Agno (formerly Phidata) has emerged as a lightweight, composable AI agent framework that supports multimodal processing and high-throughput agent teams. Building production-grade Agno agents requires infrastructure that can securely execute AI-generated code, scale to thousands of concurrent sessions, and provide GPU acceleration when multimodal workloads demand it. Choosing the right secure sandboxed execution platform determines whether your Agno agents can run reliably at scale without compromising security or developer velocity. This guide examines seven code execution sandbox platforms serving different Agno development needs in 2026, starting with Modal, a serverless compute platform that combines secure sandboxes with GPU access and a unified AI infrastructure stack.
Key Takeaways
- Secure isolation is non-negotiable for AI-generated code: Agno agents can be configured to execute generated code through toolkits such as PythonTools, CodingTools, E2B, Daytona, or provider-native code execution, making sandboxed execution critical for deployments that enable those capabilities. Modal uses gVisor containers while E2B employs Firecracker microVMs for secure isolation
- GPU support differentiates sandbox platforms: Modal offers broad on-demand NVIDIA GPU access from T4 through B200, valuable for Agno's multimodal agents processing images, video, and audio
- Massive concurrency enables agent swarms: Modal supports 100k+ concurrent sandboxes, enabling the high-throughput agent teams that Agno is designed to orchestrate
- Code-first SDKs accelerate development cycles: Modal is code-first and avoids YAML, offering code-defined infrastructure through SDKs in Python, TypeScript/JavaScript, and Go (with the TypeScript/JavaScript and Go SDKs in beta) for Sandboxes, Function calls, and resource management, while Sandboxes can run code in any programming language, enabling faster iteration on agent development
- Production-proven platforms reduce operational risk: Modal powers cloud infrastructure for over 10,000 teams including Lovable, Quora, and Ramp, demonstrating enterprise-scale reliability for agent infrastructure
1. Modal
Modal delivers serverless compute for secure code execution at scale, the core sandbox workload for Agno agents, with on-demand GPU access layered on top for multimodal workloads. The platform takes your code, puts it in a container, and executes it in the cloud with automatic scaling. Modal is code-first and supports code-defined infrastructure through SDKs in Python, TypeScript/JavaScript, and Go (with the TypeScript/JavaScript and Go SDKs in beta) for Sandboxes, Function calls, and resource management, and Sandboxes can run code in any programming language.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running AI-generated code, with containers isolated using gVisor for production-grade security
- Dynamic environment definition: Modal Sandboxes provide a direct interface for defining containers at runtime, including custom images and on-the-fly image builds
- Massive concurrency: Support for 100k+ concurrent sandboxes enables high-throughput agent swarms that Agno is designed to orchestrate
- 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 that reduce startup latency for initialization-heavy workloads
- Comprehensive GPU access: On-demand access to NVIDIA GPUs including T4, L4, A10, L40S, A100 variants, H100, H200, and B200, enabling everything from lightweight inference to large-scale model training
Security and Compliance
Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses gVisor-based sandboxing for compute isolation, TLS 1.3 for public APIs, and encryption for data in transit and at rest. Additional security features include:
Production-Proven Results
Modal powers production workloads for notable AI companies building agent systems:
- Ramp uses Modal Sandboxes to power background coding agents that generate code changes and write them back as commits or pull requests
- Lovable used Modal to run over 1 million sandboxes during a 48-hour promotional weekend, powering an estimated 250,000 app creations and up to 20,000 concurrent sandboxes at peak
- Quora leverages Modal's infrastructure for production-scale agent workloads
What Makes Modal Unique
- Unified AI platform: Sandboxes, inference, training, and batch processing in one coherent system reduces multi-vendor complexity
- AI-native container runtime: Custom-built infrastructure including file system, container runtime, scheduler, and image builder optimized for AI workloads
- Dynamic scaling: Instant autoscaling to thousands of containers without capacity planning or reservations
Best For: Teams building Agno agents that need secure code execution at scale, with on-demand GPU access for multimodal processing, ML inference, or fine-tuning, especially those seeking a unified platform that eliminates vendor sprawl.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. As of E2B's current homepage, the company claims usage by 94% of Fortune 100 companies (earlier 2025 materials cited 88%), and E2B's own materials name customers and users including Hugging Face, Perplexity, Groq, Manus, Lindy, Genspark, and LMArena.
Core Capabilities
- Firecracker microVMs: Hardware-level kernel isolation for running untrusted AI-generated code with strong security boundaries
- Cold start support: Supports cold starts for agent execution cycles
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- SDK and execution languages: E2B provides JavaScript/TypeScript and Python SDKs, while its Code Interpreter supports executing Python, JavaScript/TypeScript, R, Java, and Bash inside sandboxes
- 24-hour session support: Extended runtime for longer agent workflows on Pro tier
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. The platform supports up to 100 concurrent sandboxes on Pro tier, with higher limits available for enterprise deployments.
Architecture Approach
E2B's Firecracker-based isolation provides hardware-level security boundaries, making it well-suited for scenarios where maximum isolation is the primary concern. The template system enables reproducible sandbox environments with versioning for consistent agent execution.
Best For: Teams building Agno agents focused on secure code execution where Firecracker microVM isolation is preferred, particularly those with open-source requirements or data sovereignty needs.
3. Northflank
Northflank provides a comprehensive cloud infrastructure platform with sandbox capabilities, supporting Bring Your Own Cloud (BYOC) deployment for teams requiring VPC control. Northflank says it handles more than 2 million isolated workloads monthly and reports usage across startups, public companies, and government deployments.
Core Capabilities
- Multiple isolation options: Support for Kata, Firecracker, and gVisor isolation depending on workload requirements
- BYOC deployment: Self-serve deployment to AWS, GCP, Azure, Oracle, CoreWeave, or bare-metal infrastructure
- Unlimited session duration: No time limits on sandbox runtime for long-running agent workflows
- GPU support: Northflank publishes GPU support for L4, A100, H100, H200, and a broader GPU instance inventory that includes B200 and GB-series offerings
- Full platform scope: Databases, APIs, and workers alongside sandboxes in one platform
Use Case Focus
Northflank describes microVM boot for its sandbox product. Northflank states that it is SOC 2 Type II certified.
Architecture Approach
Northflank positions itself as a full infrastructure platform rather than a sandbox-only solution. This approach benefits teams that need databases, APIs, and additional infrastructure components alongside their sandbox environments.
Best For: Teams building Agno agents that require BYOC deployment in their own VPC, unlimited session duration for multi-day workflows, or a comprehensive infrastructure platform beyond just sandboxes.
4. Daytona
Daytona provides persistent development environments with sandbox creation times. The platform has around 72.4k to 72.5k GitHub stars as of June 2026 and offers both GPU support and configurable runtime persistence.
Core Capabilities
- Cold start support: Supports cold starts for agent execution
- Configurable runtime persistence: Sandboxes can run indefinitely without time limits
- GPU support: Available for ML workloads alongside persistent storage
- SOC 2 Type I and HIPAA: Daytona states that SOC 2 Type I and HIPAA certifications are in place
- Docker/OCI compatibility: Standard container image support for flexible environment configuration
Use Case Focus
Daytona's architecture centers on persistent workspaces that maintain state across sessions. This benefits Agno agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead.
Architecture Approach
Daytona describes its sandboxes as isolated environments with dedicated kernel, filesystem, network, vCPU, RAM, and disk, with Docker/OCI-compatible snapshots. The platform pivoted to focus on AI agent sandboxes in 2025, positioning itself around sandbox startup and unlimited runtime.
Best For: Teams building Agno agents that prioritize sandbox creation, persistent development environments, and workspace continuity over ephemeral execution.
5. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, with a focus on persistent "agent computers" that stay on standby and resume when needed. The platform positions itself around secure sandboxed compute runtimes for agents that need to run commands, manage files, and preserve execution state across sessions.
Core Capabilities
- Perpetual sandboxes: Sandboxes that remain on automatic standby rather than being torn down after each task
- microVM isolation: Secure execution environment for AI-generated code
- Template support: Reusable sandbox templates for standardized environments, including code generation agents and Git PR review agents
- Persistent storage options: Volumes for storage that survives sandbox destruction and recreation
- SOC 2 and HIPAA compliance: Enterprise security certifications for regulated workloads
Use Case Focus
Blaxel emphasizes persistent state rather than purely ephemeral execution. The platform's documentation recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time.
Architecture Approach
Blaxel's perpetual sandbox model benefits Agno agents that need continuity across workflows instead of clean-room execution on every task. The REST API and MCP server provide file system and process access for agent interactions.
Best For: Teams building Agno agents that need persistent sandbox environments, resume from standby, and secure code execution with continuity across sessions.
6. Vercel Sandbox
Vercel Sandbox is an isolated code execution environment built for running untrusted code in temporary Linux microVMs. Vercel positions it for AI agents, code execution, testing, and development workflows where teams need secure environments without managing underlying infrastructure.
Core Capabilities
- Firecracker microVMs: Each environment runs in an on-demand Linux microVM with its own filesystem, network, and process space
- Persistence model: Vercel Sandbox sessions are time-limited, but persistent sandboxes are the default, with filesystem state automatically saved when stopped and restored when resumed; non-persistent sandboxes discard state
- Developer-friendly Linux access: Full Linux environment with sudo, package managers, and command-line workflows
- Snapshot persistence: Vercel persistent sandboxes save filesystem state through snapshots; snapshots expire after 30 days by default, but expiration can be customized or disabled
Use Case Focus
Vercel Sandbox sessions range from 45 minutes to 5 hours depending on configuration, making it suitable for medium-duration agent tasks. The platform integrates naturally with the broader Vercel ecosystem for teams already using Vercel for deployment.
Architecture Approach
Vercel Sandbox functions as an execution layer for secure, isolated code running rather than a full infrastructure platform for GPU-heavy AI workloads. Its strength lies in secure ephemeral execution with straightforward integration.
Best For: Teams building Agno agents within the Vercel ecosystem that need isolated environments for code execution and testing, especially when the priority is secure ephemeral execution rather than GPU access.
7. Cloudflare Sandboxes
Cloudflare Sandboxes is a code execution environment exposed through the Sandbox SDK, positioned for running Python and Node.js workloads. The platform leverages Cloudflare's edge network for code execution, command management, file operations, and agent-style workflows.
Core Capabilities
- Python and Node.js execution: Support for Python scripts, Node.js applications, code compilation, and data-processing workloads
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, file operations, terminal access, and WebSocket connections
- Isolation model: Cloudflare Sandboxes are built on Cloudflare Containers and provide isolated Linux environments; Cloudflare's architecture documentation also describes VM-based isolation for each sandbox
- Geographic distribution: Cloudflare Sandboxes run on Cloudflare's platform and are designed for Workers and Containers-based execution with geographic distribution
- Configurable lifecycle: Support for keepAlive and configurable sleep behavior; containers restart after inactivity and lose in-container state, so durable state requires external storage
Use Case Focus
Cloudflare Sandboxes integrates naturally with the Cloudflare Workers ecosystem. The platform's tutorials include AI code executors and coding agents, positioning it for teams already invested in Cloudflare's infrastructure.
Architecture Approach
Cloudflare uses a TypeScript-first development model and builds Sandboxes on Cloudflare Containers; its architecture documentation describes VM-based isolation for each sandbox. The platform is designed for geographic distribution across Cloudflare's network, which benefits workloads that gain from distributed execution.
Best For: Teams building Agno agents within the Cloudflare ecosystem looking for isolated code execution, file handling, and agent-oriented workflows, particularly those who prefer a TypeScript-first development model.
Why Modal Stands Out for Agno Agent Development
Purpose-Built for Multimodal Agent Workloads
Agno's support for multimodal processing, handling text, images, audio, and video, requires infrastructure that can provide GPU acceleration when needed. Modal offers broad on-demand NVIDIA GPU access spanning T4 through B200, enabling Agno agents to process multimodal inputs without requiring a separate inference provider.
Massive Scale for Agent Swarms
Agno is designed for "agent teams" and high-throughput orchestration. Modal's Sandboxes page states sub-second scheduling even at 100k+ concurrent sandboxes, providing the scale that production Agno deployments demand. This concurrency level supports large-scale agent and RL workloads, eliminating bottlenecks when orchestrating large agent swarms.
Dynamic Environment Definition
Modal Sandboxes provide a direct interface for defining containers at runtime, including custom images and on-the-fly image builds, enabling Agno agents to programmatically configure their execution environments. This capability is critical for agentic systems where the agent needs to adapt its execution environment based on task requirements.
Unified AI Infrastructure
Modal reduces the multi-vendor integration work that plagues many AI deployments by combining sandboxes, inference, training, and batch processing in one coherent system. For Agno developers, this means:
- Single platform for all infrastructure needs
- Unified observability across sandbox execution and model inference
- Reduced integration work between components
- Consistent security posture across the entire stack
Code-First Developer Experience
Modal is code-first and avoids YAML configuration files. Modal provides code-defined infrastructure through SDKs in Python, TypeScript/JavaScript, and Go (with the TypeScript/JavaScript and Go SDKs in beta) for Sandboxes, Function calls, and resource management, and Sandboxes can run code in any programming language. This approach accelerates Agno development cycles by enabling faster iteration, version control for infrastructure, and reduced configuration drift.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via BAA for Enterprise customers, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise Agno deployments demand. The platform's security documentation details application, corporate, and infrastructure security practices.
Production-Proven at Scale
Modal powers infrastructure for over 10,000 teams including Lovable, Quora, Ramp, and Suno. This production track record, including Lovable running over 1 million sandboxes during a 48-hour promotional weekend with up to 20,000 concurrent sandboxes at peak, demonstrates the platform's ability to handle enterprise-scale Agno agent workloads reliably.
For teams building Agno agents that require secure code execution, GPU acceleration for multimodal processing, and production-grade reliability, Modal's combination of AI-native infrastructure, massive concurrency, and unified platform makes it the clear choice.
Explore the Modal documentation to get started with Agno agent development.
View Modal Docs