Haystack agents can orchestrate tool-using workflows and execute external tools or functions. When those tools run AI-generated code, teams need secure, scalable, isolated sandbox infrastructure to run reliably in production. A code execution sandbox provides the isolated environment where Haystack agents can safely run AI-generated code without risking host systems or sensitive data. Choosing the right sandbox platform determines whether your agents can execute code securely, scale to meet demand, and access GPU acceleration when workloads require it. This guide examines seven code execution sandbox platforms serving different Haystack agent needs in 2026, starting with Modal, a serverless compute platform built for secure execution at massive scale with broad GPU support.
Key Takeaways
- Secure isolation is essential for agentic workflows: Haystack agents can run tools that generate and execute code, making sandboxed execution critical. Modal uses gVisor containers for compute isolation, while E2B employs Firecracker microVMs
- Scale determines production viability: Modal Sandboxes are positioned for production systems, with fast scheduling and support even at 100k+ concurrent sandboxes, essential for Haystack agents handling high-volume workflows
- GPU access differentiates sandbox platforms: Modal offers a broad GPU lineup spanning T4 through B200, enabling Haystack agents to run ML inference and fine-tuning alongside code execution
- Developer experience accelerates iteration: Modal's SDK is code-first, with code-defined infrastructure available in Python, TypeScript, and Go for building apps and Functions, using Sandboxes, calling Functions, and managing resources, while sandboxes themselves can run code in any language the workload requires
- Compliance requirements vary by use case: Modal maintains SOC 2 Type II certification and supports HIPAA-compliant workloads on Enterprise plans via a BAA, meeting enterprise security standards for regulated industries
1. Modal
Modal delivers serverless compute for secure sandboxed execution at scale, the core workload for Haystack agents running AI-generated code. The platform containerizes your code and executes it in the cloud with automatic scaling, all defined through code-first SDKs available in Python, TypeScript, and Go, a model that fits Python-based agent frameworks like Haystack while letting sandboxes run code in whatever language the workload requires.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running untrusted AI-generated code, protecting host systems and other workloads from potentially malicious code
- Massive concurrency: Support for 100k+ concurrent sandboxes with fast cold starts, subject to plan and enterprise limits, enabling Haystack agents to handle high-volume production workflows
- SDKs in Python, TypeScript, and Go: Define Modal resources in code across Python, TypeScript, and Go SDKs for using Sandboxes, calling Functions, and managing resources, while code running inside a sandbox can use any language the workload requires
- Broad GPU access: Ten GPU types from T4 through B200, enabling Haystack agents to call upon GPU acceleration when workloads require ML inference or model fine-tuning
- 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
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 Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies building agent-based products. Per Modal's blog, production users such as Lovable and Quora run millions of untrusted code snippets a day; Modal's Lovable case study separately reports over 1 million sandboxes over 48 hours and 20,000 concurrent sandboxes at peak, demonstrating enterprise-scale reliability for Haystack agent deployments. On the coding-agent side specifically, Ramp built a full-context background coding agent on Modal that generates code changes and writes them back into commits and pull requests, illustrating how Modal Sandboxes support production agents that generate and execute code.
What Makes Modal Unique
- AI-native container runtime: Custom-built infrastructure including file system, container runtime, scheduler, and image builder optimized for AI workloads
- Sandbox snapshotting: Filesystem and directory snapshots can persist filesystem state; Sandbox Memory Snapshots are Alpha and can restore memory and filesystem state. This can reduce cold start latency for initialization-heavy workloads common in Haystack pipelines
- Unified platform: Sandboxes plus inference serving plus model training plus batch processing in one system, reducing vendor complexity for comprehensive Haystack deployments
Best For: Teams building Haystack agents that need secure code execution at scale, with on-demand GPU access for ML workloads, especially those seeking production-grade infrastructure with proven enterprise reliability.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. E2B claims adoption across a large share of the Fortune 100 and serves customers including Hugging Face, Manus, Groq, Lindy, Genspark, StackAI, and Rogo.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code with strong security boundaries
- Multi-language SDKs: Support for Python and TypeScript integration patterns that work with Haystack's orchestration layer
- Template system: Pre-built and custom sandbox environments with versioning for reproducible execution
- Open-source tooling: Community-driven development and self-hosting options for organizations with data sovereignty requirements
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 higher-tier plans and supports cold starts.
Best For: Teams building Haystack agents focused on code execution and testing where CPU-based workloads predominate, particularly those valuing strong microVM isolation and open-source tooling.
3. Northflank
Northflank provides a full-stack platform with sandbox capabilities, offering bring-your-own-cloud (BYOC) deployment options across AWS, GCP, and Azure. The platform processes over 2 million workloads monthly for 2,000+ startups and enterprises.
Core Capabilities
- Multiple isolation options: Choose between Kata Containers, Firecracker, or gVisor based on workload security requirements
- BYOC deployment: Deploy to your own AWS, GCP, Azure, or on-premises infrastructure with full VPC control
- GPU support: Access to L4 through B200 GPUs for ML workloads alongside sandbox execution
- Any OCI image: Standard container image support without vendor-specific SDK requirements
Architecture Approach
Northflank positions itself as a complete platform encompassing sandboxes, databases, APIs, workers, and GPU compute. This breadth suits teams that want unified infrastructure management beyond sandbox-only solutions.
Best For: Teams building Haystack agents that require BYOC deployment for compliance or data sovereignty reasons, or those seeking a comprehensive platform covering multiple infrastructure needs.
4. Daytona
Daytona focuses on sandbox creation and supports cold starts. Daytona's public repository attracted significant community attention after launch, though as of June 2026 Daytona says core development has moved to a private codebase.
Core Capabilities
- Isolated sandbox runtime: Daytona provides OCI/Docker-compatible sandboxes with a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk
- Unlimited session duration: Sandboxes can run indefinitely without forced time constraints
- GPU support: H100 and RTX GPU options for ML workloads
- IDE integration: Native support for VS Code, Cursor, and JetBrains environments
Architecture Approach
Daytona emphasizes persistent workspaces that maintain state across sessions. Sandboxes auto-stop after 15 minutes of inactivity by default but can be configured for indefinite runtime, benefiting Haystack agents that need to preserve context and cached dependencies.
Best For: Teams building Haystack agents that prefer workspace continuity over purely ephemeral execution.
5. Blaxel
Blaxel is built specifically for AI agents, focusing on persistent "agent computers" that stay on standby and resume when needed. The platform emphasizes resuming from perpetual standby.
Core Capabilities
- Perpetual sandboxes: Sandboxes remain on automatic standby rather than being destroyed after each task, keeping environments ready for recurring workflows
- Zero idle compute cost: Sandboxes can remain in standby without active compute charges, though snapshot storage and attached volume charges can apply
- MicroVM isolation: Secure execution environment for running LLM-generated code
- Persistent storage: Volumes that survive sandbox destruction and recreation for maintaining state across sessions
Architecture Approach
Blaxel recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time. This approach benefits Haystack agents that need continuity across workflows instead of clean-room execution on every task.
Best For: Teams building Haystack agents with bursty, intermittent workloads where zero idle compute cost and resume from standby optimize operational efficiency.
6. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built on Firecracker microVMs. The platform is designed for AI agents, testing workflows, and development scenarios requiring secure execution of untrusted code.
Core Capabilities
- Firecracker-powered isolation: Each environment runs in an on-demand Linux microVM with its own filesystem, network, and process space
- Flexible runtime model: Sandboxes can start when needed and stop after use, though Vercel says persistence is the default and supports persistent sandboxes
- State persistence options: Automatic filesystem state saving when sandboxes stop, with restoration on resume
- Developer-friendly Linux access: Full Linux environment with sudo, package managers, and standard command-line workflows
Use Case Focus
Vercel Sandbox functions as an execution layer for secure, isolated code running. Its strengths align with agent or developer workflows involving repeated start-run-stop cycles and short-lived tasks.
Best For: Teams building Haystack agents within the Vercel ecosystem, or those prioritizing secure ephemeral execution for short-lived code execution tasks.
7. Cloudflare Sandbox
Cloudflare Sandbox exposes a code execution environment through a TypeScript-first SDK, supporting Python and Node.js workloads with isolated Linux containers.
Core Capabilities
- Python and Node.js execution: Support for running Python scripts, Node.js applications, and data-processing workloads
- TypeScript-first SDK: API for sandbox lifecycle management, command execution, file operations, and WebSocket connections
- Isolated Linux containers: Dedicated filesystem and container per sandbox with state maintained while active
- Configurable keep-alive: Cloudflare Sandbox can keep containers alive across requests, but state exists only while the container remains active; applications should handle container restarts or use backup/external storage for durable persistence
Use Case Focus
Cloudflare Sandbox is oriented toward secure code execution and programmable sandbox workflows. Cloudflare's documentation includes tutorials for AI code executors and coding agents built with agent SDKs.
Best For: Teams building Haystack agents in a Cloudflare-native environment, particularly those preferring a TypeScript-first development model for sandbox orchestration.
Why Modal Stands Out for Haystack Agent Infrastructure
Purpose-Built for AI Agent Workloads
Modal's architecture is engineered for agentic and machine learning workloads. The platform's custom container runtime, scheduler, and file system on the core platform are optimized for fast cold starts, sandboxed code execution, GPU-accelerated computation, and dynamic scaling for AI, ML, and agent workloads.
Secure Sandboxed Execution at Scale
When Haystack agents run tools that generate and execute code, isolation becomes critical. Modal's sandboxes are secure containers for untrusted user or agent code, and Modal's product page describes fast scheduling even at 100k+ concurrent sandboxes with gVisor isolation and full observability, essential for agents handling untrusted code at production scale.
On-Demand GPU Access
Beyond CPU-based code execution, Haystack agents often need ML inference or model fine-tuning capabilities. Modal provides a broad GPU lineup, including T4, L4, A10, L40S, A100 40GB/80GB, RTX PRO 6000, H100, H200, and B200/B200+, letting agents match compute to the task at hand.
Developer Experience Without Compromise
Modal is code-first: code-defined infrastructure is available across Python, TypeScript, and Go SDKs for building Modal apps and Functions, using Sandboxes, calling Functions, and managing resources, while sandboxes can run code in any language the workload requires. This approach fits Python-based agent frameworks like Haystack.
Enterprise Security and Compliance
With SOC 2 Type II certification, HIPAA support via BAA, and comprehensive security practices including gVisor sandboxing and TLS 1.3, Modal meets the compliance requirements that enterprise Haystack agent deployments demand.
For teams building Haystack agents that require secure code execution, production-grade reliability, and on-demand GPU access, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise track record makes it the clear choice.
Explore the Modal documentation to get started with sandboxes for Haystack agents.
View Modal Docs