Best Serverless GPU Platforms for AI Apps and Inference in 2025

The performance of your AI applications depends on your underlying infrastructure. Whether leveraging high-performance GPUs, accelerators, or CPUs, AI workloads require high-performance hardware. With a range of different GPUs and accelerators available, choosing the best one for your specific workload is critical.

On top of selecting the best GPU for your workload's needs, efficiently running AI workloads in production and at scale is a challenge. Introducing Serverless GPUs: a cost-effective, scalable way to deploy and scale AI workloads without the compexity of managing infrastructure.

In this blog post, we will dive into different serverless GPU solutions ideal for deploying AI applications. Whether you’re training models, performing real-time inference, fine-tuning models, or building computer vision applications, you’ll find insights on choosing the right serverless GPU for your AI workloads.

Koyeb

Koyeb is a serverless cloud platform optimized for deploying and scaling AI workloads with minimal infrastructure management. Designed for high-performance AI applications, Koyeb offers seamless autoscaling and scale-to-zero capabilities, ensuring you only pay for the compute resources you need. It supports a wide range of AI use cases, from training deep learning models to real-time inference, and provides access to high-end GPUs, including A100s, for compute-intensive tasks.

• Autoscaling & Scale-to-Zero: Supports autoscaling and scale-to-zero, making it a good choice for workloads that need to remain cost-efficient during idle times.
• Developer Experience: Offers a streamlined platform with integrations for AI workflows. Provides simple deployment options and good API support.
• Price: Known for affordable GPU access, particularly for high-end GPUs like A100s. Pay-as-you-go pricing starts at relatively low hourly rates.

RunPod

RunPod provides dedicated and serverless GPU solutions for AI workloads. It supports on-demand GPU instances for training and inference, with both persistent and ephemeral compute options. RunPod includes features such as secure private networking, automated scaling, and storage integration,

• Autoscaling & Scale-to-Zero: Offers flexible scaling options but requires some manual setup for scale-to-zero. Better suited for continuous workloads.
• Developer Experience: Popular for its simplicity and ease of use. Features preconfigured environments for ML workflows. Good for small to medium tasks.
• Price: Known for affordable GPU access, particularly for high-end GPUs like A100s. Pay-as-you-go pricing starts at relatively low hourly rates.

Modal

Modal is a serverless cloud platform that abstracts infrastructure management for AI and machine learning applications. Modal supports Python-based AI workflows, with built-in integrations for machine learning libraries and frameworks. Its pay-as-you-go pricing and fast containerized execution make it useful for applications like model inference, fine-tuning, and data processing.

• Autoscaling & Scale-to-Zero: Excels in autoscaling and scale-to-zero, optimized for short, event-driven tasks and cost-efficiency.
• Developer Experience: Focused on Python-first developers with easy APIs and a seamless local-to-cloud development model. Great documentation and minimal setup overhead.
• Price: Designed for granular usage with competitive pricing. Particularly cost-efficient for intermittent workloads.

Baseten

Baseten is an AI model deployment platform focused on low-latency inference and scalability. It supports model hosting through a Python SDK and no-code UI, allowing users to deploy machine learning models with minimal infrastructure setup. Baseten provides automatic scaling, API endpoints for real-time inference, and integration with cloud storage and vector databases.

• Autoscaling & Scale-to-Zero: Includes scale-to-zero capabilities with robust autoscaling, aimed at building scalable ML applications.
• Developer Experience: Provides an intuitive UI for deploying and serving ML models. Integrates well with common ML tools like PyTorch and TensorFlow.
• Price: Pricing is on par with similar serverless offerings, with added focus on ease of deployment.

Replicate

Replicate is a serverless AI inference platform designed around API-based model execution. It provides access to pre-trained open-source AI models, allowing users to run inference without managing infrastructure.

• Autoscaling & Scale-to-Zero: Built with scale-to-zero as a core feature. Best suited for on-demand inference rather than continuous training workloads.
• Developer Experience: Extremely simple to deploy models via their platform. Strong integration with APIs for rapid testing and deployment. Limited customization but highly efficient for developers focused on inference.
• Price: Competitive pricing focused on inference workloads.

Fal

FAL is a serverless inference platform optimized for low-latency AI workloads. It is designed for applications requiring real-time response times, such as chatbots, interactive AI, and computer vision. The platform supports API-based model execution and offers tools for integrating AI models into production environments.

• Autoscaling & Scale-to-Zero: Offers scale-to-zero functionality. Autoscaling support is tailored to AI pipelines and operational use cases.
• Developer Experience: Designed for integrating AI workflows into existing pipelines. Developer-focused features, but may have a steeper learning curve compared to others.
• Price: Pricing is workload-dependent but often competitive for pipelines requiring lightweight serverless GPU utilization.

Deploy AI applications on high-performance infrastructure

Whether you’re fine-tuning a model, serving thousands of inference requests, or training a custom model, the right serverless GPU platform can make all the difference.

With serverless GPUs, you can seamlessly deploy globally, autoscale from zero to handle unpredictable demand, and optimize for both performance and cost. By choosing infrastructure that aligns with your workload’s specific needs, you can focus on what truly matters: delivering useful AI applications to your users around the world.

Want more AI insights? Explore our articles on the best open source LLMs and multimodal vision models available today.