AI Infrastructure

Best Cloud GPU Providers in 2026

The demand for cloud GPU infrastructure continues to accelerate as AI teams scale inference, training, and batch processing workloads. Selecting the right provider means balancing developer experience, autoscaling capabilities, cold start performance, and total cost efficiency. For AI teams seeking to move fast without infrastructure overhead, a purpose-built serverless GPU platform can transform how quickly you ship production AI applications. This guide examines seven cloud GPU providers serving different workload profiles in 2026, starting with Modal, the code-first serverless platform built around fast cold starts, scale-to-zero autoscaling, and per-second compute billing.

Modal TeamEngineering
August 202622 min read
Cloud GPU infrastructure for AI workloads

Key Takeaways

  • Code-first serverless platforms reduce infrastructure management: Modal's code-first SDK lets you define Functions, GPU resources, autoscaling behavior, and container Images with decorators and SDK primitives, without Kubernetes manifests or a separate infrastructure configuration layer, with code-defined infrastructure available in Python, TypeScript, and Go
  • Fast cold starts shape inference economics: Modal is 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
  • Scale-to-zero with per-second billing optimizes variable workloads: Modal Functions scale to zero by default and compute charges stop once containers terminate, with a configurable scaledown window and optional warm capacity for latency-sensitive endpoints
  • Enterprise compliance differs across providers: SOC 2 Type II is now held by multiple providers on this list, so the more useful differentiators are HIPAA BAA availability, isolation model, and SSO rather than the certification alone
  • Use case alignment matters more than raw GPU cost: teams running sustained 24/7 training have different optimal providers than those serving bursty inference APIs or running parallel batch jobs

1. Modal

Modal delivers a code-first serverless GPU platform designed to make running inference, training, batch processing, and secure sandboxed execution feel local to developers. Tens of thousands of developers and companies use Modal, working through a code-first SDK and a per-second billing model with no minimum usage-time increments.

How Does Modal Work?

Modal takes your code, packages it into containers, and executes it in the cloud with automatic scaling. Infrastructure is defined in code through Modal's SDKs for Python, TypeScript, and Go, and the code running inside Modal containers and Sandboxes is not limited to one programming language: a workload can run whatever runtime or language it requires. The platform handles capacity decisions across a multi-cloud GPU pool, letting developers focus on application logic rather than infrastructure management. Key Capabilities:

  • 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, and Modal's cold start guide covers the techniques that make this possible
  • Scale-to-zero autoscaling: Modal advertises autoscaling from 0 to 1,000+ GPUs, with GPU concurrency scaling across Starter and Team plans and custom concurrency available on Enterprise, as set out on the pricing page
  • Code-first SDK: define Functions, resource requests, autoscaling settings, and clustered execution with decorators, and build container environments through Image SDK primitives, all without separate configuration files, in Python, TypeScript, or Go
  • Per-second GPU billing: billing documentation confirms there are no minimum usage-time increments, and the scaledown window is configurable from 2 seconds to 20 minutes so teams can tune exactly how long containers stay warm between requests
  • Cutting-edge GPU access: availability of B300, B200, H200, H100, A100, L40S, and other GPU types through Modal's GPU infrastructure and current pricing page

Developer Experience

Modal is a code-first serverless AI infrastructure platform for GPU inference, training, batch processing, and sandboxed execution, with SDKs and code-defined infrastructure in Python, TypeScript, and Go. It is designed for fast notebook-to-production workflows: Modal Notebooks can start GPU ML workloads in under five seconds, and Modal says developers can ship a first app in minutes. Platform Features:

  • Modal Notebooks: cloud Jupyter environments that start GPU ML workloads in under five seconds, support real-time collaboration with multiple cursors and shared context, and can use up to eight H100s or B200s
  • Modal Sandboxes: secure, dynamically defined containers that run whatever runtime or language a workload requires, created and controlled through the Python, TypeScript, and Go SDKs, with the product page advertising 100k+ concurrent sandboxes for AI-generated code execution and RL rollouts
  • Built-in observability with production dashboards, logging, and log export for deployed applications
  • Native support for dynamic batching and parallel execution patterns

Security and Compliance

Modal has completed a SOC 2 Type 2 audit with no deviations found, and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform uses gVisor-based sandboxing, which places an additional kernel-level isolation boundary around untrusted workloads such as AI-generated code, and encrypts data in transit and at rest. Sandboxes can also be pinned to a chosen region for workloads with residency requirements, and sandbox tunnels support connection tokens for authenticating access to servers running inside a sandbox. Best For: AI teams seeking a fast path from development to production, organizations with bursty inference workloads that benefit from scale-to-zero economics, and teams wanting to reduce infrastructure management overhead while maintaining enterprise-grade security controls.

2. CoreWeave

CoreWeave operates as a publicly traded AI cloud provider (Nasdaq: CRWV) with a market capitalization of approximately $48.5 billion as of August 21, 2026, and reported $5.131 billion in fiscal 2025 revenue. The company positions itself for enterprise-scale distributed training with Kubernetes-native infrastructure and contracts with major AI labs including OpenAI and Meta.

Core Capabilities

  • Kubernetes-native platform: managed Kubernetes with HPC integration and a single control plane for orchestration
  • Large GPU clusters: distributed clusters that CoreWeave says can scale across clusters totaling 100,000+ GPUs using NVIDIA InfiniBand
  • Recent hardware generations: NVIDIA HGX B300 became generally available in March 2026, bringing NVIDIA Blackwell Ultra architecture to CoreWeave Cloud, with those deployments using Quantum-X800 InfiniBand and NVIDIA ConnectX-8 SuperNICs
  • Enterprise reliability: managed cluster health, observability, and support capabilities for production AI workloads

Enterprise Focus

CoreWeave earned placement as a "Visionary" in Gartner's 2026 Cloud AI Infrastructure Magic Quadrant. The platform serves organizations requiring large-scale distributed training with tight GPU-to-GPU communication and enterprise compliance requirements. Best For: Large enterprises requiring 100+ GPU clusters with InfiniBand networking, organizations committed to Kubernetes orchestration, and teams with substantial infrastructure budgets prioritizing enterprise support and managed reliability.

3. Lambda AI

Lambda AI (Lambda Labs) provides GPU cloud infrastructure alongside hardware sales. The company raised more than $1.5 billion in a November 2025 Series E and has been reported at a $5.9 billion valuation on that financing. It has also raised substantial debt financing, including a $1 billion senior secured credit facility announced in May 2026. The company emphasizes a preconfigured environment approach through its Lambda Stack.

Key Features

  • Lambda Stack: preconfigured deep learning environment with PyTorch, TensorFlow, CUDA, and associated ML tooling preinstalled
  • 1-Click Clusters: dedicated InfiniBand-connected clusters spanning 16 to 2,000+ NVIDIA GPUs, with current 1CC documentation specifying NVIDIA Quantum-2 InfiniBand for H100 and B200 infrastructure and next-generation GB300 infrastructure using Quantum-X and Quantum-X Photonics
  • Simple provisioning: SSH-accessible on-demand instances and Jupyter environments with self-serve launching
  • Research-oriented: academic pricing options and published rate structures

Developer Experience

Lambda AI's VM-based approach provides direct SSH access and traditional infrastructure management, with capacity provisioned by the user rather than autoscaled by the platform. Best For: Research teams and academic institutions preferring traditional VM access, organizations running sustained 24/7 training workloads, and developers comfortable with SSH-based workflows.

4. RunPod

RunPod offers flexible GPU cloud computing with serverless, pod-based, and cluster options across 31 global regions. The platform's FlashBoot technology supports resuming pre-warmed serverless workers.

Platform Options

  • Serverless GPU: pay-per-second compute with automatic scaling, including scale to zero, for inference workloads
  • Secure Cloud: enterprise-tier GPU pods on vetted infrastructure, with RunPod currently advertising 99.9% uptime and documenting independently audited SOC 2 Type II compliance alongside ISO 27001 and PCI DSS aligned or certified partners
  • Community Cloud: marketplace-style GPU rental from distributed providers
  • GPU Clusters: self-service clusters up to 64 GPUs with account and spend-limit requirements, and reserved clusters that can scale to 10,000+ GPUs

Technical Capabilities

Best For: Docker-native teams wanting container portability, organizations requiring global access, and developers seeking flexible options between serverless and dedicated instances.

  • FlashBoot worker resume: RunPod explains that FlashBoot pauses rather than stops a worker, leaving model state loaded and then unpausing the container
  • Docker-first approach: bring your own containers
  • Global coverage: 31 regions for worldwide access
  • Per-second billing: granular compute charges without hourly minimums, available for conventional GPU instances as well as serverless infrastructure

5. Nebius

Nebius Group N.V. operates as a publicly traded cloud GPU provider (NASDAQ: NBIS) running a global AI cloud. Its cloud regions span Finland, France, Israel, and Missouri, with additional expansion in the UK and Asia-Pacific and continued North American infrastructure growth. The company achieved NVIDIA Exemplar Cloud status on GB300 NVL72 for training workloads in Q1 2026 and holds a 4.8/5 Gartner Peer Insights rating based on eight ratings.

Core Offerings

  • Managed Kubernetes: enterprise Kubernetes orchestration with Slurm-on-Kubernetes support for HPC workloads
  • InfiniBand networking: high-bandwidth GPU interconnects for distributed training
  • EU regions and GDPR-oriented controls: European data centers and data portability mechanisms for customers with residency requirements
  • NVIDIA partnership: NVIDIA Exemplar Cloud status on GB300 NVL72 for training workloads

Enterprise Capabilities

Nebius serves enterprises across Europe, North America, and the Middle East, including customers with data sovereignty and regulatory compliance requirements, with GPU capacity offered across on-demand and reserved arrangements. Best For: Organizations needing a global AI cloud with EU regions and GDPR-oriented controls, enterprises needing managed Kubernetes with Slurm orchestration, and teams running InfiniBand-connected distributed training.

6. Vast.ai

Vast.ai operates a peer-to-peer GPU marketplace connecting compute suppliers with AI developers. Its marketplace model uses dynamically priced capacity across 20,000+ GPUs, with rates set by supply and demand.

Marketplace Model

  • Peer-to-peer rental: GPU providers list available capacity at real-time market rates
  • Wide GPU selection: variety of hardware configurations from consumer to datacenter-grade
  • Flexible configurations: custom instance specifications based on marketplace availability
  • No long-term contracts: pure pay-as-you-go without reserved capacity requirements

Reliability Considerations

Vast.ai offers Verified Hosts, which are manually tested for reliability, and Secure Cloud capacity built on professionally managed data-center partners for workloads with stronger operational control requirements, and it now markets serverless and production capabilities alongside ordinary marketplace instances. On compliance, Vast.ai completed SOC 2 Type I in April 2025 and subsequently achieved SOC 2 Type II certification; its compliance page also states support for HIPAA-covered workloads on the Secure Cloud tier for qualifying customers. Best For: Cost-sensitive experimentation and research, checkpoint-friendly batch workloads on ordinary marketplace capacity, and teams using Verified Hosts or Secure Cloud for additional operational and compliance controls.

7. Hyperstack

Hyperstack (part of NexGen Cloud) provides GPU cloud infrastructure across North American and European data centers, with sustainably powered regions and high-bandwidth networking options. The platform offers both reserved and spot instance options.

Infrastructure Features

  • Sustainably powered regions: Hyperstack markets renewable and hydro-powered servers, with region documentation listing NORWAY-1 and CANADA-1 as sustainably powered
  • Up to 350 Gbps networking: inter-VM bandwidth on compatible SR-IOV network-optimized flavors, with setup covered in Hyperstack's SR-IOV tutorial
  • VM Hibernation: supported on eligible VM flavors, preserving configuration and root-disk state while deallocating CPUs, GPUs, and RAM and pausing compute billing
  • B200/B300 availability: current hardware documentation lists both B200 (Blackwell) and B300 (Blackwell Ultra) flavors, with B300 available on demand as of August 2026

Regional Focus

Hyperstack serves European enterprises with data residency requirements and organizations prioritizing environmental sustainability in their compute infrastructure choices. Best For: Organizations prioritizing sustainably powered regions, European teams with data sovereignty requirements, and workloads benefiting from VM hibernation on eligible flavors.

Why Modal Stands Out for Cloud GPU Computing

Code-First Serverless Architecture

Modal is a code-first serverless AI infrastructure platform built for GPU inference, training, batch processing, and sandboxed execution. Its decorator-based SDK lets you define Functions, GPU resources, scaling behavior, and container Images directly in code, with SDKs and code-defined infrastructure in Python, TypeScript, and Go and no Kubernetes manifests or separate infrastructure configuration layer. This architecture is designed to reduce infrastructure setup and shorten development feedback loops relative to traditional VM-based or Kubernetes-native workflows. Modal says teams can ship a first app in minutes, and Modal Notebooks can start GPU ML workloads in under five seconds.

True Scale-to-Zero Economics

Modal's automatic scaling addresses the cost inefficiency of always-on GPU infrastructure. Functions scale to zero by default, and compute charges stop once containers terminate. The scaledown window is configurable from 2 seconds to 20 minutes, and warm-capacity settings such as min_containers give teams direct control over how much capacity stays ready for latency-sensitive endpoints. Modal is 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. For interactive endpoints where model-loading time matters, Memory Snapshots or warm containers keep responses immediate. This model proves particularly cost-effective for workloads with variable traffic patterns. Teams running bursty inference APIs, parallel batch jobs, or development workloads can see meaningful cost reductions compared to hourly-billed alternatives, with the exact crossover depending on GPU type, rates, and workload shape.

Proven Enterprise Scale

Tens of thousands of developers and companies use Modal, across generative AI inference, LLM fine-tuning, computational biotech, and media processing. Production coding agents run on the same infrastructure: Ramp uses Modal Sandboxes for a full-context background coding agent that generates code changes and writes them back into commits and pull requests, and Lovable uses Modal Sandboxes as preview environments for generated apps and websites. Modal's Sandboxes product page advertises 100k+ concurrent sandboxes, and in a July 2026 engineering benchmark of its newer sandbox system, Modal ran one million concurrent sandboxes and created all one million in under a minute. Enterprise deployments benefit from a completed SOC 2 Type 2 audit, HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement, and Okta SSO plus embedded ML engineering services included with Enterprise plans.

Complete AI Infrastructure Platform

Beyond raw compute across CPUs and GPUs, Modal provides the broad set of primitives needed for production AI applications:

  • Volumes: distributed, persistent storage for model weights and datasets
  • Queues and Dicts: coordination and distributed state primitives for parallel workloads
  • Web endpoints: built-in HTTPS endpoints that expose Modal Functions over HTTP
  • Tunnels: expose container TCP ports with TLS termination for development, debugging, and sandbox-backed previews, with connection tokens available for authenticating access
  • Cron scheduling: automated job execution for batch processing

This integrated approach to compute, storage, coordination, networking, endpoint, and scheduling primitives reduces the number of separate infrastructure services teams need to assemble for AI workloads, and it connects cleanly with external identity, database, object storage, and monitoring systems, including log export to tools teams already run.

Free Tier for Evaluation

Modal's Starter plan has a $0 base subscription plus usage and includes $30 per month in free compute credits. This keeps evaluation low-friction for teams exploring serverless GPU infrastructure before committing to production deployments. For AI teams, Modal combines code-first infrastructure, serverless autoscaling, per-second compute billing, fast cold starts, modern GPU access, and Enterprise security controls in one platform. Explore the Modal documentation to get started in minutes.

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Frequently Asked Questions

What distinguishes serverless GPU platforms from traditional GPU cloud providers?

Serverless GPU platforms like Modal handle container orchestration, scaling, and capacity management automatically. You deploy code rather than managing virtual machines or Kubernetes clusters. The key differences include automatic scale-to-zero, per-second billing granularity with no minimum usage increments, and less infrastructure management overhead. VM-based and Kubernetes-based GPU deployments typically require more infrastructure management than fully managed serverless offerings, although several cloud GPU providers now offer both managed or serverless and traditional infrastructure options, including CoreWeave Serverless Inference, RunPod Serverless, Nebius AI Cloud 3.5 serverless AI, and Vast.ai, which markets serverless infrastructure alongside its marketplace instances.

When does per-second GPU billing provide cost advantages over hourly billing?

Scale-to-zero and fine-grained billing tend to be most advantageous for spiky, intermittent, or unpredictable workloads. There is no universal break-even utilization rate: the crossover varies by GPU SKU, per-second versus hourly rate, minimum worker count, model-loading time, warm-container policy, storage and network charges, reservation or commitment discounts, concurrency efficiency, and workload duration. Development workloads, inference APIs with variable traffic, and batch jobs that don't run continuously benefit most from this model.

What security certifications should enterprise teams require from GPU cloud providers?

Enterprise deployments should prioritize SOC 2 Type II certification, which validates security controls through extended audit periods. For organizations handling healthcare data, Business Associate Agreement coverage is the relevant control: Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Additional considerations include encryption in transit and at rest, gVisor-based compute isolation for untrusted or AI-generated code, region pinning for data residency, and SSO integration for access management.

Can existing containerized applications migrate to serverless GPU platforms?

Modal can run workloads from existing compatible registry images and can build Modal Images from Dockerfiles, providing a migration path for existing containerized applications. Teams can start from an existing image, then gradually refactor to Modal's decorator-based approach for full serverless benefits. Modal's container build and runtime system is purpose-built for serverless execution rather than a conventional managed Docker service, which is what lets images be built, cached, and brought online quickly at scale.

Which cloud GPU provider works best for distributed multi-node training?

Multi-node distributed training with tight GPU-to-GPU communication benefits from InfiniBand networking, which CoreWeave, Lambda AI, and Nebius provide for large-scale clusters. Modal supports up to eight GPUs per container for several supported GPU families and offers multi-node clusters with a 3,200 Gbps RDMA network, scaling from a single GPU to large multi-GPU configurations for distributed training workloads, all defined in the same code-first SDK teams already use for inference and batch processing.

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