This guide examines seven platforms serving different AI agent infrastructure needs in 2026. These platforms fall into distinct categories: sandbox and code-execution platforms (Modal, E2B), GPU cloud and container platforms (RunPod, Beam Cloud), and managed inference and model-serving platforms (Together AI, Baseten, Replicate), with several vendors now spanning more than one category. We start with Modal, a serverless compute platform purpose-built for secure sandboxed execution with deep GPU capacity across major cloud providers.
Key Takeaways
- GPU memory snapshots can dramatically reduce cold starts: Modal's GPU Memory Snapshots, currently marked Alpha in the docs, have been observed delivering up to 10x faster cold starts for suitable initialization-heavy workloads, helping make serverless GPUs more viable for latency-sensitive AI agent applications
- Security isolation is non-negotiable for AI agents: Agents generate and execute code autonomously, requiring robust sandboxing. Modal uses gVisor-based containers while E2B uses Firecracker microVMs, giving each sandbox VM-style isolation with its own kernel, memory, and page cache
- Scale-to-zero architecture reduces costs for bursty workloads: AI agent workloads are inherently variable. By default, Modal Functions scale to zero when idle, avoiding compute charges when no containers are running, with automatic scaling to thousands of containers
- Code-first SDKs accelerate development velocity: Modal's code-first approach eliminates YAML configuration, with SDKs available in Python, TypeScript, and Go, enabling faster iteration cycles compared to container-based platforms
- Production-proven platforms reduce operational risk: Modal powers cloud infrastructure for over 10,000 teams, demonstrating enterprise-scale reliability for AI agent workloads
1. Modal
Modal delivers serverless compute infrastructure specifically engineered for AI workloads, combining secure sandboxed execution with deep GPU capacity. The platform's AI-native architecture includes a custom file system, container runtime, scheduler, and container image builder optimized for the unique demands of AI agents.
Core Capabilities
- gVisor container isolation: Secure sandboxes for running AI-generated code with full process isolation
- GPU memory snapshots: An Alpha capability that captures GPU state after model loading, observed reducing cold starts by up to 10x for suitable inference 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, supported by enabling techniques such as memory snapshotting
- Broad GPU selection: Access to T4, L4, A10, L40S, A100 variants, H100, H200, and B200 without reservations or quotas
- Code-defined infrastructure: A code-first approach with no YAML, with SDKs available in Python, TypeScript, and Go for defining and managing Modal resources, calling Functions, and running Sandboxes; code running inside a sandbox is not limited to a single programming language and can use whatever runtime or language the workload requires
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a Business Associate Agreement. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest.
Production-Proven Results
Modal powers production AI workloads for notable companies:
- Ramp built Inspect, an internal background coding agent on Modal Sandboxes that spins up full development environments in seconds and now writes over half of all merged pull requests at Ramp
- Suno shaved 4 months off its launch timeline with Modal, bringing a model to market four months early compared to building custom infrastructure
- Modal supports 100k+ concurrent sandboxes with full observability
What Makes Modal Unique
- AI-native container runtime: Custom-built infrastructure optimized specifically for AI workloads rather than general-purpose compute
- Multi-cloud GPU capacity pool: Deep capacity across major cloud providers ensures GPU availability without reservations
- Scale-to-zero economics: By default, Modal Functions scale to zero when idle, avoiding compute charges when no containers are running; teams can choose warm-container settings to reduce latency further
- Memory snapshotting: Modal Memory Snapshots can capture CPU memory state, and Modal's Alpha GPU Memory Snapshots additionally capture GPU state for compatible workloads, enabling dramatically faster cold starts on suitable initialization-heavy workloads
Best For: Teams building AI agents that require secure sandboxed execution with GPU acceleration, especially those needing fast cold starts, production-grade reliability, and enterprise compliance.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B supports cold starts for same-region sandboxes, optimized for rapid code execution cycles.
Core Capabilities
- Firecracker microVMs: E2B uses Firecracker microVMs, giving each sandbox VM-style isolation with its own kernel, memory, and page cache, using the same KVM-based technology family that powers AWS Lambda for running untrusted AI-generated code
- Open-source option: Self-hosting available under Apache 2.0 license for organizations with data sovereignty requirements
- Multi-language support: Native SDKs for Python and TypeScript/JavaScript integration
- Template system: Reproducible sandbox environments with versioning for consistent agent execution
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. E2B's pricing estimator shows 20 concurrent sandboxes included on the Hobby tier, with paid tiers listing higher included concurrency, including 100 on Pro and higher limits on Pro+, Pro++, and Enterprise.
Architecture Approach
E2B's Firecracker-based isolation provides VM-style security boundaries with a dedicated kernel per sandbox, making it well-suited for executing completely untrusted code. The platform is LLM-agnostic, working with any language model rather than being tied to a specific provider.
Best For: Teams building AI agents focused on code interpretation and execution where ephemeral sandboxes with strong security isolation are the priority over GPU acceleration.
3. RunPod
RunPod offers a hybrid serverless and dedicated GPU platform with extensive hardware selection across 30+ regions worldwide. The platform provides both on-demand serverless execution and reserved dedicated instances for different workload patterns.
Core Capabilities
- Extensive GPU selection: Access to 30+ GPU types including B200, H200, H100, and consumer-grade options like RTX 5090
- Hybrid deployment model: Choose between serverless execution with automatic scaling or dedicated pods for sustained workloads
- Container-based execution: Full Docker container support for running any language or framework
- Jupyter integration: Built-in notebook support on dedicated pods for interactive development
Performance Characteristics
RunPod supports cold starts for serverless invocations, though cold-start behavior depends on worker type, image size, model size, and whether active warm workers are configured. The platform provides both Flex Workers, which scale to zero and incur cold starts, and Active Workers, which keep instances warm to eliminate cold starts.
Architecture Approach
RunPod takes a container-first approach, giving teams full control over runtime environments. This flexibility comes with additional configuration overhead compared to function-based platforms but supports multi-language workloads beyond Python.
Best For: Teams needing maximum GPU hardware selection, hybrid serverless and dedicated deployment options, or full container runtime flexibility for non-Python workloads.
4. Beam Cloud
Beam Cloud provides open-source serverless GPU infrastructure with container startup support and signup credits for new users. The platform's beta9 runtime is fully open-source, allowing self-hosting for teams requiring complete infrastructure control.
Core Capabilities
- Open-source runtime: The beta9 platform can be self-hosted for full control over infrastructure
- Container startup: Beam documents container startup support, though total cold-start time depends on image loading and application or model initialization
- GPU memory snapshots for selected configurations: Support for warm function pools and checkpoint restore or memory snapshotting to reduce latency, documented for particular GPU types such as RTX 4090, H100, and A10G rather than universally across all GPU types
- Usage-based billing: Beam does not charge for machine-start wait time or image pulling, though container initialization and application startup work can still be billable
Architecture Approach
Beam Cloud uses container-based isolation with a focus on keeping costs low through efficient resource utilization. The platform supports Jupyter-related workflows for interactive agent development. Beam offers signup credits, documented as 15 hours of free credit on signup, which is a one-time credit rather than a recurring free tier.
Production Considerations
Beam's open-source beta9 runtime is available for self-hosting under AGPL-3.0. The open-source model provides transparency and self-hosting flexibility that some organizations require.
Best For: Teams seeking open-source serverless GPU infrastructure with low costs and the option to self-host.
5. Together AI
Together AI provides managed LLM inference with optimized inference engines and, more recently, code-execution and configurable-infrastructure products. The platform serves open-source models through OpenAI-compatible APIs and also offers sandboxed code execution.
Core Capabilities
- Optimized inference stack: Custom kernels and FlashAttention-3 integration for model serving
- OpenAI-compatible APIs: Drop-in replacement for existing OpenAI integrations with open-source models
- 100+ model library: Extensive selection of pre-deployed open-source models ready for inference
- Code execution products: Together Code Sandbox and Together Code Interpreter let users run code, install dependencies, run servers, and execute Python in sandboxed environments
- GPU cluster access and dedicated containers: Dedicated H100 and H200 clusters for training and fine-tuning, plus Dedicated Containers for running Dockerized workloads on managed GPU infrastructure with autoscaling to zero
Use Case Focus
Together AI excels at managed inference for teams that want to call open-source LLMs through an API without managing infrastructure. With the addition of Code Sandbox and Code Interpreter, Together now also supports sandboxed code execution, although its broader platform remains heavily oriented around inference, fine-tuning, GPU clusters, and custom deployments.
Architecture Approach
Together abstracts infrastructure for serverless inference APIs, but it also supports more configurable infrastructure paths such as Dedicated Containers and Code Sandbox. This range lets teams move from fully managed API inference toward more customizable execution when needed.
Best For: Teams building AI agents that need LLM inference through managed APIs, especially those migrating from OpenAI to open-source models, with the option to use Together's sandboxed code-execution products.
6. Baseten
Baseten provides production inference infrastructure with enterprise features including SOC 2 compliance and HIPAA support. The platform uses the open-source Truss packaging system for model deployment and is oriented primarily around deploying ML models as production endpoints rather than arbitrary agent code execution.
Core Capabilities
- Truss model packaging: Open-source tool for packaging and deploying ML models with dependencies
- Enterprise compliance: SOC 2 certification and HIPAA support for regulated industries
- GPU infrastructure: Access to H100 and B200 GPUs for inference workloads
- Autoscaling: Automatic scaling based on traffic patterns with configurable concurrency
Performance Characteristics
Baseten's own cold-start docs explain that cold-start duration depends on factors such as model size, image size, and weight downloads, and that large models can take longer to cold start. The platform uses per-minute billing granularity rather than per-second.
Architecture Approach
Baseten focuses on production model serving rather than general-purpose sandboxed execution. The Truss packaging system provides a standardized way to define model dependencies and serving logic, though it requires more upfront configuration than function-based approaches.
Best For: Enterprise teams deploying production ML models that need compliance certifications and prefer a model-centric deployment abstraction.
7. Replicate
Replicate offers model-centric API hosting with over 100 official models ready for inference through HTTP APIs, plus broader community and custom model workflows. The platform simplifies deployment by abstracting infrastructure and is oriented around model inference rather than arbitrary code execution.
Core Capabilities
- Official and community model library: Over 100 official models available immediately, alongside community and custom or private model workflows
- Cog packaging system: Open-source tool for packaging custom models as Docker containers
- Async workflow support: Built-in support for background jobs and webhook notifications
- Model-type-dependent billing: Public models are billed for active processing time, official models may use input/output pricing, and custom or private model billing can differ
Performance Characteristics
Replicate's own documentation describes cold-start behavior as variable: setup time while models are prepared depends on the model, and cold boots for large models can take longer than for smaller fine-tuned models.
Architecture Approach
Replicate abstracts model deployment to simple HTTP API calls, making it easy to integrate existing models into applications. However, the platform focuses on model inference rather than arbitrary code execution or sandboxed environments.
Best For: Teams needing quick access to pre-deployed models through simple APIs, especially for prototyping AI agents before investing in custom infrastructure.
Why Modal Stands Out for GPU-Enabled AI Agent Sandboxes
Purpose-Built AI Infrastructure
Modal's architecture is specifically engineered for AI workloads rather than adapted from general-purpose cloud infrastructure. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of GPU-accelerated sandboxed execution that AI agents require.
Strong Cold Start Performance
GPU memory snapshots represent one of Modal's most notable technical differentiators. This Alpha capability captures GPU state after model initialization, enabling cold starts that have been observed up to 10x faster than traditional approaches, especially where expensive initialization such as JIT compilation or CUDA setup can be snapshotted. For AI agents that need responsive inference, this capability can make serverless GPU more economically viable for latency-sensitive applications.
Secure Sandboxed Execution at Scale
Modal's sandboxes support 100k+ concurrent sandboxes with gVisor isolation, fast cold starts, and full observability. For AI agents that generate and execute untrusted code autonomously, this combination of security and scale is essential.
Deep GPU Capacity Without Reservations
Modal provides elastic access to GPUs ranging from T4 through B200 without requiring reservations or quotas. The platform's multi-cloud capacity pool ensures availability even during high-demand periods, critical for AI agents that need predictable scaling.
Developer Experience Without Compromise
Modal's code-first SDKs eliminate infrastructure configuration overhead, with SDKs available in Python, TypeScript, and Go for calling Functions, running Sandboxes, and managing resources. Teams define compute requirements, container images, and scaling behavior directly in code. This approach enables rapid iteration compared to YAML-based or container-centric platforms.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via Business Associate Agreements, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise AI agent deployments demand.
Production-Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, demonstrating the platform's ability to handle enterprise-scale AI agent workloads reliably. This production track record reduces operational risk for teams building mission-critical agent systems.
For teams building AI agents that require secure sandboxed execution, GPU acceleration, and production-grade reliability, Modal stands out through gVisor-based isolation, elastic GPU capacity, code-defined infrastructure, and Memory Snapshots that can materially reduce cold starts for suitable workloads.
