HopX vs Docker vs AWS Lambda: Choosing the Right Execution Environment

When building AI agents that execute code, you need an execution environment. The three most common options are Docker containers, AWS Lambda, and microVM-based sandboxes like HopX.

Each has its place. This guide helps you choose the right one.

Quick Comparison

Docker Containers

Docker is the industry standard for packaging and deploying applications. It uses OS-level virtualization to run isolated processes.

Pros

• Mature ecosystem - Vast library of pre-built images
• Developer familiarity - Most developers know Docker
• Full control - You manage everything
• No duration limits - Run as long as needed
• Persistent storage - Volumes survive restarts

Cons

• Not a security boundary - Containers share the host kernel
• Container escapes - Regular CVEs (2019, 2020, 2022 had major ones)
• Infrastructure overhead - You manage orchestration, scaling, updates
• Cold start for new containers - 500ms-2s typically
• Resource management - Manual configuration of limits

When to Use Docker

✅ Running trusted code that you wrote ✅ Long-running services (web servers, APIs) ✅ Development environments ✅ CI/CD pipelines

❌ Executing untrusted or LLM-generated code ❌ Multi-tenant workloads requiring isolation ❌ Security-critical applications

Docker Example

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .

CMD ["python", "main.py"]

docker build -t my-agent .

docker run --rm my-agent

AWS Lambda

Lambda is AWS's serverless compute service. Code runs in microVMs managed by AWS.

Pros

• True isolation - Each function runs in a dedicated microVM
• Zero infrastructure - AWS handles everything
• Auto-scaling - From 0 to thousands of concurrent executions
• Pay-per-use - Only pay when code runs
• Integrated with AWS - Easy access to S3, DynamoDB, etc.

Cons

• Cold starts - 1-5 seconds for new instances
• 15-minute limit - Long tasks must be split
• No persistent filesystem - /tmp is cleared between invocations
• 250MB package limit - Layers help but are complex
• Vendor lock-in - AWS-specific patterns
• Expensive at scale - Warm functions still cost money

When to Use Lambda

✅ Event-driven workloads (webhooks, queue processing) ✅ Infrequent, short-duration tasks ✅ AWS-centric architectures ✅ Batch processing

❌ Real-time AI agents (cold starts too slow) ❌ Long-running computations (15-min limit) ❌ Tasks requiring persistent state ❌ Heavy package dependencies

Lambda Example

# handler.py

def lambda_handler(event, context):

    code = event.get('code', '')

    # Execute code (but with all Lambda limitations)

    exec(code)  # Don't do this in production!

    return {'statusCode': 200}

# serverless.yml

functions:

  executor:

    handler: handler.lambda_handler

    timeout: 900  # Max 15 minutes

    memorySize: 1024

HopX Sandboxes

HopX provides microVM-based sandboxes optimized for AI workloads. Each sandbox is an isolated Linux VM with its own kernel.

Pros

• True isolation - Hardware-level separation via microVMs
• 100ms cold starts - Fast enough for real-time AI
• No duration limits - Run for hours if needed
• Persistent filesystem - Files survive between calls
• Runtime package installation - pip install anything
• Full Linux environment - Root access, any tool
• Simple SDK - Python and JavaScript

Cons

• Newer platform - Less ecosystem than Docker/Lambda
• Requires API key - Not self-hosted
• Cost for idle sandboxes - Pay while running (pause to save)

When to Use HopX

✅ AI agent code execution ✅ Running LLM-generated code safely ✅ Multi-tenant SaaS with code execution ✅ Data analysis and notebook workloads ✅ Browser automation and desktop testing ✅ Long-running agent tasks

❌ Simple web application hosting ❌ Event-driven queue processing ❌ Extremely high-frequency, low-latency calls

HopX Example

from hopx_ai import Sandbox

# Create isolated sandbox

with Sandbox.create(template="code-interpreter") as sandbox:

    # Install any package at runtime

    sandbox.commands.run("pip install pandas matplotlib")

    # Execute untrusted code safely

    result = sandbox.run_code("""

import pandas as pd

df = pd.DataFrame({'x': [1,2,3], 'y': [4,5,6]})

print(df.describe())

""")

    print(result.stdout)

Real-World Scenario Comparisons

Scenario 1: AI Coding Assistant

You're building a coding assistant that executes user code to help debug.

Winner: HopX - Built for this exact use case.

Scenario 2: Webhook Processing

You receive webhooks and need to process them quickly.

Winner: Lambda - Designed for event-driven, short tasks.

Scenario 3: Long-Running Data Pipeline

You have a data pipeline that runs for 2 hours processing large datasets.

Winner: Docker/HopX - Lambda can't handle this.

Scenario 4: Multi-Tenant SaaS

You're building a SaaS where each customer can run custom code.

Winner: HopX/Lambda - Docker lacks sufficient isolation for multi-tenant.

Cost Comparison

Let's compare costs for a typical AI agent workload: 10,000 executions/day, 30 seconds average, 1 vCPU, 1GB RAM.

Docker (self-hosted on AWS EC2)

c5.large (2 vCPU, 4GB): $0.085/hour

Monthly: $0.085 × 24 × 30 = $61.20

+ Reserved capacity for spikes: ~$100/month

Total: ~$160/month

But you're paying for idle time and managing infrastructure.

AWS Lambda

10,000 executions × 30 days = 300,000/month

Duration: 300,000 × 30s = 9,000,000 GB-seconds

Cost: 9,000,000 × $0.0000166667 = $150/month

+ Requests: 300,000 × $0.20/million = $0.06

Total: ~$150/month

But cold starts hurt UX, and 15-minute limit is restrictive.

HopX

Compute: 9,000,000 vCPU-seconds × $0.000014 = $126

Memory: 9,000,000 GB-seconds × $0.0000045 = $40.50

Total: ~$167/month

But you get 100ms cold starts, no duration limits, and full Linux environment.

Cost verdict: All three are competitive. Choose based on features, not cost.

Decision Framework

Use this flowchart to choose:

Is the code trusted (you wrote it)?

├── Yes → Docker (full control, mature ecosystem)

└── No → Continue...

Is the code LLM-generated or user-submitted?

├── Yes → Need strong isolation

│   ├── Tasks under 15 minutes? → Lambda is an option

│   └── Longer tasks or real-time? → HopX

└── No → Depends on requirements

Do you need sub-second cold starts?

├── Yes → HopX (~100ms)

└── No → Lambda (1-5s) is acceptable

Do you need persistent filesystem?

├── Yes → Docker or HopX

└── No → Lambda works

Are you already deep in AWS ecosystem?

├── Yes → Lambda for integration benefits

└── No → Evaluate based on other factors

Hybrid Approaches

You don't have to choose just one. Many teams use:

1. Docker for their main application (web servers, APIs)
2. Lambda for event processing (webhooks, queues)
3. HopX for AI agent code execution

# Your main app (Docker/K8s)

@app.post("/execute")

async def execute_code(request: CodeRequest):

    # Delegate unsafe execution to HopX

    with Sandbox.create(template="code-interpreter") as sandbox:

        result = sandbox.run_code(request.code)

        return {"output": result.stdout}

# Meanwhile, Lambda handles webhooks

# HopX handles AI agent tasks

Conclusion

The right choice depends on your specific requirements. For AI agents that execute code, HopX provides the best combination of security, speed, and flexibility.

Ready to try HopX? Sign up for free and get $200 in credits.