Best GPU Clouds With Scale-to-Zero in 2026

This guide covers seven GPU cloud options with scale-to-zero capabilities in 2026. It starts with Modal, a code-first platform that combines serverless GPU inference, automatic scaling, and an optimized infrastructure stack with CPU and GPU compute for training, batch processing, notebooks, and isolated code execution.

Key Takeaways

• Fast cold starts make scale-to-zero more practical: Modal engineers its container runtime and distributed filesystem for fast cold starts and faster feedback loops. The distinction between container startup and model initialization remains important when assessing the end-to-end behavior of any serverless GPU application.
• Scale-to-zero targets idle GPU compute: A zero-replica configuration stops running GPU-replica compute charges after scale-down. Usage-based billing and warm-capacity settings shape the economics of each deployment.
• Code-first infrastructure streamlines development: Modal supports SDKs for Python, TypeScript, and Go. Python developers can define GPU Functions directly in code, while TypeScript and Go applications can interact with deployed Functions and manage other Modal resources, including Sandboxes.
• Deployment models vary: The providers in this guide include code-defined Functions, container-based serverless workers, managed inference endpoints, and model-serving frameworks. Each offers a distinct way to configure scaling and deploy custom models.
• Modal brings the broader AI workflow together: Alongside inference, Modal integrates model training, batch processing, secure Sandboxes, and GPU notebooks in one platform.

1. Modal

Modal is a code-first serverless compute platform designed for AI workloads across CPUs and GPUs. It combines a purpose-built runtime, an optimized distributed filesystem, automatic scaling, and managed infrastructure so developers can move from experiments to production services through a consistent development workflow.

For scale-to-zero inference, Modal provides a particularly cohesive approach: applications can scale GPU Functions with incoming demand, use a configurable warm-capacity strategy for interactive workloads, and take advantage of infrastructure engineered to bring containers online quickly.

How Does Modal Work?

Developers define Modal Functions, dependencies, and resource requirements in Python. Modal builds and caches the application Image, schedules compute, and manages scaling. Existing Dockerfiles and registry Images are also supported through Modal Images.

Modal also provides SDKs for TypeScript and Go to interact with deployed Functions, Sandboxes, and other cloud resources. For isolated code execution, Modal Sandboxes can run applications and commands in any programming language, independently of the language used to orchestrate them.

Core capabilities include:

• Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large Images slow startup down.
• Automatic scale-to-zero: Modal Functions scale up with demand and scale down to zero by default. The min_containers, buffer_containers, and scaledown_window settings provide additional control over warm capacity and scaling behavior.
• Code-defined GPU execution: Python developers can use @app.function() with a gpu= setting to specify accelerators directly in application code. Modal manages the container orchestration and GPU scheduling.
• Memory snapshotting: Modal Memory Snapshots preserve initialized memory state for reuse during container startup. CPU Memory Snapshots and opt-in GPU Memory Snapshots, currently in alpha, support compatible initialization workflows.
• Per-second compute billing: Modal pricing meters GPU, CPU, and memory compute by the second. Automatic scale-to-zero aligns running compute consumption with application demand.

Enterprise-Ready Infrastructure

Modal has completed a SOC 2 Type II audit. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Its security architecture incorporates isolation mechanisms, encrypted data transport and storage, and controls for enterprise applications.

Modal draws on a multi-cloud compute pool and offers a selection of NVIDIA GPUs, including the B300, B200, H200, H100, A100, and L40S families. Region selection and routing options support workload placement and network-latency optimization.

What Makes Modal Unique?

• Integrated developer experience: Application code, dependency Images, GPU resources, and deployment settings come together through Modal's development workflow, with automatic Image caching and reusable infrastructure primitives.
• Deep infrastructure ownership: Modal develops its own runtime, scheduler, and filesystem to support elastic AI execution, efficient data access, and fast container startup.
• One platform across workloads: Teams can use Modal for online inference, GPU training, high-volume batch jobs, collaborative notebooks, and isolated CPU or GPU code execution.

Best For: Teams seeking a unified, code-first platform for scale-to-zero GPU inference and the broader AI development lifecycle, including training, batch processing, and secure execution.

2. Runpod Serverless

Runpod Serverless provides GPU-backed endpoints that scale workers in response to inference requests. It offers different GPU classes and lets applications use serverless workers or configured warm capacity.

Core Capabilities

• Scale-to-zero workers: Flex workers can scale to zero after periods of inactivity and start again when requests arrive.
• Cold-start support: Runpod supports cold starts for on-demand workers and provides a container-resume mechanism called FlashBoot.
• GPU selection: Runpod offers NVIDIA accelerator options across different memory and compute classes.
• Warm workers: Active workers can maintain provisioned capacity for applications using a warm-worker configuration.

Deployment Model

Runpod supports custom container Images and handler-based inference applications. It also offers a Python deployment workflow through its Flash SDK and GitHub-based deployment options. Autoscaling settings and worker type determine how an endpoint provisions capacity.

Best For: Teams looking for serverless inference workers, GPU selection, and a choice of container or Python deployment workflows.

3. Beam Cloud

Beam Cloud offers managed GPU inference endpoints with Python-based deployment and support for scale-to-zero. Its managed service is powered by beta9, an open-source runtime that teams can also operate in their own environments.

Key Features

• Python deployment: Beam supports decorator-based definitions for inference endpoints and GPU workloads.
• Open-source runtime: The beta9 project is published under the AGPL-3.0 license and provides a self-hosting option.
• Memory snapshots: Beam provides GPU memory snapshot capabilities for supported serving configurations.
• Cold starts: Managed endpoints can start GPU containers as requests arrive after idle scale-down.

Open-Source Approach

Beam combines managed endpoints with the option to work with the beta9 runtime. Its managed service includes endpoint autoscaling, an idle keep-warm window, and usage-based GPU compute metering. This structure suits teams interested in Python inference development and an open-source serving runtime.

Best For: Teams using Python for model inference that also value access to a self-hostable serving runtime.

4. Azure Container Apps GPU

Azure Container Apps provides serverless GPU workload profiles within Microsoft's container platform. Its GPU offering brings scale-to-zero deployment to applications that use Azure infrastructure and services.

Enterprise Integration

• Scale-to-zero GPU profiles: Azure Container Apps supports automatic scaling to zero for supported NVIDIA A100 and T4 consumption GPU workloads.
• Container deployment: Applications are packaged as containers and deployed within an Azure Container Apps workload-profiles environment.
• Azure integration: Deployments can use Azure networking, container registries, identity, and monitoring services.
• Model deployment: The service supports custom models and a preview integration path for selected Microsoft Foundry models.

Compliance and Security

Azure provides enterprise identity, networking, regional deployment, and compliance programs across its cloud services. GPU workloads can operate within the Azure Container Apps management model, using the configuration and governance features available to the deployment.

The service uses a consumption-based GPU billing model and offers regional GPU workload profiles.

Best For: Enterprises deploying GPU-backed applications within an existing Azure environment.

5. Replicate

Replicate combines a model marketplace, hosted inference, and dedicated deployments. Its deployment model supports custom models and the option to scale dedicated inference instances according to request demand.

Platform Approach

• Model marketplace: Replicate hosts official and community models that developers can invoke through an API.
• Custom model packaging: Cog provides a framework for packaging and publishing custom inference applications.
• Dedicated deployments: Teams can select from supported GPU options and manage replica settings for their models.
• Deployment controls: Managed deployments support version updates, canary rollouts, and rollback workflows.

Scale-to-Zero and Cold Starts

Replicate supports scale-to-zero for dedicated deployments configured with a zero minimum instance count. It also supports warm-instance configurations. Cold starts are part of its on-demand model-serving workflow, while Replicate-maintained official models have their own availability model.

Best For: Teams using hosted models or deploying custom inference models through a managed API and model marketplace.

6. Hugging Face Inference Endpoints

Hugging Face Inference Endpoints provides managed, dedicated inference deployments connected to the Hugging Face Hub. It supports compatible open models, configurable GPU infrastructure, and endpoint autoscaling.

Autoscaling Capabilities

• Scaling strategies: Endpoints support autoscaling based on hardware utilization or pending requests, with additional inference-load options for selected inference engines.
• Scale-to-zero: An endpoint can scale to zero after a configured idle period when automatic scale-to-zero is enabled and its minimum replica count is set to zero.
• GPU options: Supported hardware includes NVIDIA GPU families such as T4, A10G, A100, and H100.
• Hub integration: Teams can deploy compatible Hub models using supported inference engines or custom container Images.

Billing and Deployment Model

Hugging Face Inference Endpoints uses dedicated instances with consumption-based billing and configurable replica settings. An endpoint can use scale-to-zero or a positive minimum replica count, depending on its serving configuration. A request arriving after scale-down initiates a cold start before the new replica serves inference.

Best For: Teams already working in the Hugging Face ecosystem that want dedicated managed inference endpoints.

7. BentoML/BentoCloud

BentoML is an Apache-2.0 open-source framework for packaging and serving machine learning models. Its managed deployment ecosystem has included BentoCloud and the Bento Inference Platform. BentoML joined Modular in February 2026, bringing its serving technology into Modular's broader inference platform.

Framework and Cloud

• Open-source framework: BentoML supplies model-serving APIs, packaging tools, and deployment workflows.
• Managed deployment: BentoCloud documentation describes managed model serving with configurable autoscaling and a scale-to-zero option.
• Concurrency-based scaling: Bento deployments can use concurrent requests as a scaling signal.
• Cloud deployment options: The ecosystem supports deployment workflows across cloud and customer-managed infrastructure.

Autoscaling Approach

BentoCloud supports scale-to-zero when the deployment's minimum replica count is zero. Its serving system also includes request queues and configurable replica ranges. These controls provide a managed way to match model-serving capacity to incoming request volume.

Best For: Teams using BentoML's model packaging and serving framework that want configurable deployment and autoscaling workflows.

Why Modal Stands Out for Scale-to-Zero GPU Workloads

The seven platforms offer different approaches to GPU inference and idle-capacity management. Modal stands out by combining scale-to-zero with a purpose-built execution stack and integrated tools for the full AI workload lifecycle. It brings inference, training, batch execution, notebooks, and secure code execution into the same code-first infrastructure platform.

Optimized Cold Starts for Production Inference

Modal is engineered for fast cold starts and efficient model-serving workflows. Its container runtime, optimized filesystem, and optional Memory Snapshots help shorten the path from an incoming request to ready compute. These components support complementary stages of application startup:

• Optimized distributed filesystem: Modal Images and Volumes provide efficient distribution of container data and model artifacts across its infrastructure.
• Memory snapshotting: Modal Memory Snapshots can restore prepared memory state for supported initialization patterns, reducing repeated startup work.
• AI-focused scheduling: Modal's runtime and scheduler coordinate containers and GPU resources for bursty inference and parallel workloads.
• Configurable warm capacity: Autoscaling controls let teams combine scale-to-zero with warm containers for latency-sensitive application patterns.

Code-First Developer Experience

Modal makes infrastructure a natural part of application development. Python developers can define Functions, container Images, dependencies, and GPU resources in code, with development tools for local iteration and cloud execution.

Modal's SDKs for Python, TypeScript, and Go support code-driven access to cloud resources. Python defines and runs Modal Functions, while TypeScript and Go clients can invoke those deployed Functions and orchestrate other resources. Modal Sandboxes can execute code in any language, making the platform useful for polyglot applications and coding-agent workflows.

The Modal documentation covers GPU Functions, web endpoints, scheduling, persistent storage, and distributed execution through consistent platform concepts.

Complete Platform for AI Workloads

Modal extends beyond GPU inference with integrated products for other stages of AI development:

• Model Training: Run GPU training and fine-tuning jobs, including distributed training patterns supported by multi-node clusters.
• Batch Processing: Parallelize large processing workloads and map independent inputs across on-demand compute.
• Secure Sandboxes: Run untrusted or AI-generated code inside isolated environments with support for CPU and GPU compute and arbitrary programming languages. Sandboxes can expose application servers through TLS-enabled tunnels.
• GPU Notebooks: Develop and experiment in interactive notebook environments backed by on-demand compute.

Enterprise Security and Compliance

Modal has completed a SOC 2 Type II audit. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Its infrastructure includes gVisor-based isolation, encryption of platform data, and access controls suited to enterprise workflows.

Modal Sandboxes provide isolated execution environments for AI agents that run code, inspect outputs, and iterate. The platform also supports filesystem and directory snapshots, sandbox tunnels, and connection tokens for application-backed environments. These capabilities complement Modal Functions, which serve as the code-defined execution model for trusted application workloads.

Proven at Scale

Modal supports production AI workloads at DoorDash, as well as Suno, Substack, and Ramp. These deployments span different AI application and infrastructure needs. As DoorDash Co-founder Andy Fang said: "At DoorDash, we have to ship quickly and securely. [...] Modal is a big part of how we get those apps from experiment to production."

Ramp's background coding agent, Inspect, uses Modal Sandboxes to provide complete remote development environments for generating, executing, and iterating on code changes. Lovable uses Modal Sandboxes as preview environments for AI-generated applications and websites. Both examples show how isolated execution complements Modal's GPU and CPU compute products.

For organizations that want scale-to-zero GPU inference together with efficient development workflows, high-volume parallel processing, training infrastructure, and secure agent execution, Modal offers the strongest all-around platform in this group. Explore the Modal documentation and customer stories for implementation patterns.