Key Takeaways
- Code-first SDKs reduce deployment boilerplate: Code-first SDKs and decorator-based deployment models can cut infrastructure boilerplate compared with workflows that require manually maintaining container and infrastructure configuration. The distinction is narrowing, though: Vast.ai added a Python
@remote deployment path in April 2026 while still supporting Docker image definitions - Cold start performance depends on what is being measured: Infrastructure startup, snapshot restore, and end-to-end model-ready readiness are three different quantities. End-to-end model-ready cold starts depend on image state, weights, compilation, GPU capacity, and cache hits, so a single headline number rarely describes a full workload
- Per-second metering is common, but billing units differ: Per-second metering is common among the platforms reviewed, though exact billing quanta and chargeable resources vary, and Cerebrium's 2026 buyer's guide notes that GPU-second price alone excludes idle behavior, startup time, platform fees, and infrastructure-management differences
- Compliance certifications matter for regulated procurement: SOC 2 Type II certification and HIPAA pathways matter for regulated buyers, but they do not draw a clean enterprise boundary on their own. Enterprise readiness also depends on SLA scope, tenancy and isolation, residency, support, networking, and auditability
- Comprehensive tooling reduces operational complexity: Platforms bundling secrets management, persistent volumes, scheduled jobs, and observability can reduce the number of external integrations required, though production stacks typically still involve CI/CD, gateways, model registries, and data systems
1. Modal
Modal delivers AI infrastructure that developers love, with a code-first SDK that makes deploying GPU inference feel as natural as writing local code. The platform powers cloud infrastructure for thousands of customers across generative AI inference, LLM fine-tuning, computational biotech, and media processing.
How Does Modal Work?
Modal takes your code, puts it in a container, and executes it in the cloud with automatic scaling. The platform handles capacity decisions across a multi-cloud GPU pool, abstracting cluster provisioning, scheduling, capacity management, and autoscaling.
Core 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, plus Memory Snapshots and the full set of documented cold start optimization techniques including warm-container controls
- Autoscaling from zero to 1,000+ GPUs, with the ability to burst to thousands of GPUs as demand rises, then back down to zero
- Code-defined infrastructure through SDKs in Python, TypeScript, and Go, including a Python decorator model (
@app.function) for defining compute and GPU requirements inline, with no YAML-based infrastructure definitions required (client credentials and settings live in a small .modal.toml file) - All-language workload support: Modal's SDKs are code-first rather than Python-only, and containers can run whatever runtime or language a workload requires
- Comprehensive primitives including Volumes, Queues, Dicts, and web endpoints
- Multi-cloud capacity pool providing elastic access to B300, B200, H200, H100, RTX PRO 6000, A100, L40S, A10, L4, T4, and other GPU types
Developer Experience
Modal's SDK enables developers to deploy inference endpoints with minimal boilerplate. The platform supports multiple inference patterns:
Security and Compliance
Modal has successfully completed a SOC 2 Type II audit, with no deviations found. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Compute isolation uses gVisor-based sandboxing, with public Modal APIs protected by TLS 1.3 encryption.
What Makes Modal Unique
- Custom infrastructure stack: Modal built its own file system, container runtime, scheduler, and container image builder, all optimized for AI workloads
- Memory snapshotting: CPU Memory Snapshots capture container state and restore future containers into it, cutting initialization overhead for initialization-heavy Functions
- AI-native container runtime: purpose-built for inference and training workloads rather than general-purpose computing
- Deep tooling integration: Secrets, volumes, scheduled jobs, and distributed workloads in one unified platform
Best For: ML teams that want code-defined infrastructure, serverless GPU autoscaling, and enterprise security features in one substrate that handles inference, training, batch processing, and scheduled jobs.
2. RunPod Serverless
RunPod Serverless offers on-demand GPU compute with a focus on startup optimization and broad GPU availability across 31+ global regions.
Key Features
- FlashBoot technology for startup optimization on snapshot-hit paths
- 13 GPU tiers, including B300
- Secure Cloud tier with enterprise SLA options
- Custom container support via Docker images
- REST API v2 in public beta as of August 13, 2026, covering Pods, Serverless, storage, templates, registries, hardware catalog, and billing, with legacy GraphQL still available
Platform Architecture
RunPod provides both Community Cloud (marketplace-style) and Secure Cloud (enterprise-grade) deployment options. The Secure Cloud tier includes dedicated infrastructure with published uptime SLAs.
Cold Start Performance
RunPod's FlashBoot supports startup optimization on paths where snapshots hit. As with every platform in this category, snapshot-hit startup and end-to-end model-ready readiness measure different things, with end-to-end readiness shaped by image state, weight loading, and compilation. Keeping active workers warm removes the cold start from the request path entirely.
Best For: Teams prioritizing snapshot-optimized startup and requiring broad GPU selection across multiple global regions with flexible deployment tiers.
3. Vast.ai Serverless
Vast.ai operates a GPU marketplace model connecting users to compute capacity from distributed hosts, with a Serverless inference layer that launched in December 2025 across a fleet of more than 17,000 GPUs, 1,400+ providers, and 500+ locations.
Core Capabilities
- Multiple pricing modes: On-Demand, Interruptible, and Reserved Serverless pricing, with bidding used specifically for interruptible capacity
- Broad GPU catalog spanning consumer and datacenter hardware
- Global host network spanning multiple continents
- Per-second billing with scale-to-zero capability and predictive scaling
- Docker container support, plus a Python
@remote deployment path added in April 2026
Marketplace Dynamics
Vast.ai's approach differs from managed platforms by aggregating GPU capacity from independent providers, which gives teams a wide range of hardware options and pricing modes. For higher-assurance workloads, Secure Cloud uses vetted datacenter partners meeting stated security standards, and Vast documented SOC 2 Type II support at its Serverless launch.
GPU Variety
The platform's catalog includes everything from consumer RTX cards to enterprise H100s, providing flexibility for teams with specific hardware requirements or budget constraints.
Best For: Teams seeking broad GPU variety and marketplace flexibility across a distributed provider network.
4. Beam Cloud
Beam Cloud provides inference infrastructure with an open-source runtime (beta9, AGPL-3.0) and bring-your-own-cloud (BYOC) deployment options, the combination it positions as its core differentiation.
Key Features
- Open-source beta9 runtime enabling self-hosted deployments
- BYOC support across AWS, GCP, Azure, and Hetzner
- Cold start optimization with controllable warm pools
- Python-native SDK with decorator-based patterns
- Managed cloud option for teams preferring hosted infrastructure, with per-second billing and scale-to-zero
Self-Host Flexibility
Beam's open-source runtime addresses a specific market need: teams requiring serverless GPU abstractions while maintaining infrastructure control. This enables deployment within existing cloud accounts, addressing data residency and compliance requirements. Self-hosted and VPC-resident inference options are also available from other vendors in this category.
Inference Focus
The platform emphasizes inference-specific optimizations, including warm container pooling and cold start management for latency-sensitive workloads.
Best For: Teams requiring self-hosted serverless GPU infrastructure or BYOC deployments while maintaining the developer experience of managed platforms.
5. Cerebrium
Cerebrium positions itself as a production-grade AI deployment platform with enterprise compliance certification and developer-focused tooling.
Core Capabilities
- SOC 2 Type II audit completed in July 2026 for enterprise security requirements
- Python SDK with production deployment features
- 12+ GPU types including H100 and A100 configurations
- Per-second billing for active compute with scale-to-zero
- Monitoring and observability built into the platform, alongside multi-region deployment and routing
Enterprise Compliance
Cerebrium announced completion of its SOC 2 Type II audit on July 8, 2026, which supports organizations with strict security-review requirements.
Production Focus
The platform emphasizes production deployment controls, autoscaling, and continuous monitoring, features essential for teams operating inference endpoints at scale. Its memory snapshotting work covers restoring CUDA workloads from saved checkpoints rather than reinitializing them on every start.
Best For: Enterprise teams requiring SOC 2 Type II audited infrastructure for AI inference deployments with production-grade monitoring and compliance documentation.
6. Koyeb
Koyeb delivers a global serverless platform combining PaaS simplicity with GPU compute capabilities, deploying workloads into core regions fronted by a global edge network.
Key Features
- Global edge network that routes users to the closest core region in which the application is running
- PaaS deployment model simplifying container orchestration
- Docker and Git-based deployments for flexible workflows
- Zero-downtime deployments with built-in versioning and easy rollback
- GPU compute with scale-to-zero in public preview
Edge Architecture
Koyeb's region documentation distinguishes core locations, where applications actually run, from edge locations, which route traffic to the nearest core region. GPU inference therefore executes in core regions, and the edge network reduces network latency to those regions rather than placing GPUs at every PoP.
PaaS Simplicity
The platform abstracts infrastructure complexity through a PaaS model, enabling deployments directly from Git repositories or container registries without extensive configuration.
Buyer Context
On February 17, 2026, Koyeb announced a definitive agreement to join Mistral AI. Koyeb said its platform would continue operating while gradually transitioning to become a core part of Mistral Compute.
Best For: Teams building globally distributed AI applications that want PaaS-level simplicity with multi-region core deployments and edge routing.
7. Nebius Serverless
Nebius offers container-based serverless AI compute within a broader AI cloud, introduced on March 26, 2026 as Serverless AI Jobs and Endpoints in public preview.
Core Capabilities
- Container-based Serverless AI Endpoints with GPU acceleration, explicitly distinguished by Nebius from traditional Function-as-a-Service
- Serverless AI Jobs for batch-style work, separate from Endpoints for model serving
- Custom or public container images supplied by the user
- Pay-as-you-go compute charged while workloads run
- RTX PRO 6000, announced as part of the Aether 3.5 release, and other professional GPU options
Platform Integration
Nebius Serverless AI runs user-supplied public or custom container images, so teams can deploy the same artifacts they already build for other GPU environments. Nebius operates AI Cloud infrastructure in both Europe and the US, and its Token Factory Dedicated Endpoints announcement describes dedicated infrastructure spanning Europe and the US as a separate product line from Serverless AI.
Product Roadmap
Nebius describes Serverless AI Jobs and Endpoints as public preview and says Endpoints are particularly suited to pre-production deployments and testing, with Endpoint autoscaling, optimized startup latency, and multi-region scheduling placed on a Q4 2026 and later roadmap.
Best For: Teams already invested in the Nebius AI cloud that want to evaluate container-based serverless GPU endpoints for pre-production and testing workloads.
Why Modal Stands Out for Serverless GPU Inference
Purpose-Built AI Infrastructure
Modal's approach starts from a different foundation: the team built custom infrastructure specifically for AI workloads rather than adapting general-purpose cloud primitives. This includes a custom file system, container runtime, scheduler, and container image builder, each optimized for the demands of model inference and training.
Code-First Developer Experience
Modal's SDKs are the strongest fit for ML teams shipping production inference, and the underlying differentiators are concrete. Modal supports code-defined infrastructure through SDKs in Python, TypeScript, and Go, and the containers themselves can run whatever runtime or language a workload requires. The decorator-based model (@app.function) lets developers define compute and GPU requirements directly in code, without YAML-based infrastructure definitions, and common container environments can be built in Python rather than requiring a Dockerfile.
The platform's tooling, including secrets management, persistent volumes, scheduled jobs, and web endpoints, reduces integration overhead. Modal provides a unified substrate in place of separately assembled tooling.
Enterprise Security and Governance with Serverless Economics
Modal combines enterprise security and governance features with usage-based serverless infrastructure and marketplace procurement. The platform has successfully completed a SOC 2 Type II audit, providing audit-ready security documentation. Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA, enabling healthcare deployments.
Security architecture includes gVisor-based compute isolation, TLS 1.3 for public API communications, and encryption for data in transit and at rest. On Team and Enterprise plans, Modal provides role-based access control at both the Workspace and Environment levels.
Comprehensive Inference Patterns
Modal supports the full spectrum of inference deployment patterns:
One Platform for the Complete ML Lifecycle
Beyond inference, Modal provides a unified platform for training, batch processing, secure sandboxes, and GPU-backed notebooks, spanning compute resources across CPUs and GPUs. Teams avoid context-switching between platforms and reduce integration complexity by standardizing on a single infrastructure substrate.
Proven Enterprise Scale
Modal powers infrastructure for thousands of customers spanning generative AI inference, computational biotech, and media processing, and manages over 20,000 concurrent GPUs across its globally distributed fleet. The platform's AWS and Google Cloud Marketplace availability enables Enterprise procurement using committed cloud spend.
For teams evaluating serverless GPU platforms, Modal's combination of code-defined infrastructure, enterprise compliance, and comprehensive tooling makes it our top recommendation for organizations shipping production AI inference.
