Key Takeaways
1. Modal
Modal delivers high-performance serverless GPU infrastructure through a code-first approach that removes traditional DevOps complexity. The platform takes your code, puts it in a container, and executes it in the cloud. Container startup is engineered for fast cold starts through an optimized, content-addressed filesystem and multi-tier caching, and GPU Memory Snapshots reduce initialization work substantially for compatible workloads.
Core Capabilities
Product Portfolio
Modal offers purpose-built products for distinct AI workloads:
Security and Compliance
Modal has successfully completed a SOC 2 Type 2 audit. For HIPAA, Modal states plainly that there is no officially recognized HIPAA certification process, and Modal supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. Audit logs and Okta SSO are available as enterprise features. Compute isolation uses gVisor-based sandboxing, all user data is encrypted in transit and at rest, and public Modal APIs use TLS 1.3. Best For: ML teams prioritizing developer experience, an integrated product surface across the AI lifecycle, and enterprise governance controls.
2. RunPod Serverless
RunPod provides serverless GPU infrastructure with a focus on broad hardware availability and flexible deployment options. RunPod announced in July 2026 that its platform had crossed one million developers. That figure is a platform-wide developer metric rather than a Serverless-specific count, and it is separate from the Community Cloud and Secure Cloud tiers, which describe RunPod's Pod and GPU Cloud infrastructure.
Key Features
Platform Deployment Options
Beyond Serverless, RunPod's broader GPU Cloud offers Community Cloud and Secure Cloud Pod deployment types, allowing teams to balance cost against consistency requirements. For Serverless specifically, the relevant controls are worker configuration, active versus flex worker counts, and endpoint scaling settings. Best For: Teams needing broad GPU variety and the flexibility to mix serverless workloads with dedicated instances on a single platform.
3. Vast.ai Serverless
Vast.ai operates a GPU marketplace model connecting users with compute providers worldwide. The company reported in March 2026 that monthly active paying customers had exceeded roughly 14,000; a separate May 2026 company article cited 120,000+ AI developers actively searching for compute, which is a different metric. Its Serverless launch documented 500+ locations, and the company reported in March 2026 that its marketplace had grown to nearly 20,000 GPUs across 5,000 machines from more than 1,000 independent hosts.
Key Features
Marketplace Considerations
The marketplace model means pricing and availability vary with host supply. This creates opportunities for cost savings alongside variability across hosts. Vast.ai documents production-use considerations, including reliability filtering and its Secure Cloud option. Best For: Cost-conscious teams with tolerance for marketplace variability who prioritize hardware selection breadth over managed platform consistency.
4. Beam Cloud
Beam Cloud delivers Python-native serverless GPU infrastructure with a focus on simplicity and competitive compute rates. The platform emphasizes deployment workflows and developer-friendly APIs.
Key Features
Developer Experience
Beam's Python-native approach reduces the learning curve for ML engineers already familiar with serverless patterns, and its scheduled jobs, volumes, and checkpoint-based startup optimizations cover the common deployment primitives for recurring GPU work. Best For: Python-focused teams seeking a balance between developer experience and compute costs, particularly for scheduled batch workloads.
5. Cerebrium AI
Cerebrium focuses on real-time AI workloads, with particular optimization for voice agents, video processing, and streaming applications. The platform emphasizes inference workloads and production reliability.
Key Features
Specialized Use Cases
Cerebrium's optimization for real-time streaming workloads makes it particularly suited for voice agents, video processing pipelines, and other latency-sensitive applications. Best For: Teams building real-time AI applications, particularly voice agents and streaming inference.
6. Koyeb
Koyeb provides a global serverless platform with GPU support, emphasizing scale-to-zero billing and multi-region deployment. The platform expanded GPU availability in January 2026 with reduced pricing across several SKUs.
Key Features
Platform Focus
Koyeb's strength lies in its developer-friendly deployment experience and global distribution. As of May 2026, Koyeb labels both scale-to-zero and volumes as public-preview functionality. Best For: Teams seeking a general-purpose serverless platform with GPU support and native GitHub integration for containerized workloads.
7. Nebius Serverless AI
Nebius offers enterprise AI cloud infrastructure with serverless capabilities, backed by its corporate history and a strategic partnership with NVIDIA. Nebius Group is the renamed former Dutch parent of Yandex following the divestiture of the Russian and certain other businesses. The platform includes interactive development environments alongside its serverless Jobs and Endpoints services.
Key Features
Development Focus
DevPods give Nebius an integrated interactive Jupyter and VS Code workflow alongside Jobs and Endpoints. Nebius described DevPods, Jobs, and Endpoints as its first steps toward container-based serverless compute, with endpoints running as persistent workloads and a current single-node scope. Best For: Enterprise teams seeking interactive GPU development environments alongside serverless deployment capabilities, particularly those requiring the latest NVIDIA hardware.
Why Modal Stands Out for Serverless GPU Workloads
Code-First Developer Experience
Modal's decorator-based API lets engineers define compute requirements directly in code without external configuration files, avoiding Dockerfiles, YAML configurations, and Kubernetes manifests for supported workloads. Modal ships SDKs and code-defined infrastructure in Python, TypeScript, and Go, and the code running on Modal is not limited to a single programming language. This code-first workflow can materially shorten deployment cycles. As one concrete example, OpenArt reported moving ComfyUI workflow deployments from hours to minutes and described development speed in its own workflow as roughly 5x to 10x faster. Those figures reflect that customer's own workflow.
Production-Grade Cold Start Performance
Cold start behavior separates predictable production APIs from unpredictable services. Modal is engineered for fast cold starts and faster feedback loops: a Rust-based container runtime with a content-addressed filesystem and multi-tier caching helps containers come online quickly without letting large images slow startup down. Container reuse and configurable warm pools, controlled by parameters such as scaledown_window, min_containers, and buffer_containers, keep frequently used functions ready to serve, and GPU Memory Snapshots cut initialization work for initialization-heavy inference workloads.
Comprehensive AI Workload Coverage
Modal provides purpose-built products spanning the AI development lifecycle:
Modal integrates inference, training, batch, Sandboxes, Notebooks, storage, queues, and key-value state in one code-first platform, which can reduce infrastructure integration work compared with assembling separate services for each workload type.
Enterprise Security and Compliance
Modal has completed a SOC 2 Type 2 audit. SOC 2 is an attestation and reporting framework rather than a legally mandated certification, but enterprise buyers commonly require a current report during procurement. For healthcare workloads, Modal notes that no officially recognized HIPAA certification process exists, and Modal supports HIPAA-compliant workloads on Enterprise plans via a BAA. Governance features including Audit logs and Okta SSO are available to enterprise customers. Modal's security architecture includes gVisor-based compute isolation, TLS 1.3 for public APIs, and encryption of all user data in transit and at rest.
Scalability Without Operational Burden
Modal handles capacity management by pooling capacity across major clouds and automatically scaling workloads to zero when inputs stop and scaling out across thousands of containers. Teams access B300, B200, H200, H100, A100, and other GPU types without reservations or minimum usage-time increments, and the same platform runs CPU and GPU workloads. Modal's AWS and Google Cloud Marketplace availability, announced in January 2026 for Enterprise customers, allows enterprises to apply existing committed cloud spend to Modal usage. Modal's Starter plan also includes $30 per month of free compute. For teams that prioritize developer velocity, an integrated product surface, and enterprise governance, Modal brings inference, training, batch, Sandboxes, and Notebooks together in a single code-first platform. Explore Modal's documentation to start building, or visit the examples library for sample implementations spanning LLM inference, image generation, batch processing, Sandboxes, and other workloads.
