Compare the Top AI Gateways that integrate with GitHub as of June 2025

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

What are AI Gateways for GitHub?

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

  • 1
    Tyk

    Tyk

    Tyk Technologies

    Tyk is a leading Open Source API Gateway and Management Platform, featuring an API gateway, analytics, developer portal and dashboard. We power billions of transactions for thousands of innovative organisations. By making our capabilities easily accessible to developers, we make it fast, simple and low-risk for big enterprises to manage their APIs, adopt microservices and adopt GraphQL. Whether self-managed, cloud or a hybrid, our unique architecture and capabilities enable large, complex, global organisations to quickly deliver highly secure, highly regulated API-first applications and products that span multiple clouds and geographies.
    Starting Price: $600/month
  • 2
    Dataiku

    Dataiku

    Dataiku

    Dataiku is an advanced data science and machine learning platform designed to enable teams to build, deploy, and manage AI and analytics projects at scale. It empowers users, from data scientists to business analysts, to collaboratively create data pipelines, develop machine learning models, and prepare data using both visual and coding interfaces. Dataiku supports the entire AI lifecycle, offering tools for data preparation, model training, deployment, and monitoring. The platform also includes integrations for advanced capabilities like generative AI, helping organizations innovate and deploy AI solutions across industries.
  • 3
    TrueFoundry

    TrueFoundry

    TrueFoundry

    TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.
    Starting Price: $5 per month
  • 4
    LiteLLM

    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
  • 5
    LangDB

    LangDB

    LangDB

    LangDB offers a community-driven, open-access repository focused on natural language processing tasks and datasets for multiple languages. It serves as a central resource for tracking benchmarks, sharing tools, and supporting the development of multilingual AI models with an emphasis on openness and cross-linguistic representation.
    Starting Price: $49 per month
  • 6
    DagsHub

    DagsHub

    DagsHub

    DagsHub is a collaborative platform designed for data scientists and machine learning engineers to manage and streamline their projects. It integrates code, data, experiments, and models into a unified environment, facilitating efficient project management and team collaboration. Key features include dataset management, experiment tracking, model registry, and data and model lineage, all accessible through a user-friendly interface. DagsHub supports seamless integration with popular MLOps tools, allowing users to leverage their existing workflows. By providing a centralized hub for all project components, DagsHub enhances transparency, reproducibility, and efficiency in machine learning development. DagsHub is a platform for AI and ML developers that lets you manage and collaborate on your data, models, and experiments, alongside your code. DagsHub was particularly designed for unstructured data for example text, images, audio, medical imaging, and binary files.
    Starting Price: $9 per month
  • 7
    RouteLLM
    Developed by LM-SYS, RouteLLM is an open-source toolkit that allows users to route tasks between different large language models to improve efficiency and manage resources. It supports strategy-based routing, helping developers balance speed, accuracy, and cost by selecting the best model for each input dynamically.
  • Previous
  • You're on page 1
  • Next