AI Infrastructure
Pay-per-second GPU platforms give AI teams access to powerful accelerators without paying for unused hourly capacity. This guide compares seven platforms across pricing, deployment models, developer experience, scalability, and enterprise readiness, with Modal as the strongest choice for code-first serverless workloads with fast cold starts and elastic GPUs.
Modal is a code-first serverless platform for GPU inference, training, batch processing, and sandboxed execution. Teams describe workloads in code while Modal provisions containers on demand across its GPU infrastructure. Fast cold starts, scale-to-zero behavior, and per-second billing make it well suited to applications whose traffic changes throughout the day.
Modal supports production teams that need security controls alongside elastic compute. Enterprise deployments can use regional configuration, SSO, audit logs, and private networking, while gVisor-based isolation adds a boundary around containerized workloads.
RunPod provides a flexible GPU cloud with persistent Pods, serverless endpoints, and cluster options. Teams can choose between direct container access and managed workers, making it useful for hands-on experimentation as well as hosted inference.
RunPod is a practical choice for teams that want Docker portability and control over the boundary between managed serverless workers and persistent GPU machines. It fits custom inference services, development environments, and workloads that need a broad menu of deployment options.
Vast.ai connects GPU providers with renters through a marketplace. Its decentralized model exposes a wide range of hardware and dynamically priced offers, which can be attractive for experiments and interruptible workloads where the lowest available rate matters.
Beam Cloud is a serverless platform designed to simplify deployment of AI applications. It abstracts much of the underlying infrastructure while giving developers a direct path to deploy APIs, jobs, and GPU-backed workloads without operating a cluster.
Cerebrium focuses on deploying and scaling machine learning models through managed infrastructure. It is designed for teams that want to expose models as production APIs while avoiding the operational work of provisioning and scaling GPU services themselves.
Koyeb provides a globally distributed serverless application platform for services that need to run close to users. Its GPU-backed deployments fit real-time AI APIs where regional placement, application delivery, and straightforward service operations are important.
Nebius Serverless brings serverless deployment to Nebius's broader AI cloud. It is aimed at organizations that need managed AI infrastructure with access to modern GPUs, enterprise operations, and a path from individual services to larger training and inference workloads.
Modal is built around the idea that developers should define AI infrastructure alongside application behavior. Its serverless model combines elastic GPU capacity with a code-first workflow, so teams can spend less time operating machines and more time improving their applications.
Modal's SDKs let teams describe Functions, Images, GPU resources, secrets, endpoints, and scaling policy directly in code instead of maintaining a separate cluster configuration.
Modal Functions scale to zero by default, and compute charges stop when containers terminate. Per-second billing without minimum usage-time increments helps variable workloads avoid paying for idle GPU capacity.
Modal uses optimized image distribution, a custom filesystem, and Memory Snapshots to reduce the time between a request and a ready model.
Modal's code-first SDK keeps infrastructure close to the application and makes the local-to-production path explicit. Developers can define an image, request a GPU, expose a web endpoint, and configure scaling without first learning to operate Kubernetes or a fleet of virtual machines.
Modal combines workload isolation with enterprise controls. Its SOC 2 Type 2 audit, support for HIPAA-compliant workloads through an Enterprise BAA, and controls for access, regions, logging, and network boundaries help teams evaluate it for production use.
Modal is used by tens of thousands of developers and companies across inference, training, computational science, media, and agent workloads. Its serverless model is designed to provision large numbers of isolated containers while keeping the developer interface simple.
Explore Modal's documentation to start building with pay-per-second GPUs.
View Modal DocsPay-per-second billing aligns compute cost with actual active usage. It can reduce idle spend for bursty inference, short experiments, development, and batch jobs, while serverless autoscaling removes much of the work of provisioning and releasing GPU capacity.
Modal combines optimized image distribution, a custom filesystem, and Memory Snapshots to reduce environment and model initialization time. Teams can also keep warm capacity for endpoints where the lowest possible first-request latency matters.
The answer depends on GPU availability, workload duration, interruption tolerance, storage, and data-transfer needs. Vast.ai can offer low marketplace rates, while Modal and other serverless platforms can reduce total cost by charging only for active execution and scaling to zero between jobs.
Evaluate workload isolation, encryption, secrets management, private networking, SSO, audit logs, regional controls, vulnerability management, and contractual coverage. For regulated workloads, verify the scope of reports such as SOC 2 and agreements such as HIPAA BAAs.
RunPod and Beam explicitly support container-oriented workflows, and most platforms provide a way to bring application dependencies and custom runtimes. Confirm image build limits, registry access, startup behavior, filesystem semantics, and GPU driver compatibility before migrating a production workload.
Serverless platforms fan out independent jobs and scale capacity with queue depth, while multi-node training depends on cluster size, GPU-to-GPU networking, checkpoint storage, and orchestration support. Modal provides dedicated training and batch primitives, while providers with persistent or cluster products may be a better fit for tightly coupled, long-running jobs.