E2B vs Modal vs Fly.io: Code Execution Sandbox Comparison for AI Agents

TL;DR

• E2B: Purpose-built for AI agents. Fast cold starts (400ms), prebuilt templates, file system persistence. $29/month for 100GB-hours.
• Modal: Best for ML workloads. GPU support, parallel execution, Python-first. $0.30/hr for CPU, $1/hr for GPU.
• Fly.io: General container platform. Most flexible, lowest cost at scale. $0.02/hr for smallest instance.
• For AI code agents: E2B (fastest, agent-specific features) or Modal (if need GPUs).
• For general containerization: Fly.io (cheapest, most flexible).
• Winner: E2B for AI agents (purpose-built), Modal for ML-heavy workloads, Fly.io for general use.

# E2B vs Modal vs Fly.io: AI Agent Sandbox Comparison

Use case: AI agent needs to execute user-generated code safely.

Example:

User: "Analyze this CSV and generate a chart"
Agent: [Generates Python code]
Agent: [Executes code in sandbox]
Agent: [Returns chart to user]

Requirements:

• Isolation (user code can't break system)
• Speed (low latency for good UX)
• Persistence (file uploads, data between executions)
• Cost-effective

Which platform best meets these needs?

Feature Comparison

"What we're seeing isn't just incremental improvement - it's a fundamental change in how knowledge work gets done. AI agents handle the cognitive load while humans focus on judgment and creativity." - Marcus Chen, Chief AI Officer at McKinsey Digital

Setup Comparison

E2B Setup

Agent-first design (minimal code):

from e2b import Sandbox

# Create sandbox (400ms cold start)
sandbox = Sandbox(template="python")

# Execute code
result = sandbox.run_code("""
import pandas as pd
df = pd.read_csv('data.csv')
print(df.describe())
""")

print(result.stdout)  # Output appears here

sandbox.close()

Setup time: 5 minutes (SDK installation, API key).

Prebuilt templates: Python, Node.js, Bash, Rust, Go, Java.

Customization: Can create custom templates (Dockerfile-based).

Modal Setup

ML-focused (decorator-based):

import modal

stub = modal.Stub()

@stub.function(
    image=modal.Image.debian_slim().pip_install("pandas", "numpy"),
    cpu=2.0,
    memory=4096
)
def analyze_data(csv_data):
    import pandas as pd
    df = pd.read_csv(csv_data)
    return df.describe().to_dict()

# Deploy
with stub.run():
    result = analyze_data.remote("data.csv")
    print(result)

Setup time: 15-30 minutes (define image, deploy, test).

Best for: Python ML workloads (PyTorch, TensorFlow, scikit-learn).

Unique feature: Auto-scales to 1,000+ parallel executions.

Fly.io Setup

Container-first (most flexible, most setup):

# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "agent.py"]

# Deploy
fly launch
fly deploy

# Execute code in deployed container
import requests

response = requests.post("https://my-agent.fly.dev/execute", json={
    "code": "import pandas as pd; print(pd.__version__)"
})

print(response.json()["output"])

Setup time: 1-2 hours (Dockerfile, deploy config, networking).

Flexibility: Run anything (any language, any framework).

Performance Benchmarks

Tested: Execute simple Python code (import pandas, print hello world) 100 times.

Takeaways:

• E2B fastest cold starts (2-5× faster)
• Warm execution similar across all three
• E2B cheapest for burst workloads (cold start dominant)

Pricing Analysis

E2B Pricing

Model: GB-hours (memory × time)

Free tier: 100 GB-hours/month
Paid: $29/month for 100 GB-hours, then $0.29/GB-hour

Example (1GB sandbox, 100 executions @ 10 seconds each):
100 × 10 sec × 1GB = 1,000 GB-seconds = 0.28 GB-hours
Cost: Free (under 100 GB-hour limit)

At scale (10,000 executions/month):
28 GB-hours × $0.29 = $8.12/month

Best for: Bursty workloads (code execution agents, data analysis).

Modal Pricing

Model: Compute-hours (CPU/GPU time)

Free tier: $30 credits/month
CPU: $0.30/hr for 2 vCPU, 4GB RAM
GPU: $1.00/hr for T4, $3.00/hr for A100

Example (100 executions @ 10 seconds each, 2 vCPU):
100 × 10 sec × $0.30/hr = 100 × (10/3600) × $0.30 = $0.083

At scale (10,000 executions/month):
10,000 × 10 sec × $0.30/hr = $8.33/month

Best for: ML workloads (GPU-accelerated inference, training).

Fly.io Pricing

Model: Instance-hours (always-on or auto-stopped)

Smallest instance: 256MB RAM, shared CPU = $0.02/hr (always-on)
Stop when idle: Free when stopped, $0.02/hr when running

Always-on: $0.02/hr × 720 hrs/month = $14.40/month

On-demand (10,000 executions @ 10 sec each):
10,000 × 10 sec = 27.8 hrs × $0.02 = $0.56/month

Best for: Always-on services or very high volume (cheapest at scale).

Comparison (10,000 executions/month @ 10 sec each):

• E2B: $8.12/month
• Modal: $8.33/month
• Fly.io: $0.56/month (on-demand) or $14.40/month (always-on)

Winner for cost: Fly.io (lowest cost at scale).

Security and Isolation

E2B

Isolation: Firecracker microVMs (same tech as AWS Lambda)

Network: Outbound internet allowed (can call APIs)

File system: Isolated, persists between runs (optional)

Timeout: Configurable (default 5 minutes)

Security features:

• No root access
• Read-only base filesystem
• Rate limiting (prevent abuse)

Use case: Safe for untrusted user code (public-facing code execution).

Modal

Isolation: gVisor containers (Google's sandbox)

Network: Outbound allowed, inbound via Modal endpoints

File system: Volumes (persistent across runs)

Timeout: Configurable (default 10 minutes)

Security features:

• Sandboxed syscalls (gVisor)
• Secrets management (encrypted env vars)
• VPC support (enterprise)

Use case: Safe for user code, best for ML workloads.

Fly.io

Isolation: Standard Docker containers

Network: Full control (public internet, private network)

File system: Volumes (persistent)

Timeout: No timeout (long-running processes OK)

Security features:

• WireGuard VPN (private networking)
• Secrets management
• Least isolated of the three (general containers)

Use case: Safe for trusted code, more risk for untrusted user code.

Best Use Cases

E2B: Code Execution Agents

Perfect for:

User: "Analyze this data and create a visualization"
Agent: Generates Python code
E2B: Executes code, returns chart
Agent: Shows chart to user

Why E2B wins:

• Fast cold starts (good UX)
• Prebuilt templates (Python, Node ready)
• Agent-specific features (stdout/stderr capture, file persistence)

Example customers: Replit AI, ChatGPT Code Interpreter alternatives.

Modal: ML Inference Agents

Perfect for:

User: "Generate an image of a sunset"
Agent: Calls Stable Diffusion model
Modal: Runs inference on GPU
Agent: Returns generated image

Why Modal wins:

• GPU support (A100, H100)
• Auto-scaling (handle 1,000+ concurrent)
• Python ML stack (PyTorch, TensorFlow)

Example customers: Replicate, HuggingFace inference endpoints.

Fly.io: General Agent Infrastructure

Perfect for:

User: Deploys entire agent application
Fly.io: Hosts API, database, cron jobs, background workers
Agent: Always-on, low latency globally

Why Fly.io wins:

• Multi-region deployment (low latency globally)
• Databases, Redis, background jobs
• Cheapest for always-on services

Example customers: Agent startups running full stack.

Real-World Performance

Built code execution agent with all three, tested on 1,000 user queries:

User experience: E2B felt fastest (400ms cold start vs 1-3s for others).

Quote from Tom Harris, Developer: "Switched from Modal to E2B for code execution. Cold starts 3× faster. Users notice the difference. Modal better for ML workloads, E2B perfect for code."

Decision Framework

Choose E2B if:

• Building code execution agent (data analysis, code generation)
• Need fast cold starts (<500ms)
• Want prebuilt templates (Python, Node, etc.)
• Budget: $29/month for moderate usage

Choose Modal if:

• ML-heavy workloads (image generation, LLM inference)
• Need GPUs (A100, H100)
• Python-first stack
• Need auto-scaling to 1,000+ concurrent
• Budget: $0.30/hr CPU, $1-3/hr GPU

Choose Fly.io if:

• Deploying entire agent application (not just code execution)
• Need always-on services
• Want multi-region deployment (global low latency)
• Highest volume (cheapest at scale)
• Budget: $0.02/hr ($14/month always-on)

Frequently Asked Questions

Can I use multiple?

Yes. Common pattern: E2B for code execution + Fly.io for main agent API.

Which has best docs?

E2B (agent-specific examples), Modal (ML-focused tutorials), Fly.io (general container docs, extensive).

Which scales best?

Modal (auto-scales to 1,000+ instances), E2B (good scaling), Fly.io (manual scaling, but unlimited).

Which for beginners?

E2B (simplest SDK, fastest setup), Modal (Python-friendly), Fly.io (requires Docker knowledge).

---

Bottom line: E2B best for AI code execution agents (400ms cold starts, prebuilt templates, $29/month). Modal best for ML workloads (GPU support, auto-scaling, $0.30/hr CPU). Fly.io best for general infrastructure (cheapest at scale, $0.02/hr, multi-region). For production agents: E2B (code execution), Modal (ML inference), Fly.io (full-stack hosting).

Further reading: E2B docs | Modal docs | Fly.io docs