compare models by how often they're used for different purposes. Chat with multiple at once in the chatroom. Model usage can be paid by users, developers, or both, and may shift in availability. You can also fetch models, prices, and limits via API. OpenRouter routes requests to the best available providers for your model, given your preferences. By default, requests are load-balanced across the top providers to maximize uptime, but you can customize how this works using the provider object in the request body. Prioritize providers that have not seen significant outages in the last 10 seconds.
    Starting Price: $2 one-time payment
  • 2
    Taam Cloud

    Taam Cloud

    Taam Cloud

    Taam Cloud is a powerful AI API platform designed to help businesses and developers seamlessly integrate AI into their applications. With enterprise-grade security, high-performance infrastructure, and a developer-friendly approach, Taam Cloud simplifies AI adoption and scalability. Taam Cloud is an AI API platform that provides seamless integration of over 200 powerful AI models into applications, offering scalable solutions for both startups and enterprises. With products like the AI Gateway, Observability tools, and AI Agents, Taam Cloud enables users to log, trace, and monitor key AI metrics while routing requests to various models with one fast API. The platform also features an AI Playground for testing models in a sandbox environment, making it easier for developers to experiment and deploy AI-powered solutions. Taam Cloud is designed to offer enterprise-grade security and compliance, ensuring businesses can trust it for secure AI operations.
    Starting Price: $10/month
  • 3
    MLflow

    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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