What Are Serverless GPUs?

Serverless GPUs provide a cloud computing model where developers can run GPU-accelerated workloads without managing underlying infrastructure. Unlike traditional GPU instances that run 24/7, serverless GPUs activate only when needed, scaling automatically based on demand :cite[3].

Key Benefits of Serverless GPU Architecture

Top Serverless GPU Providers Compared

The serverless GPU market has expanded significantly in 2025. Here’s a detailed comparison of the leading platforms:

Detailed Provider Analysis

1. RunPod: Most Affordable Option

RunPod leads in price-performance ratio with extensive GPU options from entry-level to high-end accelerators. Their “Community Cloud” model aggregates resources from data centers and individual contributors, enabling aggressive pricing :cite[7].

RunPod offers three deployment modalities: “Quick Deploy” for pre-built endpoints, “Handler Functions” for custom code, and “vLLM Endpoint” for Hugging Face models :cite[3]. Their main limitation is slightly less polished monitoring compared to competitors.

2. Koyeb: Unified Platform Solution

Koyeb provides a comprehensive serverless platform that supports both standard applications and GPU-accelerated workloads. Their native autoscaling and scale-to-zero capabilities make them ideal for production AI applications :cite[2].

Koyeb excels at handling full-stack deployment – developers can host frontend applications, databases, and GPU-powered APIs on a single platform. Their global network spans 6 continents with high-speed connectivity :cite[2].

3. Modal: Developer Experience Focus

Modal stands out for its exceptional Python SDK and rapid cold starts (2-4 seconds). Developers define GPU-accelerated functions directly in Python without Dockerfile complexity :cite[3].

import modal

image = modal.Image.debian_slim().pip_install("torch")

app = modal.App("gpu-example")

@app.function(gpu="A100")
def gpu_task():
    import torch
    return torch.cuda.get_device_name(0)

Modal’s flexibility supports diverse workflows beyond model serving, including GPU-accelerated CI/CD pipelines and batch processing. However, costs can be higher for sustained workloads compared to RunPod :cite[4].

4. Baseten: ML Model Specialization

Baseten focuses exclusively on machine learning model deployment with their open-source Truss framework. The platform simplifies converting models into production-ready APIs with minimal configuration :cite[3].

While more expensive than alternatives, Baseten offers advanced features like automatic model versioning, monitoring, and canary deployments. Their platform is less suitable for non-ML workloads :cite[3].

Serverless GPU Pricing Analysis

Understanding serverless GPU pricing requires analyzing multiple dimensions beyond hourly rates:

For budget-conscious projects, consider comparing serverless GPU pricing across multiple providers before committing.

Real-World Use Cases

Optimal Workloads for Serverless GPUs

• Real-time inference: On-demand processing for user-facing applications
• Batch processing: Parallel processing of large datasets
• Model fine-tuning: Periodic retraining with custom datasets
• Event-driven workflows: Trigger-based processing (e.g., new data arrival)
• CI/CD pipelines: GPU-accelerated testing and validation

For training large foundation models, dedicated GPU instances remain more cost-effective due to sustained usage requirements. Learn more about training ML models with serverless GPUs for smaller-scale projects.

Choosing the Right Provider

Selecting the optimal serverless GPU platform depends on your specific requirements: