Compare the Top AI Inference Platforms for Linux as of October 2025

What are AI Inference Platforms for Linux?

AI inference platforms enable the deployment, optimization, and real-time execution of machine learning models in production environments. These platforms streamline the process of converting trained models into actionable insights by providing scalable, low-latency inference services. They support multiple frameworks, hardware accelerators (like GPUs, TPUs, and specialized AI chips), and offer features such as batch processing and model versioning. Many platforms also prioritize cost-efficiency, energy savings, and simplified API integrations for seamless model deployment. By leveraging AI inference platforms, organizations can accelerate AI-driven decision-making in applications like computer vision, natural language processing, and predictive analytics. Compare and read user reviews of the best AI Inference platforms for Linux currently available using the table below. This list is updated regularly.

  • 1
    LM-Kit.NET
    LM-Kit.NET brings advanced AI to C# and VB.NET by letting you create and deploy context-aware agents that run small language models directly on edge devices, trimming latency, protecting data, and delivering real-time performance even in resource-constrained environments so both enterprise systems and rapid prototypes can ship faster, smarter, and more reliable applications.
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    Starting Price: Free (Community) or $1000/year
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  • 2
    Feast

    Feast

    Tecton

    Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution.
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