SWE-bench has become the standard benchmark for evaluating coding agents, measuring their ability to resolve real GitHub issues autonomously. But running these agents at scale requires more than raw compute. It demands secure sandboxed environments that can execute AI-generated code safely, scale to thousands of concurrent sessions, and provide the observability needed to debug agent behavior. Choosing the right sandbox platform determines whether your coding agents can iterate rapidly on benchmark improvements or get stuck in infrastructure bottlenecks.
This guide examines seven sandbox platforms serving different coding agent needs in 2026, starting with Modal, which has upstreamed support into SWE-bench and runs the 500-task Verified benchmark in just 7 minutes.
Key Takeaways
- Official SWE-bench integration accelerates agent development: Modal has upstreamed support into SWE-bench, enabling benchmark runs with a simple
--modal flag that completes 500 tasks in 7 minutes - Security isolation is non-negotiable for untrusted code: Coding agents generate and execute code autonomously. Modal uses gVisor containers, while E2B employs Firecracker microVMs for hardware-level isolation
- Production scale separates prototypes from deployments: Modal powers infrastructure for over 10,000 teams, including companies like Ramp and Lovable running large-scale code executions
- GPU access extends agent capabilities beyond code execution: Modal combines sandboxed execution with on-demand GPU access for workloads requiring ML inference or model fine-tuning
- Session persistence matters for long-running evaluations: Platforms differ significantly on session limits. E2B Pro supports 24 hours of continuous runtime with indefinite paused-state retention; Blaxel supports standby with tier-dependent persistence and storage-related charges. These differences impact multi-day benchmark runs
1. Modal
Modal delivers serverless compute for secure code execution at scale, with official SWE-bench integration that makes it the purpose-built choice for coding agent development. The platform's custom infrastructure handles containerization, execution, and automatic scaling. Modal's code-first SDK supports Python, TypeScript, and Go for working with Functions, Sandboxes, and other Modal resources. Code running inside a sandbox is not limited to any single language; the sandbox can run whatever runtime or language the workload requires.
Core Capabilities
- Official SWE-bench integration: Modal has upstreamed support into the SWE-bench framework, enabling the 500-task Verified benchmark to run in 7 minutes with a simple
--modal flag - gVisor container isolation: Secure sandboxed execution for running AI-generated code, the primary workload for coding-agent development
- 50,000+ concurrent sessions: Massive scale with fast cold starts, essential for parallel benchmark execution
- On-demand GPU access: Agents can call upon GPUs when needed for workloads requiring ML inference, including B200, H200, H100, RTX PRO 6000, A100 80 GB, A100 40 GB, L40S, A10, L4, and T4
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. See Modal's security docs for full details.
Production-Proven Results
Modal powers production workloads for AI companies building coding agents:
- Ramp uses Modal to power Ramp Inspect, its internal background coding agent, with full development environments running in Modal Sandboxes and scaling to hundreds of concurrent sessions
- Lovable used Modal Sandboxes at massive scale to run LLM-generated code for app-generation sessions in safe, isolated sandboxes, running over 1 million sandboxes in 48 hours and reaching 20,000 concurrent sandboxes at peak
- Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe; the team stress-tested Sandbox creation throughput to 1,000 sandboxes per second
What Makes Modal Stand Out for SWE-Bench
- Upstreamed SWE-bench support: Run benchmarks directly with official integration, adding a
--modal flag to run evaluations in the cloud and complete the 500-task Verified benchmark in 7 minutes - 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
- AI-native container runtime: Custom-built infrastructure including file system, container runtime, scheduler, and image builder optimized for AI workloads
- Memory snapshotting (alpha): Technology that snapshots CPU or GPU memory state to reduce cold start latency for initialization-heavy workloads; subject to documented constraints
- Unified platform: Combines sandboxes with inference, training, and batch processing in one stack
Best For: Teams building SWE-bench-style coding agents that need official benchmark integration, production-scale execution, and on-demand GPU access when ML workloads require acceleration.
2. Northflank
Northflank provides a full production application platform with flexible isolation options and self-serve bring-your-own-cloud (BYOC) deployment. The platform self-reports handling over 2 million microVMs monthly and offers unlimited session lengths without the continuous-runtime caps found on other platforms.
Core Capabilities
- Triple isolation choice: Kata Containers, Firecracker, or gVisor, selectable per workload
- Self-serve BYOC: Deploy into your own AWS, GCP, Azure, or bare-metal infrastructure without sales involvement
- Cold start support: Northflank supports cold starts for its microVM-based sandboxes
- GPU workloads supported: L4, A100, H100, and H200 available on the same platform as sandboxes
Security and Compliance
Northflank maintains SOC 2 Type 2 certification and provides comprehensive compliance controls for regulated industries. The BYOC model enables data sovereignty requirements without sacrificing platform capabilities.
Architecture Approach
Northflank positions itself as a complete infrastructure stack: sandboxes plus databases, APIs, CI/CD, and observability in one control plane. This breadth benefits teams that want unified infrastructure management beyond isolated code execution.
Best For: Enterprise teams requiring BYOC deployment, flexible isolation options, or unlimited session lengths for extended benchmark runs.
3. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform states it is used by 94% of Fortune 100 companies for agentic workflows, with users including Perplexity, Hugging Face, and Groq.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation with dedicated kernel per sandbox for running untrusted AI-generated code
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- Multi-language SDKs: Python and TypeScript support for flexible integration patterns
- 24-hour continuous runtime: Pro tier supports up to 24 hours of continuous runtime; longer stateful workflows can use pause/resume, with state preserved indefinitely
Use Case Focus
E2B excels at secure code execution, spinning up isolated environments for agents to run generated code. The platform's SDK-first approach makes integration straightforward for teams building custom agent frameworks.
Architecture Approach
E2B supports ephemeral sandbox execution as well as pause/resume workflows with persistent sandbox state. Each sandbox gets a dedicated kernel through Firecracker, providing hardware-enforced isolation between workloads.
Best For: Teams building coding agents that need secure isolated code execution, whether ephemeral or stateful via pause/resume, where GPU acceleration is not required, particularly those valuing open-source flexibility.
4. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, emphasizing standby persistence and resume capabilities. The platform supports resume from standby, reducing the overhead of returning to an active state compared to full cold starts.
Core Capabilities
- Standby persistence model: On higher tiers, sandboxes remain on standby with no memory or compute charges; storage-related charges still apply, and unlimited persistence depends on quota tier
- Resume from standby: Blaxel supports resume from standby, preserving filesystem and process state; external network connections are not preserved
- MicroVM isolation: Hardware-enforced kernel-level separation between workloads
- 50,000+ concurrent sandboxes: Scale comparable to Modal's sandbox infrastructure
Security and Compliance
Blaxel maintains SOC 2 Type II certification, ISO 27001, and offers HIPAA BAA for healthcare workloads, positioning it for enterprise deployments with stringent compliance requirements.
Architecture Approach
Blaxel treats sandboxes as persistent "agent computers" rather than ephemeral execution environments. This model benefits coding agents that maintain context across multi-day workflows, preserving shell history, installed dependencies, and execution state.
Best For: Teams building coding agents that require state persistence across sessions, standby resume support, and extended standby on higher tiers.
5. Runloop
Runloop provides coding agent devbox infrastructure with a unique focus on integrated benchmarking. The platform includes built-in SWE-Bench benchmarking capabilities covering Verified, SWE-Smith, and R2E-Gym benchmarks.
Core Capabilities
- Integrated benchmark testing: Run agents against SWE-Bench directly from the platform with no external setup required
- Isolated microVM environments: Runloop's Devboxes provide hardware-level security boundaries through isolated microVM-based environments
- Snapshot branching: Fork devbox disk state for parallel experimentation on different agent approaches
- Repo Connections: RepositoryConnection Inspection workflows for generating build blueprints from connected repositories
Architecture Approach
Runloop uses isolated microVM-backed Devboxes and supports snapshot-based branching, providing strong security boundaries while maintaining the developer experience of container-based workflows.
Use Case Focus
The platform's integrated benchmarking makes it particularly suited for teams systematically evaluating coding agent improvements. Snapshot branching enables A/B testing different agent configurations against the same baseline.
Best For: Teams focused on systematic SWE-bench evaluation and agent improvement, particularly those needing integrated benchmark automation and snapshot-based experimentation.
6. Daytona
Daytona provides persistent development environments with multi-language SDK support spanning Python, TypeScript, Ruby, Go, and Java, offering broader language coverage than most competitors offering one or two SDKs.
Core Capabilities
- Five-language SDK support: Python, TypeScript, Ruby, Go, and Java enable polyglot team integration
- Strong IDE integration: Native VS Code support with built-in LSP support for editor integrations
- Docker/Dev Container compatibility: Standard OCI/container image support for familiar development workflows
- Configurable persistence: Sandboxes support extended runtime with configurable auto-stop behavior
Architecture Approach
Daytona describes its sandboxes as isolated, full Linux environments with a dedicated kernel, filesystem, network stack, and OCI/Docker compatibility, making it accessible for teams with existing container-based workflows.
Use Case Focus
Daytona's strength lies in developer experience: LSP support, IDE integrations, and multi-language SDKs make it accessible for teams with diverse technology stacks building coding agents.
Best For: Polyglot teams building coding agents that prioritize IDE integration and developer experience, particularly those with existing container-based workflows.
7. Fly.io Sprites
Fly.io Sprites provides persistent Firecracker VMs with idle-based billing. No compute charges when sandboxes are inactive, though persistent storage continues to be billed. The platform includes a 100GB durable root filesystem backed by object storage, with NVMe used for active caching, and live checkpoint and restore support.
Core Capabilities
- True idle billing: No compute charges when sandboxes are inactive, though storage is still billed, optimizing costs for sporadic agent usage patterns
- 100GB durable storage: A 100GB durable ext4 root filesystem backed by object storage, with NVMe serving as active cache, included per sandbox
- Checkpoint and restore: Fly.io Sprites supports live checkpoints with restore, reducing the overhead of pausing and resuming sandbox state
- Full Linux environment: Any language and package supported through standard Linux tooling
Architecture Approach
Fly.io Sprites focuses on individual developer workflows and cost efficiency. The idle billing model particularly benefits coding agent development where usage is intermittent rather than continuous.
Use Case Focus
The combination of durable storage, idle billing, and checkpoint/restore support makes Fly.io Sprites well-suited for individual developers iterating on coding agents without the overhead of always-on infrastructure costs.
Best For: Individual developers and small teams building coding agents with sporadic usage patterns who prioritize cost efficiency and persistent storage.
Why Modal Stands Out for SWE-Bench Coding Agents
Official SWE-Bench Integration
Modal has upstreamed support into the SWE-bench framework. Adding a --modal flag lets teams run evaluations in the cloud, execute tests in parallel, and complete the 500-task Verified benchmark in 7 minutes, with no custom infrastructure setup required.
Purpose-Built AI Infrastructure
Modal's architecture is specifically engineered for AI workloads. The platform's custom container runtime, scheduler, and file system are optimized for the unique demands of coding agents: fast cold starts, secure code execution, dynamic scaling, and GPU access when ML workloads require it.
Production-Proven Scale
Modal powers cloud infrastructure for over 10,000 teams, including AI companies running production coding agent workloads. Ramp uses Modal to power Ramp Inspect, its internal background coding agent, scaling to hundreds of concurrent Sandbox sessions. Lovable ran over 1 million sandboxes in 48 hours, reaching 20,000 concurrent sandboxes at peak. This production track record demonstrates reliability that prototype-stage platforms cannot match.
Secure Sandboxed Execution at Massive Scale
Most coding-agent work is CPU-based execution of the code the agent writes, and Modal's sandboxes handle this at 50,000+ concurrent sessions with gVisor isolation and full observability. For coding agents that generate and execute untrusted code autonomously, this combination of scale and security is valuable for teams that need parallel execution at production volumes.
GPU Access When Agents Need It
Modal combines secure code execution with on-demand GPU access in the same platform. When coding agents need to run ML models for code understanding, generation, or analysis, they can call upon GPUs including B200, H200, H100, RTX PRO 6000, A100 80 GB, A100 40 GB, L40S, A10, L4, and T4, without switching platforms or managing separate infrastructure.
Developer Experience Without Compromise
Modal's code-first SDK eliminates YAML configuration overhead, with support for Python, TypeScript, and Go across Sandboxes, Functions, and Modal resources. Teams define compute, images, and scaling in code. This approach enables rapid iteration, critical when you're running hundreds of benchmark variations to improve agent performance.
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 coding agent deployments demand.
For teams building SWE-bench-style coding agents, Modal's combination of official benchmark integration, AI-native infrastructure, production-proven scale, and enterprise compliance makes it the clear choice.
Explore the Modal documentation to get started with coding agent sandboxes.
View Modal Docs