Best 10 Serverless GPU Clouds & 14 Cost-Effective GPUs
Serverless GPU can provide easy-to-scale computing services for AI workloads. However, their costs can be substantial for large-scale projects. Navigate to sections based on your needs:
- Find the most cost-effective providers by tokens per dollar
- Compare hourly rates across all major providers
- Performance data for inference and fine-tuning throughput
Serverless GPU price per throughput
Serverless GPU providers offer different performance levels and pricing for AI workloads. Compare the most cost-effective GPU configurations for your fine-tuning and inference needs across leading serverless platforms:
Cloud GPU Throughput & Prices
Updated on January 29, 2026
Seeweb
Seeweb
Runpod
Koyeb
Runpod
Beam Cloud
Koyeb
Modal
Runpod
Runpod
Serverless GPU price calculator
Serverless GPU benchmark results
You can read more about our benchmark methodology for serverless GPU.
Shortlisted 10 serverless GPU providers
Companies are sorted alphabetically because this field is an emerging domain and limited data are available, except for the sponsors, which are placed at the top of the list with a link to their website.
RunPod
RunPod delivers fully managed and scalable AI endpoints for diverse workloads. RunPod users can choose between GPU instances and serverless endpoints and employ a Bring Your Own Container (BYOC) approach. Some of the RunPod features include:
- Loading process through dropping a container link to pull a pod
- A credit-based payment and billing system.
Baseten Labs
Baseten is a machine learning infrastructure platform that helps users deploy models of various sizes and types from the model library at scale. It leverages GPU instances like A100, A10, and T4 to enhance computational performance.
Baseten also introduces an open-source tool called Truss. This tool can help developers deploy AI/ML models in real-world scenarios. With Truss, developers can:
- Package and test model code, weights, and dependencies using a model server.
- Develop their model with quick feedback from a live reload server, avoiding complex Docker and Kubernetes configurations.
- Accommodate models created with any Python framework, be it transformers, diffusors, PyTorch, Tensorflow, XGBoost, sklearn, or even entirely custom models.
Beam Cloud
Beam, formerly known as Slai, provides easy REST API deployment with built-in features like authentication, autoscaling, logging, and metrics. Beam users can:
- Execute GPU-based long-running training tasks, choosing between one-time or scheduled automated retraining
- Deploy functions to a task queue with automated retries, callbacks, and task status queries.
- Customize autoscaling rules to optimize user wait times.
Cerebrium AI
Cerebrium AI offers a diverse selection of GPUs, including H100s, A100s, and A5000s, with a total of over 8 GPU types available. Cerebrium allows users to define their environment with infrastructure-as-code and to access code directly, without needing to manage S3 buckets.
Fal AI
FAL AI delivers ready-to-use models with API endpoints for customization and integration into customer apps. Their platform supports Serverless GPUs, such as A100 and T4.
Koyeb
Koyeb is a serverless platform designed to let developers easily deploy applications globally without managing servers, infrastructure, or operations. Koyeb offers serverless GPUs with Docker support and horizontal scaling for AI tasks such as generative AI, video processing, and LLMs. Its offer includes H100 and A100 GPUs with up to 80GB vRAM.
Its pricing ranges from $0.50/hr to $3.30/hr, billed by the second.
Modal
Modal is a serverless cloud platform that allows developers to execute code remotely, define container environments programmatically, and scale to thousands of containers. It supports GPU integration, web endpoint serving, scheduled job deployment, and distributed data structures like dictionaries and queues. The platform operates on a pay-per-second model and requires no infrastructure configuration, focusing on code-based setup rather than YAML.
To use Modal, developers sign up at modal.com, install the Modal Python package via pip install modal, and authenticate with modal setup. Code runs in containers within Modal’s cloud, abstracting away infrastructure management like Kubernetes or AWS. Currently limited to Python, it may expand to other languages.
Mystic AI
Mystic AI’s serverless platform is a pipeline core that hosts ML models through an inference API. Pipeline core can create custom models with over 15 options, such as GPT, Stable diffusion, and Whisper. Here are some of the Pipeline core features:
- Simultaneous model versioning and monitoring
- Environment management, including libraries and frameworks
- Auto-scale across various cloud providers
- Support for online, batch, and streaming inference
- Integrations with other ML and infrastructure tools.
Mystic AI also provides an active Discord community for support.
Novita AI
Novita AI is a platform designed to help developers create advanced AI products without deep machine learning expertise. It offers a comprehensive suite of APIs and tools for building applications across various domains, including image, video, audio, and large language model (LLM) tasks.
Novita AI’s serverless system offers auto-scaling, deployment with DockerHub support, and real-time monitoring.
Replicate
Replicate’s platform supports custom and pre-trained machine learning models. The platform delivers a waitlist for open-source models and offers flexibility with a choice between Nvidia T4 and A100. The platform also includes an open-source library, COG, to facilitate model deployment.
Seeweb
Seeweb is a cloud computing provider that offers serverless GPU solutions to optimize AI workloads. These solutions serve as an entry point for developers looking to run, fork, or pre-train popular models efficiently in Python. They can leverage Kubernetes to speed up deployments
Key features:
- Autoscaling to dynamically adjust resources, reducing cold starts associated with serverless functions.
- GDPR compliance by operating in a European cloud and using a global network for expanded reach.
- 24x7x365 support ensuring users receive reliable assistance for managing their ML models.
Provided GPUs include A100, H100, L40S, L4 and RTX A6000.
What are other cloud providers?
Top cloud providers such as Google, AWS, and Azure offer serverless functionality that does not support GPUs at the moment. Other providers, such as Scaleway or CoreWeave, offer GPU inference but do not offer serverless GPUs.
Find out more about cloud GPU providers and the GPU market.
What are the benefits of serverless GPU?
LLMs like ChatGPT have been a hot topic in the business world since last year. Thus, the number of these models has drastically increased. Serverless GPUs’ benefits help avoid several LLM challenges, such as:
- Cost efficiency: Users only pay for the GPU resources they actually use, making it a cost-effective solution. In a traditional server setup, users are expected to pay for ongoing resource provisioning.
- Scalability: Serverless architectures automatically scale to handle varying workloads. When the demand for resources increases or decreases, the infrastructure dynamically adjusts without manual intervention.
- Simplified management: Developers can focus on writing code for specific functions or tasks, as the cloud provider handles server provisioning, scaling, and other infrastructure management.
- On-Demand resource allocation: Serverless GPU architecture allows applications to access GPU resources on demand. This helps manage and maintain physical or virtual servers dedicated to GPU processing. Resources are allocated dynamically based on application requirements.
- Flexibility: Developers can scale resources up or down based on the specific needs of their applications. This adaptability is particularly useful for workloads with varying computational requirements.
- Enhanced parallel processing: GPU computing excels at parallel processing tasks. Therefore, serverless GPU architectures can be utilized in applications that require significant parallel computation, such as machine learning inference, data processing, and scientific simulations.
Serverless GPU benchmark methodology
Prices: Serverless GPU prices are crawled monthly from all providers.
Performance:
- The performance of all serverless GPU models was measured on the Modal cloud platform.
- Text finetuning was measured by finetuning Llama 3.2-1B-Instruct on the FineTune-100k dataset, using 1M tokens across 5 epochs. The number of tokens multiplied by the number of epochs was divided by the finetuning time to obtain the number of tokens finetuned per second.
- Text inference was measured over 1 million tokens, including both input and output tokens. We divided the number of tokens by the total inference duration to calculate the average number of tokens per second.
H200 vs H100 Performance Notes:
- The H200 showing lower finetuning performance than H100 may seem counterintuitive given its newer architecture and larger memory (141GB vs 80GB). Several factors could contribute to this result, including differences in memory bandwidth utilization, software optimization maturity, or thermal management under sustained workloads.
- This benchmark used a relatively small 1B parameter model, which may not fully leverage H200’s additional memory capacity. The performance gap might differ significantly with larger models that better utilize the H200’s expanded memory.
- Performance can also vary based on specific workload characteristics, batch sizes, and the particular software stack used during testing.
Next Steps:
- We plan to expand our benchmarks to include larger models (7B, 13B, and 70B parameters) to better understand how performance scales with model size and memory requirements.
- Future testing will include multi-GPU setups and longer context length scenarios where H200’s architectural advantages may be more apparent.
How to use Serverless GPUs for ML models
In traditional machine learning workflows, developers and data scientists often provision and manage dedicated servers or GPU clusters to handle the computational demands of training complex models. Serverless GPU for machine learning removes the complexities of infrastructure management.
Please follow the guide below to understand how to use Serverless GPU in ML models:
- Training models: Serverless GPU enables efficient machine learning model training by dynamically allocating resources for extensive datasets. Developers benefit from on-demand resources without the hassle of managing dedicated servers.
- Inference: Serverless GPUs are crucial for model inference, enabling quick predictions on new data. Ideal for applications such as image recognition and natural language processing, it ensures fast, efficient execution, especially during periods of variable demand.
- Real-time processing: Applications that require it, such as video analysis, leverage Serverless GPU. Dynamic resource scaling enables the swift processing of incoming data streams, making it suitable for real-time applications across domains.
- Batch processing: Serverless GPUs handle large-scale data processing in ML workflows. This is essential for data preprocessing, feature extraction, and other batch-oriented machine learning operations.
- Event-driven ML workflows: Serverless architectures are event-driven, responding to triggers or events, such as updating a model when new data becomes available or retraining it in response to specific events.
- Hybrid architectures: Some ML workflows combine serverless and traditional computing resources. For instance, GPU-intensive model training transitions to a serverless environment for AI inference, optimizing resource utilization.
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Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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