Best Serverless GPU Platforms for AI Apps and Inference in 2025

The performance of AI applications depends on its underlying infrastructure. Whether its fine-tuning custom models, performing real-time inference, deploying AI agents, AI workloads require high-performance hardware like Nvidia GPUs or next-gen AI accelerators from Tenstorrent.

On top of performance, efficiently running AI workloads in production and at scale is a challenge. Serverless GPUs provide a cost-effective, scalable, and efficient way to deploy and scale AI workloads without the compexity of managing infrastructure.

In this blog post, we dive into different serverless GPU solutions built for deploying AI applications, including: Koyeb, Modal, RunPod, Baseten, and Fal. After covering the different platforms, we'll explore their strengths, and compare price points across L40s, A100, and H100 GPUs.

Koyeb

Koyeb provides a serverless cloud for developers and teams to seamlessly deploy AI apps and databases on high-performance infrastructure, including CPUs, GPUs, and Accelerators, worldwide. Offering native autoscaling and scale-to-zero capabilities, it ensures optimized and cost-efficient infrastructure by automatically adjusting GPU resources based on demand.

With support for high-performance GPUs like Nvidia H100 and A100s, and next-gen AI accelerators from Tenstorrent, Koyeb is well-suited for AI inference, model fine-tuning, training and other compute-intensive tasks. With global availability, high-speed networking, and pay-as-you-go pricing, Koyeb is ideal for teams looking to run AI applications, inference, fine-tuning, and other compute-intensive tasks.

Pricing: A100, H100, L40S

• L40S: $1.55/hour
• A100: $2/hour
• H100: $3.30/hour

Modal

Modal is a serverless cloud platform that abstracts infrastructure management for AI and GPU-accelerated functions. Modal offers a Python SDK for deploying AI workloads with serverless GPUs.

Since everything is defined and deployed through their SDK, you also need to manage infrastructure in code, which can be limiting if you're trying to migrate an existing app or bring your own containers. Running pre-built AI services or standard web apps isn’t straightforward, making Modal best suited for new AI and machine learning apps.

Pricing: A100, H100, L40S

• L40S: $1.95/hour
• A100: $2.50/hour
• H100: $3.95/hour

RunPod

RunPod offers flexible access to GPUs via both serverless and dedicated instances. You can bring your own containers, run inference or training workloads, and scale up via API or dashboard.

It’s beginner-friendly, with preconfigured environments for common ML frameworks. That said, RunPod isn't optimized for high-performance workloads—cold starts can be slow, and costs ramp up quickly for long-running or production-scale deployments.

Pricing: A100, H100, L40S

• L40S: $1.90/hour
• A100: $2.72/hour
• H100: $4.18/hour

Prices based on Flex option.

Baseten

Baseten is focused on serving machine learning models with low-latency inference and support for async processing. Using their open-source Truss framework, developers can package models into production-ready APIs with minimal setup.

Baseten is a strong fit for deploying PyTorch, TensorFlow, and Hugging Face models into real-time or batch inference pipelines. That being said, it’s more specialized for model serving and less flexible for broader infrastructure needs or custom training workflows.

Pricing: A100, H100, L40S

• L40S: Not listed on the pricing page
• A100: $4.00/hour
• H100: $6.50/hour

Replicate

Replicate lets you run, fine-tune, and deploy custom models at scale via a developer-focused SDK. It’s designed for fast iteration, with great support for async processing, versioning, and a solid API for integrating ML into apps.

It excels in developer experience and tooling, especially for building around hosted models and workflows. On the other hand, it’s expensive at scale, can’t run arbitrary workloads, and locks you into their deployment layer, offering less flexibility than other serverless GPU platforms.

Pricing: A100, H100, L40S

• L40S: $3.51/hour
• A100: $5.04/hour
• H100: $5.49/hour

Fal

Fal is optimized for generative media and diffusion models, with a strong focus on real-time inference. It offers an inference engine and is a great option for building ML-powered apps, especially in the generative media space.

Using Fal in production can get expensive fast, and the platform lacks certain flexibility. For example, you’re locked into their stack and can’t export model weights if you fine-tune using their tools.

Pricing: A100, H100, L40S

• L40S: Not listed on the pricing page
• A100: $0.99/hour*
• H100: $1.99/hour*

*All of these prices are starting points and it seems you need to contact Fal's support to get started.

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, top open source multimodal vision models, and AI agent protocols like MCP and A2A.