This guide examines seven GPU cloud platforms that serve different startup needs in 2026. It starts with Modal, a serverless AI infrastructure platform used by thousands of customers, with a container runtime engineered for fast cold starts and a code-first developer experience that reduces traditional infrastructure complexity.
Key Takeaways
- Code-first platforms can shorten time-to-deployment: Modal's code-first workflows are designed to compress deployment cycles through code-defined infrastructure, with no Dockerfile or YAML required and SDKs available in Python, TypeScript, and Go; Modal says startups can turn ideas into production systems in hours
- Serverless architecture reshapes cost economics: Scale-to-zero can eliminate idle compute charges on services that support it, though total cost may still include storage, networking, plan, or other resource charges, and the economics versus committed capacity depend on utilization and provider pricing
- Sandboxes isolate untrusted code, and GPUs are an option rather than a prerequisite: Sandboxing provides isolation for AI-generated or otherwise untrusted code, and sandboxed workloads can run on CPU and/or GPU compute. GPU acceleration is valuable when the sandboxed workload itself requires GPU compute, but it is not a prerequisite for coding agents generally
- Compliance groundwork reduces friction, not responsibility: SOC 2 reports and HIPAA-capable services with Business Associate Agreements, such as Modal's, can reduce compliance friction, but customers remain responsible for their own regulatory obligations, configurations, risk analysis, policies, and data-handling controls
- Unified platforms can reduce operational complexity: Platforms supporting inference, training, batch processing, and sandboxes in one stack can reduce the integration burden of stitching together multiple point solutions, even though applications still integrate model runtimes, databases, observability, authentication, and external APIs
1. Modal
Modal delivers a serverless AI infrastructure platform that transforms how startups build and deploy GPU-powered applications. The platform's code-first approach lets developers define compute environments and GPU requirements directly in code, with no Dockerfile or YAML required and no infrastructure management, while existing Dockerfiles remain supported for teams that already have them. Modal has raised over $466 million. In May 2026 it announced a $355 million Series C at a $4.65 billion post-money valuation; its prior September 2025 Series B was $87 million at a $1.1 billion post-money valuation.
How Does Modal Work?
Modal takes your code, packages it into containers, and executes it in the cloud with automatic scaling. The platform handles capacity decisions across a multi-cloud GPU pool, with a container runtime engineered for fast cold starts, plus instant autoscaling without manual configuration. End-to-end cold-start time also reflects image loading, model weights, application initialization, and GPU initialization.
- Deployment: Define GPU requirements in code with decorators and deploy without separate infrastructure configuration
- Scaling: Automatic scaling from zero to thousands of GPUs based on demand
- Billing: Per-second metering with Functions that scale to zero by default, so there is no compute charge once resources have scaled down; warm containers, idle buffers, and longer scale-down windows are billed only while those resources remain provisioned
- Observability: Built-in logging and visibility across Functions, Sandboxes, and containers for production deployments
Core Capabilities
Modal offers a unified platform spanning multiple AI workload types:
- Inference: Low-latency serving with Memory Snapshots and Alpha GPU Memory Snapshots available to accelerate compatible GPU initialization workloads, supporting real-time, dynamically batched, and offline inference patterns
- Training: Single-node training plus multi-node GPU training in Beta, including B200, H200, and H100 clusters featuring InfiniBand and private networking
- Sandboxes: Secure, dynamically defined containers for AI-generated code execution at massive scale, running on CPU and/or GPU compute, with code inside a Sandbox free to use whatever runtime or language the workload requires
- Batch Processing: Large-scale batch jobs with built-in queues supporting up to 1M inputs and instant scaling across thousands of containers
- Notebooks: Collaborative, GPU-backed notebooks with serverless billing and automatic idle shutdown
Security and Compliance
Modal maintains a robust security posture designed for enterprise requirements:
What Makes Modal Unique
- AI-Native Architecture: Custom file system, container runtime, scheduler, and image builder engineered specifically for AI workloads
- 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
- Multi-Language SDKs: Modal supports code-defined infrastructure through SDKs in Python, TypeScript, and Go, covering calls to Modal Functions, Sandbox operations, and management of Modal resources, rather than acting as thin API wrappers
- Cloud Marketplace Integration: Enterprise customers can transact through the AWS or Google Cloud Marketplaces and apply eligible existing AWS or GCP spend commitments to Modal usage
- Sandboxes Across CPU and GPU: Modal natively supports Sandboxes with GPUs attached for agent and dynamic-code-execution workloads, alongside CPU-only Sandboxes, and says it is the only platform where sandboxes and training infrastructure are native to the same stack
Best For: Startups prioritizing developer velocity and time-to-market that want to ship GPU-powered applications quickly, with a unified platform that scales from prototype to production.
2. Runpod
Runpod provides a full-lifecycle GPU platform spanning Pods, Serverless, and Clusters. The company announced in 2026 that it serves more than one million developers, and it emphasizes flexibility across deployment models.
Key Features
- FlashBoot Technology: A worker-resume capability that supports cold starts for Serverless workloads
- Deployment Options: Choose between persistent GPU Pods, serverless endpoints, or multi-node clusters
- Community Cloud: Access GPU capacity through community-provided infrastructure
- Secure Cloud: A deployment tier positioned for production workloads, which a July 2026 Runpod guide describes as carrying an SLA
- Template Library: Reusable GPU Cloud and Serverless templates for popular models and frameworks
Platform Architecture
Runpod offers three primary deployment models to match different workload requirements:
- GPU Pods: Persistent containers with root-level control and customizable environments
- Serverless: Auto-scaling endpoints with scale-to-zero and usage-based billing
- Clusters: Multi-node configurations for distributed training, introduced in July 2026
Use Cases
Runpod's flexible architecture supports diverse deployment patterns:
- Production inference with high-throughput requirements
- Large-scale image generation workloads
- Model fine-tuning and experimentation
- Development environments with GPU access
Best For: Teams that want flexible deployment options across serverless, persistent, and cluster configurations, with a large community ecosystem.
3. Vast.ai
Vast.ai operates a market-driven GPU marketplace connecting compute providers with AI developers, and it now also offers managed Serverless and Clusters products. Vast reported nearly 20,000 GPUs across about 5,000 machines and more than 1,000 independent hosts in March 2026, with availability spanning a broad range of GPU types.
Key Features
- Marketplace Model: Supply-and-demand pricing with fixed-price on-demand offers and bid-based interruptible capacity
- GPU Variety: Access to a broad range of GPU types from consumer to enterprise grade
- Interruptible Instances: An option for fault-tolerant workloads
- Managed Serverless: Automatic provisioning and orchestration of GPU inference capacity
- Clusters: A dedicated product for large-scale distributed GPU workloads
- Docker-Based Deployment: Standard container workflows for portability
- Real-Time Pricing: API-queryable pricing for programmatic resource selection
Marketplace Dynamics
The market-driven model creates distinctive characteristics:
- Host characteristics can vary across independent providers
- Pricing varies with supply and demand
- Interruptible instances suit restartable or checkpointable workloads, while on-demand capacity is intended for workloads requiring continuity
- Cost management is an ongoing part of working with marketplace capacity
Considerations
The marketplace approach has a few defining traits:
- Interruptible instances can be preempted, so checkpointing and restart logic are relevant for long-running jobs
- Direct marketplace instances remain relatively infrastructure-oriented, and Vast also provides managed Serverless orchestration for inference and a Clusters product for distributed GPU workloads
- The raw marketplace tier is oriented toward teams comfortable with hands-on infrastructure work
- The interruptible marketplace suits cost-sensitive, restartable batch work, while on-demand GPU Cloud and Serverless products also target production inference
Best For: Budget-conscious startups running experimental or restartable batch workloads on interruptible capacity, with on-demand and Serverless options available when continuity or production inference is required.
4. Nebius
Nebius delivers AI cloud infrastructure as a Reference Platform NVIDIA Cloud Partner. The platform emphasizes non-virtualized GPU access and InfiniBand networking for large-scale training.
Key Features
- NVIDIA Reference Platform: Infrastructure based on NVIDIA-validated reference architectures designed for AI workloads
- Non-Virtualized GPUs: Direct hardware access without a virtualization layer
- InfiniBand Networking: InfiniBand interconnect for distributed training
- Managed Services: Managed Kubernetes and managed Slurm tooling through Soperator, plus MLOps services such as Managed MLflow, are available
- Dedicated Support: Solution-architect and engineering support for production AI deployments
Enterprise Customer Base
Nebius powers AI infrastructure for established companies:
- Revolut, which Nebius described as a customer in June 2026, runs production AI workloads on Nebius infrastructure
- Shopify uses Nebius GPU infrastructure for AI experimentation and training workloads
- Cursor has been named as a Nebius customer
Implementation Experience
Enterprise customers report efficiency gains:
- Nebius stated in March 2026 that RoboForce reduced its AI pipeline setup time
- Managed infrastructure can reduce operational overhead
- Dedicated support can accelerate time to production
Best For: Established startups with enterprise requirements seeking dedicated GPU infrastructure with hands-on support and NVIDIA-validated hardware configurations.
5. DigitalOcean GPU Droplets
DigitalOcean extends its cloud platform with GPU Droplets, offering tooling and ecosystem integration that will be familiar to teams already using DigitalOcean services.
Key Features
- Simple Provisioning: GPU access through the familiar Droplet interface
- Ecosystem Integration: Works with existing DigitalOcean databases, networking, and Kubernetes
- Pre-Configured Images: Ready-to-use AI/ML images with popular frameworks
- Multi-GPU Options: Configurations up to 8x GPU instances
- SOC 2 Compliance: GPU Droplets were included within DigitalOcean's SOC 2 compliance scope in a March 2025 announcement
Platform Integration
DigitalOcean GPU Droplets connect with the broader ecosystem:
- Managed Kubernetes (DOKS) with GPU worker nodes for orchestration
- Managed databases for model metadata and logging
- VPC networking for secure multi-service architectures
- Terraform provider for infrastructure-as-code workflows
Deployment Model
The Droplet model differs from serverless approaches. These points apply specifically to GPU Droplets rather than to DigitalOcean as a whole, since the wider platform also includes serverless AI and inference services:
- VM-based GPU compute resources rather than scale-to-zero serverless workers
- Billing follows the standard Droplet model, which is tied to provisioned resources rather than request-driven usage
- Per-second billing after a minimum charge period
- On-demand GPU Droplets are available in selected regions, subject to GPU model; Spot and contract GPU capacity have separate regional availability
Best For: Startups already invested in the DigitalOcean ecosystem seeking GPU access through a familiar interface without adopting new platforms.
6. Koyeb
Koyeb offers a serverless platform with GPU support. On February 17, 2026, Koyeb announced a definitive agreement to be acquired by Mistral AI, with completion subject to closing conditions. The platform emphasizes PaaS-style deployment with built-in orchestration and auto-scaling.
Key Features
- Serverless Architecture: Auto-scaling with health checks and rolling deployments
- GPU Scale-to-Zero: Documented scale-to-zero support for serverless GPU workloads
- Native CI/CD: GitHub integration for automated builds and deployments
- JavaScript and TypeScript SDK for Sandboxes: Koyeb Sandboxes provides a JavaScript SDK with TypeScript support, alongside its Python Sandbox SDK
- Global Edge Network: Distributed infrastructure with global routing
Platform Capabilities
Koyeb provides application platform features alongside GPU compute:
- Built-in load balancing and TLS termination
- Environment variable and secrets management
- Custom domain support with automatic certificates
- CPU and GPU sandbox execution
- Real-time logs and metrics
Recent Developments
The Mistral AI agreement signals investment in AI infrastructure:
- Scale-to-zero available for GPU workloads
- Increased focus on AI workload optimization
- Koyeb said it would gradually transition to become a core part of Mistral Compute
Best For: Startups seeking a PaaS-style deployment experience with GPU support and integrated application platform features.
7. Lambda
Lambda provides GPU cloud infrastructure with a focus on machine learning research and training workloads. The company moved from lambdalabs.com to lambda.ai in March 2025 and dropped "Labs" from its name. Founded in 2012, it has long-standing ties to the ML community.
Key Features
- Practitioner Focus: Lambda positions compute as a specialized rather than commodity input for AI teams
- Zero Egress Fees: No data transfer charges for model artifacts and datasets
- Pre-Configured Environments: Preconfigured AI software environments are available on products such as 1-Click Clusters
- 1-Click Clusters: Simplified multi-node deployment for distributed training
- Persistent Storage: Persistent filesystems for large datasets and checkpoints
Training Focus
Lambda emphasizes long-running training workloads:
- Virtualized on-demand GPU instances alongside bare-metal, 1-Click Cluster, and large dedicated infrastructure products, spanning multiple on-demand GPU instance configurations, including multi-GPU options
- Persistent environments for iterative development
- Reserved capacity options for predictable availability
- A Research Grant Program that provides qualifying researchers and research groups with cloud-compute credits
Deployment Model
The instance-based cloud model differs from serverless approaches:
- Instance-based GPU pricing expressed per GPU-hour rather than request-driven scale-to-zero, with billing granularity that varies by product
- Resources remain allocated until the instance is terminated through the Lambda Cloud API
- Self-serve on-demand instances are customer-managed, and Lambda also offers managed and preconfigured cluster products, including 1-Click Clusters, Reserved Private Cloud, and Superclusters
- Well suited to sustained GPU utilization
Best For: Research-focused startups running long training jobs that benefit from zero egress fees and preconfigured ML environments.
Why Modal Stands Out for AI Startups
Code-First Developer Experience
Modal's code-first SDKs reduce the infrastructure complexity that slows down AI development. Developers define GPU requirements, container images, and scaling behavior directly in code using decorators, with code-defined infrastructure supported in Python, TypeScript, and Go. Modal says this code-first approach lets startups turn ideas into production systems in hours, and that developers can ship their first app in minutes.
True Serverless Architecture
Modal delivers genuine serverless GPU computing with instant autoscaling from zero to thousands of containers. Per-second metering combined with Functions that scale to zero by default means startups incur no compute charges once resources have scaled down, which can transform the cost economics of bursty inference workloads. For applications with variable traffic patterns, this architecture can reduce infrastructure costs relative to always-on reserved capacity, and teams retain direct control over how much warm capacity they keep provisioned.
Sandboxes for AI Agents on CPU and GPU
Modal's Sandbox capability provides isolated environments for executing AI-generated code, and Sandboxes can be run with GPUs attached when the workload calls for it, alongside CPU-only configurations. Code running inside a Sandbox is not limited to a single programming language, so an agent can use whatever runtime the workload requires. This supports building coding agents and secure AI applications that need dynamic code execution, whether the agent runs inside the sandbox or outside it with the sandbox reserved for untrusted code.
Teams run this pattern in production today. Lovable uses Modal Sandboxes as preview environments for generated apps and websites, and Ramp uses them for background coding agents that generate code changes and write them back into commits or pull requests, described in the write-up on Ramp's background coding agent.
Filesystem, directory, and memory snapshots (memory snapshots are in alpha) let teams restore sandbox state quickly instead of rebuilding it from scratch, and directory snapshots can be mounted after a sandbox has started, so project-specific state can be attached to pre-started sandboxes. Sandboxes can run a server exposed through a URL, with tunnels and connection tokens available to authenticate access to sandbox-backed previews embedded in an application. Sandboxes are charged by CPU and memory consumption by the second, with tunable requests and headroom to burst above them. Modal supports scaling to 50,000+ concurrent Sandboxes.
Enterprise-Ready Security
Modal's security architecture supports startups as they grow into regulated markets. A completed SOC 2 Type II audit, HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement, gVisor-based isolation, and TLS 1.3 encryption provide the foundation for serving enterprise customers. Region pinning keeps a sandbox in the region you choose, and sandbox tunnel traffic connects directly to the sandbox. Customers remain responsible for their own regulatory obligations, risk analysis, and data-handling controls.
Production-Proven Scale
Modal powers production workloads for companies including Decagon, Runway, and Physical Intelligence. Modal's joint case study reports that Decagon Voice 2.0 achieved a 65% latency reduction following combined model-training and runtime work, rather than as a generic platform benchmark. Modal announced in March 2026 that Runway chose Modal to power real-time inference for Runway Characters, moving from proof of concept to production in under 30 days, and its April 2026 case study reports roughly 10 to 15 ms of network overhead for real-time robot inference at Physical Intelligence.
For AI startups evaluating GPU cloud platforms, Modal's combination of developer velocity, serverless economics, unified capabilities, and enterprise security creates a foundation that supports growth from first prototype to production scale. Explore Modal's customer stories to see how teams ship GPU-powered applications faster.
Explore Modal's serverless AI infrastructure for GPU-powered applications.
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