Continue and other AI coding assistants are transforming software development, generating code autonomously and iterating at speeds that manual workflows cannot match. But running AI-generated code safely requires robust sandbox infrastructure that isolates execution, scales on demand, and integrates seamlessly with agent workflows. The right code execution sandbox determines whether your AI assistant can execute untrusted code securely, handle concurrent sessions without bottlenecks, and access GPU acceleration when workloads demand it. This guide examines seven sandbox platforms serving different Continue integration needs in 2026, starting with Modal, a serverless compute platform built for secure code execution at massive scale.
Key Takeaways
- Secure isolation is non-negotiable for AI-generated code: Continue and similar AI assistants generate code autonomously, making sandboxed execution critical. Modal uses gVisor containers for compute isolation, while E2B employs Firecracker microVMs
- Cold start performance directly impacts user experience: Fast startup times keep AI coding workflows responsive. Modal delivers fast cold starts enabled by memory snapshotting and an optimized filesystem, while Daytona supports sandbox cold starts and Blaxel supports resume from standby
- GPU access enables advanced AI workloads: Modal supports extensive GPU options from T4 through B200, allowing Continue to run ML models for code analysis, generation, and understanding alongside standard execution
- Production-proven platforms reduce operational risk: Modal powers over 10,000 teams including Ramp, Lovable, and Quora Poe, demonstrating enterprise-scale reliability for sandbox infrastructure
- Session duration and persistence vary significantly across platforms: Continuous runtime limits, idle/standby behavior, and persistent state restoration differ by platform. E2B supports up to 24-hour continuous runtime on Pro plans; Modal Sandboxes support configurable lifetimes up to 24 hours, with Filesystem Snapshots recommended for longer workflows; Northflank imposes no forced termination for long-running tasks; platforms like Blaxel and Fly.io Sprites emphasize persistent state across idle and restore cycles rather than unlimited continuous runtime
1. Modal
Modal delivers serverless compute for secure sandboxed execution at scale, the core requirement for running AI-generated code from Continue and similar assistants. The platform containerizes your code and executes it in the cloud with automatic scaling, all defined through code-first SDKs in Python, TypeScript, and Go. Code running inside a Modal Sandbox is not limited to any one programming language; the sandbox can run whatever runtime or language the workload requires.
Core Capabilities
- gVisor container isolation: Secure sandboxed execution for running untrusted AI-generated code, protecting your systems from potentially harmful operations
- Massive concurrency: Scale to 100,000+ concurrent sandboxes for production workloads; account-level concurrency scales from Starter through Enterprise plans
- 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
- Code-first SDKs: Define compute, storage, and networking in code without YAML configuration files, with Python plus TypeScript and Go SDKs for using Sandboxes, invoking Functions, and managing resources
- On-demand GPU access: Extensive GPU support including T4, L4, A10, L40S, A100 (40GB and 80GB), RTX PRO 6000, H100, H200, and B200 for workloads requiring acceleration
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 workloads for notable AI companies:
- Ramp uses Modal Sandboxes to power Inspect, an internal background coding agent that runs full development environments and is responsible for a large share of merged pull requests at Ramp
- Lovable ran 1M+ sandboxes in 48 hours, peaking at 20K concurrent sessions
- Quora Poe relies on Modal Sandboxes to securely execute LLM-generated code in Poe
What Makes Modal Unique
- AI-native container runtime: Custom-built infrastructure including file system, container runtime, scheduler, and image builder optimized for AI workloads
- Multi-cloud capacity pool: Deep CPU and GPU capacity across major cloud providers ensures availability without reservations
- Full observability: Production dashboards, logging, and per-sandbox monitoring for debugging agent behavior
- Primitives for coordination: Built-in Queues, Dicts, and Volumes for managing state across sandbox sessions
Best For: Teams integrating Continue or building coding agents that need secure code execution at scale, with on-demand GPU access for ML inference and code analysis workloads.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform reports 3.5M+ monthly downloads and adoption across a large share of Fortune 100 companies.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation for running untrusted AI-generated code with strong security boundaries
- Cold starts: E2B supports same-region sandbox cold starts
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- Multi-language SDKs: Native support for Python and TypeScript with integrations for LangChain and major model providers such as OpenAI and Anthropic
- Template system: Reproducible sandbox environments with versioning for consistent execution
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 24-hour continuous runtime on Pro plans, with pause/resume and full state preservation also available.
Production Evidence
- E2B's own customer materials state that Perplexity shipped advanced data analysis features in one week using E2B
- Hugging Face used E2B for DeepSeek-R1 replication workflows
- GenSpark's CTO noted that E2B lets them scale to thousands of concurrent sessions
Best For: Teams building Continue integrations focused on code execution and testing where GPU acceleration is not required, particularly those needing SDK integration with existing AI frameworks.
3. Northflank
Northflank provides a full-stack cloud platform with flexible sandbox capabilities. The company reports 2M+ isolated workloads monthly and a large developer user base.
Core Capabilities
- Flexible isolation: Northflank documents microVM-backed and user-space-kernel sandbox isolation, including gVisor in some sandbox contexts, for strong security boundaries
- Unlimited session duration: No forced termination, allowing long-running agent workflows to complete without interruption
- Bring Your Own Cloud (BYOC): Self-serve deployment across supported cloud providers and imported Kubernetes clusters, including AWS, GCP, Azure, and Oracle
- GPU support: GPU-enabled workloads and sandboxes are supported; specific GPU model availability varies by provider and region
- Any OCI container image: Run standard container images without modification
Architecture Approach
Northflank documents microVM-backed and user-space-kernel isolation approaches for sandbox execution, with gVisor used in some sandbox and GPU contexts. This infrastructure expertise enables strong isolation options matched to specific workload requirements.
Production Evidence
- Northflank's materials reference customers including Writer, Sentry, and cto.new
- SOC 2 Type 2 compliance and production workloads since 2021 are referenced in Northflank's materials
Best For: Teams requiring BYOC deployment flexibility or unlimited session duration for complex Continue workflows.
4. Daytona
Daytona provides persistent development environments with sandbox creation capabilities. The platform raised a $24M Series A in February 2026 and has repositioned around AI agent infrastructure.
Core Capabilities
- Cold starts: Daytona supports sandbox cold starts
- Docker/OCI-based sandboxes: Sandbox snapshots are built from Docker/OCI images for reproducible isolated execution
- GPU support: Available for ML workloads; GPU sandboxes run as ephemeral environments
- Open-source core: Self-hosting available with enterprise features for larger teams
- Broad SDK support: Python, TypeScript, Ruby, Go, and Java SDKs are available for integration; direct runtime execution examples are documented for Python, TypeScript, and JavaScript
- Native development features: Built-in Git, LSP support, and Docker-in-Docker capabilities
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead.
Production Evidence
- Daytona's Trust Center references SOC 2 Type I and HIPAA certifications; Daytona's security exhibit lists SOC 2 Type II as in progress
Best For: Teams building Continue integrations that require persistent development environments with cold start support and workspace continuity.
5. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, emphasizing persistent "agent computers" with resume from standby. The platform focuses on perpetual standby capabilities with zero compute cost during inactivity.
Core Capabilities
- Resume from standby: Blaxel supports resume from standby with full filesystem and memory state intact
- Perpetual standby: Sandboxes remain available at zero compute cost during inactivity with no forced termination
- MicroVM isolation: AWS Lambda-inspired architecture for secure execution boundaries
- Auto-suspend efficiency: Sandboxes automatically enter standby after a period of inactivity, reducing costs
- Co-located agent hosting: Eliminates network roundtrip latency between agent and sandbox
Architecture Approach
Blaxel emphasizes persistent state rather than purely ephemeral execution. Sandboxes retain shell history, installed dependencies, and context over time, benefiting agents that need continuity across workflows.
Production Evidence
- Blaxel claims SOC 2 Type II, ISO 27001, and HIPAA compliance; the platform emphasizes auto-suspend and standby mechanics to reduce idle compute costs
Best For: Teams building Continue integrations with intermittent workloads that benefit from resume and persistent state preservation across sessions.
6. Fly.io Sprites
Fly.io Sprites is a persistent microVM platform that launched publicly in January 2026, offering large persistent storage capacity for sandbox workloads.
Core Capabilities
- Large persistent storage: Each Sprite provides a persistent ext4 filesystem; during execution, data is written to a sparse 100GB NVMe volume used as a cache, while durable state is backed by object storage
- Firecracker microVMs: Hardware-level isolation with strong security boundaries
- Checkpoint/restore: Stateful rollback capability for experimentation and debugging; checkpoint creation time depends on data size
- Persistent execution model: Filesystem and state survive idle periods and restoration cycles, supporting long-running agent tasks
- Idle billing model: No compute charges when sandbox is not actively running
Architecture Approach
Fly.io Sprites positions itself as the closest thing to giving your agent a persistent development machine. The platform excels at long-running sessions with large storage requirements and state that persists across idle and restore cycles.
Use Case Focus
Sprites is designed for multi-day projects where agents need persistent state, large file handling, and the ability to checkpoint and restore execution for debugging or rollback.
Best For: Teams building Continue integrations that work on multi-day projects with large storage needs and checkpoint/restore workflows.
7. Vercel Sandbox
Vercel Sandbox provides isolated code execution environments built for running untrusted code in temporary Linux microVMs. Vercel Sandbox is generally available as of 2026; persistent sandboxes remain in beta.
Core Capabilities
- Firecracker microVMs: Isolated Linux environment with dedicated filesystem, network, and process space
- Cold starts: Vercel Sandbox supports startup for its temporary microVM environments
- Ephemeral runtime model: Temporary sandboxes designed for start-run-stop cycles
- Developer-friendly Linux access: Sudo, package managers, and standard command-line workflows
- State persistence options (beta): Persistent sandboxes with automatic filesystem state preservation across sessions are currently in beta
Architecture Approach
Vercel Sandbox is an execution layer for secure, isolated code running rather than a full infrastructure platform. Session limits range from 45 minutes to 5 hours depending on plan configuration.
Use Case Focus
The platform's fit is strongest for agent or developer workflows involving repeated start-run-stop cycles, short-lived tasks, or safe execution of generated code within the Vercel ecosystem.
Best For: Teams already using Vercel wanting sandboxed Continue execution for demos, prototypes, and short-lived coding tasks.
Why Modal Stands Out for Continue Sandbox Infrastructure
Purpose-Built for AI Agent Workloads
Modal's architecture is specifically engineered for agentic and machine learning workloads. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of secure code execution, fast cold starts, and dynamic scaling that Continue and similar AI coding assistants require.
Secure Sandboxed Execution at Scale
Most AI coding assistant sandbox work is CPU-based execution of generated code, and Modal's sandboxes are built to handle that workload at massive scale. The platform supports 100,000+ concurrent sandboxes for production workloads, with fast cold starts, gVisor isolation, and full observability essential for coding assistants that generate and execute untrusted code continuously.
On-Demand GPU Access
Beyond CPU execution, Continue integrations can call upon GPUs on demand when workloads require acceleration. Modal supports a broad GPU lineup including T4, L4, A10, L40S, A100 (40GB and 80GB), RTX PRO 6000, H100, H200, and B200, letting agents match compute to the task at hand, whether running lightweight code analysis models or large language models for code generation.
Developer Experience Without Compromise
Modal's code-first SDKs eliminate infrastructure configuration overhead, letting teams define compute requirements, container images, and scaling behavior directly in code without YAML files. Modal supports code-defined infrastructure in Python, TypeScript, and Go. The TypeScript and Go SDKs cover using Sandboxes, invoking Functions, and managing resources. Code running inside a Modal Sandbox is not limited to any one language; the sandbox supports whatever runtime or language the workload requires, enabling seamless Continue integration across polyglot projects.
Production-Proven Reliability
Modal powers cloud infrastructure for over 10,000 teams, including AI companies like Ramp, Lovable, and Quora Poe. Lovable ran 1M+ sandboxes in 48 hours, peaking at 20K concurrent sessions, demonstrating the platform's ability to handle enterprise-scale coding assistant workloads reliably.
Enterprise Security and Compliance
With SOC 2 Type II certification and HIPAA support via BAA on Enterprise plans, Modal meets the compliance requirements that enterprise Continue deployments demand. The platform uses comprehensive security practices including gVisor sandboxing and TLS 1.3.
For teams building Continue integrations that require secure code execution, production-grade reliability, and on-demand CPU and GPU access, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise reliability makes it the clear choice.
