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Serverless Workflow Architecture

Kubiya implements a cutting-edge serverless architecture where every workflow and step runs as an independent Docker container. This design enables unprecedented flexibility, scalability, and software compatibility.

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Core Principles

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1. Containerized Everything

🐳 Docker-Based Execution

Every single step in your workflow runs in its own Docker container:
  • Any Language: Python, JavaScript, Go, Rust, Java, C++, Ruby, etc.
  • Any Tool: Git, AWS CLI, Terraform, kubectl, npm, cargo, etc.
  • Any Library: Install and use any package or dependency
  • Any Version: Pin specific versions of languages and tools

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2. True Statelessness

Each workflow execution is completely independent: 
# This workflow runs in a fresh environment every time
workflow = Workflow(
    name="data-pipeline",
    runner="kubiya-hosted"
)

# Step 1 runs in container A
workflow.add_step(
    name="fetch-data",
    image="python:3.11",
    code="# This container is destroyed after execution"
)

# Step 2 runs in container B (completely separate)
workflow.add_step(
    name="process-data",
    image="python:3.11",
    code="# Fresh container, no state from Step 1"
)

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3. Infinite Scalability

The serverless model means:
  • No pre-provisioned resources
  • Automatic scaling based on demand
  • Pay only for actual execution time
  • Handle 1 or 1,000,000 workflows seamlessly

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Architecture Diagram

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Comparison with Other Orchestration Platforms

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Kubiya vs Traditional Orchestrators

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Execution Model Comparison

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Resource Management

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How It Works

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1. Workflow Definition

You define workflows using Python SDK, YAML, or through AI generation: 
from kubiya_workflow_sdk import Workflow, Step

workflow = Workflow(
    name="multi-tool-pipeline",
    runner="kubiya-hosted"
)

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2. Container Specification

Each step specifies its container requirements: 
# Use pre-built images
workflow.add_step(Step(
    name="python-analysis",
    image="python:3.11-slim",
    packages=["pandas", "numpy", "scikit-learn"],
    code="..."
))

# Or custom images
workflow.add_step(Step(
    name="custom-tool",
    image="myregistry/my-tool:latest",
    code="..."
))

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3. Execution Flow

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4. Data Flow

Steps communicate through artifacts: 
# Step 1: Generate data
workflow.add_step(Step(
    name="generate",
    code="""
    data = create_dataset()
    save_artifact('dataset.csv', data)
    """
))

# Step 2: Process data
workflow.add_step(Step(
    name="process",
    code="""
    data = load_artifact('dataset.csv')
    results = analyze(data)
    save_artifact('results.json', results)
    """
))

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Real-World Examples

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Multi-Language ETL Pipeline

// ... existing code ...

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DevOps Automation

// ... existing code ...

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Advanced Features

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Custom Base Images

Create specialized images for your workflows: 
# Dockerfile
FROM python:3.11
RUN pip install pandas numpy scikit-learn tensorflow
RUN apt-get update && apt-get install -y graphviz
COPY models/ /opt/models/

workflow.add_step(Step(
    name="ml-pipeline",
    image="myregistry/ml-base:latest",
    code="# Use pre-installed ML tools"
))

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Resource Management

Control container resources: 
workflow.add_step(Step(
    name="memory-intensive",
    image="python:3.11",
    resources={
        "memory": "8Gi",
        "cpu": "4",
        "gpu": "1"  # For ML workloads
    },
    code="# Run memory-intensive operations"
))

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Network Isolation

Each container runs in isolation: 
# Step 1: Start a service
workflow.add_step(Step(
    name="api-server",
    image="node:20",
    ports=[3000],
    code="# Start API server"
))

# Step 2: Different network namespace
workflow.add_step(Step(
    name="client",
    image="python:3.11",
    code="# Cannot access Step 1's ports directly"
))

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AI-Powered Generation

With ADK orchestration, you can generate these complex workflows using natural language:
from kubiya_workflow_sdk import KubiyaWorkflow

# Generate entire workflow from description
workflow = KubiyaWorkflow.from_prompt(
    """
    Create a data pipeline that:
    1. Extracts data from PostgreSQL and MongoDB
    2. Transforms using Python pandas
    3. Runs ML predictions with TensorFlow
    4. Stores results in S3 and sends Slack notification
    """,
    runner="kubiya-hosted"
)

# ADK generates the complete containerized workflow
result = workflow.execute()

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Benefits Summary

🚀 Any Software

Run literally any software, tool, or language in your workflows

📦 Zero Dependencies

No need to pre-install anything - containers have everything

🔄 Perfect Isolation

Each step runs in complete isolation with no side effects

⚡ Instant Scale

From 1 to 1 million executions without infrastructure changes

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Security & Compliance

The containerized architecture provides:
  • Process Isolation: Each step runs in its own namespace
  • Resource Limits: Prevent runaway processes
  • Network Policies: Control communication between steps
  • Audit Trails: Complete execution history
  • Secrets Management: Secure credential injection

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What’s Next?

Getting Started

Create your first containerized workflow

ADK Provider

Learn about AI-powered workflow generation

Examples

See real-world containerized workflows