Author: Alan Blount (@zeroasterisk) Date: June 29, 2026 Context: Evaluating sandbox providers for the Flue agent framework with focus on GEAP (Gemini Enterprise Agent Platform) integration
Flue supports 10+ sandbox providers via its SandboxApi interface. This report compares the five most significant providers across capabilities that matter for AI agent workloads. GEAP is the only provider with enterprise governance built in, but trades developer experience for security posture.
| Capability | GEAP | E2B | Daytona | Modal | Cloudflare |
|---|---|---|---|---|---|
| Cold start | ~2 min (LRO) | ~150ms | ~90ms | varies | <50ms |
| Languages | Python, JS | Any (Linux) | Any (Linux) | Python | JS (V8) |
| Shell access | No (code exec only) | Yes (full bash) | Yes (full bash) | Yes | No |
| Filesystem | Via code exec | Direct API | Direct SSH/exec | File open/read/write | In-memory |
| Persistence | 14 days TTL | 24hr sessions | Unlimited | Unlimited | 30min |
| Custom containers | Yes (BYOC) | Yes (templates) | Yes (Docker) | Yes (images) | Yes (DO) |
| GPU | No | No | Yes (H100) | Yes (A100/H100) | No |
| Network | Allowlist only | Full | Full | Full | Edge-only |
| Snapshots | Yes | No | Via workspace | Via container | No |
| Max RAM | 4GB | 8GB+ | configurable | configurable | 128MB |
| Pricing | GCP billing | ~$0.05/vCPU-hr | ~$0.05/vCPU-hr | pay-per-use | Workers plan |
| Auth model | GCP IAM/SPIFFE | API key | API key | API key/OAuth | CF account |
| Region | us-central1 only | Multi-region | Multi-region | Multi-region | Global edge |
What it is: Google Cloud's managed sandbox for AI agents, part of the broader Gemini Enterprise Agent Platform.
E2E verified (June 29, 2026): Successfully created reasoning engine, provisioned sandbox, and executed Python code via REST API.
Architecture:
- Sandboxes run inside "Reasoning Engines" — a managed container that persists state
- Code execution via REST: input is a JSON Chunk (base64-encoded
{"code": "..."}withmimeType: application/json) - Output is a JSON Chunk with
{exit_status_int, msg_err, msg_out} - No direct shell access — all operations must be expressed as Python or JavaScript code
- Network disabled by default; must explicitly declare an allowlist
Unique strengths:
- Enterprise governance stack: Agent Gateway (policy enforcement), Agent Registry (centralized catalog), Agent Identity (SPIFFE + X.509 certs), Semantic Governance Policies (natural language security rules)
- 14-day state persistence — longest of any provider
- Snapshots — save and restore sandbox state
- BYOC — bring your own container image
- Integrated with Vertex AI / Gemini models — native model access from within the sandbox
- Zero-trust by default — no network access unless explicitly allowed
Weaknesses:
- Slowest cold start (~2 min via long-running operation vs sub-second for competitors)
- No shell access — must generate Python for every operation (mkdir, ls, cat all become Python code)
- us-central1 only — single region
- Complex IAM —
executeCodepermission not in any predefined role (required custom role creation) - Poorly documented REST API — had to reverse-engineer request format from Python SDK source code
- Discovery doc schema doesn't match actual API — Chunk format was not documented correctly
Best for: Compliance-heavy enterprise workloads (finance, healthcare, government) where governance, audit trails, and zero-trust networking are requirements.
What it is: Purpose-built code execution sandbox using Firecracker microVMs.
Architecture:
- Each sandbox runs in a Firecracker microVM (same tech as AWS Lambda)
- Full Linux environment with bash shell access
- Direct filesystem API (read, write, mkdir, stat)
- SDK-first design with TypeScript, Python, and Go clients
Strengths:
- Sub-150ms cold start — fast enough for interactive use
- Full Linux environment — any language, any tool, full bash
- Direct filesystem API — no need to shell out for file operations
- Custom templates — pre-built environments with specific packages
- Billionth sandbox milestone — proven at scale
- Simple API key auth — frictionless developer onboarding
Weaknesses:
- 24-hour session limit — not suitable for long-running workloads
- No GPU — can't run ML inference
- No snapshots — state is lost when session ends
- No enterprise governance — API key is the only auth model
Best for: Code interpretation, LLM-generated code execution, notebook-in-a-loop workflows. The reference implementation for "what an agent sandbox should feel like."
What it is: Persistent development workspace infrastructure, pivoted to AI agent use in 2025.
Architecture:
- Full Docker containers with SSH access
- Persistent workspaces that survive across sessions
- GPU support (H100)
- Git-aware workspace management
Strengths:
- Fastest cold start (~90ms)
- Unlimited persistence — workspace survives indefinitely
- GPU support — H100 available
- Full bash + SSH — complete development environment
- Git integration — clone repos, manage branches natively
- $24M funding — active development
Weaknesses:
- Workspace-oriented — more than needed for simple code execution
- No built-in governance — API key auth only
- Higher complexity — more moving parts than E2B
Best for: Multi-turn agent workflows that need a persistent workspace (clone repo → install deps → make changes → test → commit). The "development environment as a service" model.
What it is: Serverless GPU compute platform optimized for ML/AI workloads.
Architecture:
- gVisor containers for secure Python execution
- GPU-accelerated (A100, H100)
- Serverless scaling (pay per second of compute)
- Python-first SDK
Strengths:
- GPU support — A100/H100 for inference and training
- Serverless scaling — automatic scale-to-zero
- $355M funding — well-capitalized
- Python-native — deep Python ecosystem integration
- Unlimited persistence — containers persist
Weaknesses:
- Thin filesystem API — only file open/read/write/close; directory ops require shell
- Python-focused — limited multi-language support
- Higher latency — GPU provisioning adds startup time
- No enterprise governance stack
Best for: ML inference, data science workloads, GPU-heavy agent tasks. The only provider where an agent can train a model or run heavy inference inside the sandbox.
What it is: Edge-deployed container sandbox using Durable Objects and V8 isolates.
Architecture:
- V8 isolates for JavaScript execution (Dynamic Workers)
- Durable Object containers for stateful workloads
- Global edge network deployment
- R2 storage integration
Strengths:
- Fastest cold start (<50ms)
- Global edge deployment — lowest latency worldwide
- Tight Flue integration — Flue was built by the Astro team, Cloudflare is a launch partner
- Durable Objects — stateful containers at the edge
Weaknesses:
- 30-minute session limit — shortest of any provider
- No GPU
- JavaScript only (V8 isolates) — can't run Python, bash, or arbitrary Linux code
- 128MB memory limit — most constrained
- Workers Paid plan required — not free tier
- Complex setup — requires wrangler, Dockerfile, DO bindings
Best for: Low-latency, edge-deployed agent tasks that run JavaScript. Natural fit for Flue projects already deployed on Cloudflare Workers.
| Use Case | Recommended Provider |
|---|---|
| Enterprise compliance (finance, healthcare, gov) | GEAP |
| Code interpretation / LLM code execution | E2B |
| Persistent multi-turn development workspace | Daytona |
| GPU inference / ML workloads | Modal |
| Low-latency edge execution (JS) | Cloudflare |
| Quick prototype / fastest DX | E2B or Daytona |
Most production agents will use two providers: a code interpreter (E2B) for quick execution and a persistent workspace (Daytona or GEAP) for long-running development tasks. GEAP differentiates by being the only option that adds governance on top — but at the cost of developer experience.
| Provider | Flue Blueprint | Adapter Status |
|---|---|---|
| E2B | sandbox--e2b.md |
Official, mature |
| Daytona | sandbox--daytona.md |
Official, mature |
| Modal | sandbox--modal.md |
Official |
| Cloudflare | sandbox--cloudflare.md |
Official, launch partner |
| Vercel | sandbox--vercel.md |
Official |
| GEAP | sandbox--geap.md |
New — prototype on zeroasterisk/flue |
2027.dev benchmarks how agent-friendly devtools are by having Claude Code autonomously run getting-started guides. Rankings are by Time, Cost, Errors, and Interruptions.
GEAP would score:
- Time: Poor (2 min cold start + complex setup)
- Cost: Moderate (GCP billing)
- Errors: High risk (undocumented API format, custom IAM role needed)
- Interruptions: High (IAM permission prompts, region restriction)
To improve GEAP's 2027.dev score:
- Fix the documentation — the REST API format needs to match what the SDK actually sends
- Include
executeCodepermission in a predefined role - Reduce cold start (pre-warmed sandbox pools?)
- Multi-region support
- Better error messages (the generic 400 "Invalid argument" with no detail is hostile to autonomous agents)
This report is based on E2E testing of GEAP (June 29, 2026), Flue blueprint documentation, and third-party benchmark research. Updated continuously.