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

TL;DR

• E2B: Purpose-built for AI agents. Fast sub-second cold starts, prebuilt templates, file system persistence. Usage-based pricing.
• Modal: Best for ML workloads. GPU support, parallel execution, Python-first. Pay per compute-second, with GPUs priced separately.
• Fly.io: General container platform. Most flexible, and cheap at scale for small instances.
• 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

Setup Comparison

E2B Setup

Agent-first design (minimal code):

from e2b import Sandbox

# Create sandbox (fast 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: 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: Usually under an hour (define image, deploy, test).

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

Unique feature: Auto-scales to large numbers of 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: Longest of the three (Dockerfile, deploy config, networking).

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

Performance

Cold starts are where the three differ most, so benchmark with your own workload before committing:

Takeaways:

• E2B generally has the fastest cold starts, because sandboxes are its core product
• Warm execution is similar across all three
• For bursty workloads dominated by cold starts, E2B and Modal tend to suit best

Pricing Analysis

The figures below are illustrative, to show how each pricing model behaves. Check each vendor's pricing page for current rates before you budget.

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)

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 (handles high concurrency)
• Python ML stack (PyTorch, TensorFlow)

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

User Experience

For a code execution agent, cold start latency is what users notice most. A sandbox that starts in well under a second feels instant; one that takes several seconds feels like the agent is stalling. That is why E2B tends to suit interactive code execution, while Modal's strengths show up in ML-heavy workloads.

Decision Framework

Choose E2B if:

• Building code execution agent (data analysis, code generation)
• Need fast, sub-second cold starts
• Want prebuilt templates (Python, Node, etc.)
• Want usage-based pricing for bursty workloads

Choose Modal if:

• ML-heavy workloads (image generation, LLM inference)
• Need GPUs (A100, H100)
• Python-first stack
• Need auto-scaling to high concurrency

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)

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 many 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).

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Bottom line: E2B best for AI code execution agents (fast cold starts, prebuilt templates). Modal best for ML workloads (GPU support, auto-scaling). Fly.io best for general infrastructure (cheapest at scale, 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