The AI-First Make Alternative

While Make offers visual workflow building, Draft’n Run takes it further with native AI capabilities, advanced agent orchestration, and enterprise-grade features designed for modern automation needs.

Head-to-Head Comparison

Feature	Draft'n Run	Make	Advantage
Visual Builder	AI-aware canvas	General purpose canvas	🏆 Draft'n Run
AI Integration	Native GPT-4, Claude, Gemini	Via HTTP modules	🏆 Draft'n Run
Agent Orchestration	Multi-agent workflows	Not supported	🏆 Draft'n Run
RAG Systems	Built-in with Qdrant	Manual implementation	🏆 Draft'n Run
Number of Apps	100+ AI-focused	1,500+ general	🏆 Make
Execution Model	Scenario + Agent-based	Scenario-based only	🏆 Draft'n Run
Error Handling	AI-powered retry logic	Manual configuration	🏆 Draft'n Run
Observability	Full OpenTelemetry traces	Execution history	🏆 Draft'n Run
Self-hosting	Full support	Not available	🏆 Draft'n Run
Learning Curve	Moderate	Moderate	⚖️ Tie

Pricing Comparison

Make Pricing Structure

Free:         $0/mo     → 1,000 operations
Core:         $9/mo     → 10,000 operations
Pro:          $16/mo    → 10,000 operations + extras
Teams:        $29/mo    → 10,000 operations + team features
Enterprise:   Custom    → Custom operations

Limitations:
- Operations count includes every module
- AI API calls can be expensive
- Premium apps cost extra operations
- Complex scenarios use operations quickly

Draft’n Run Advantage

Free:         $0/mo     → 1,000 runs
Pro:          $49/mo    → 10,000 runs
Business:     $199/mo   → 50,000 runs
Enterprise:   $499/mo   → Unlimited

Included:
✓ All AI models (GPT-4, Claude, Gemini)
✓ RAG and vector search
✓ Agent orchestration
✓ Full observability
✓ No operation counting complexity

Real Cost Example

AI workflow with document processing (10K runs/month):

• Make: $16/mo + API costs (~$100/mo) = ~$116/mo
• Draft’n Run: $49/mo all included
• Savings: $67/mo (58%)

What You Keep When Switching

✅ Keep Visual Workflow Building

Draft’n Run offers the same visual experience:

• Drag-and-drop interface
• Visual connections between nodes
• Real-time execution visualization
• Debugging with step-by-step views
• Module/component library

✅ Keep Your Logic

Familiar concepts translate directly:

• Routers → Conditional branches
• Aggregators → Data collection
• Iterators → Loop processing
• HTTP modules → API calls
• Data stores → State management

✅ Keep Your Integrations

We support the apps that matter:

• Google Workspace
• Slack, Microsoft Teams
• Airtable, Notion
• GitHub, GitLab
• Shopify, WooCommerce
• And 100+ more

What You Gain

🤖 Native AI Capabilities

In Make (Complex):

HTTP Module → OpenAI API
↓
Parse JSON response
↓
Error handling
↓
Format output
↓
Use in next module

In Draft’n Run (Simple):

llm_component:
  model: gpt-4
  prompt: "Analyze customer feedback"
  context: previous_data
  # That's it - fully integrated!

🧠 Multi-Agent Orchestration

# Impossible in Make - native in Draft'n Run
workflow:
  - research_agent:
      task: "Gather market data"
      tools: [internet_search, database]
  - analysis_agent:
      task: "Analyze findings"
      input: research_agent.output
  - writer_agent:
      task: "Create report"
      input: analysis_agent.insights

📚 Built-in RAG Systems

# Simple RAG in Draft'n Run
rag_component = RAGAgent(
    knowledge_base="company_docs",
    model="gpt-4",
    retrieval_k=5
)

answer = rag_component.query(
    "What is our refund policy?"
)

🔍 Advanced Observability

• Distributed tracing with OpenTelemetry
• Prometheus metrics
• Grafana dashboards
• Real-time monitoring
• Performance analytics

Migration Success Stories

SaaS Startup: From Complex to Simple

“Our Make scenarios were getting unwieldy with all the HTTP modules for AI calls. Draft’n Run’s native AI support reduced our workflow complexity by 70%.” — David Kim, Technical Lead

E-commerce Brand: Better AI, Lower Costs

“Make couldn’t handle our document processing needs. Draft’n Run’s RAG system processes 10K documents/month with better accuracy and half the cost.” — Emma Rodriguez, Operations Manager

Agency: Finally Enterprise-Ready

“We needed self-hosting and audit logs for clients. Make couldn’t provide that. Draft’n Run gave us enterprise features without enterprise prices.” — James Wilson, Agency Owner

Migration Guide

Step 1: Audit Your Make Scenarios

// Export your Make scenarios
// Identify:
scenarios.forEach(scenario => {
  console.log({
    modules: scenario.modules.length,
    ai_modules: scenario.modules.filter(m => m.type === 'http'),
    complexity: scenario.routes.length
  });
});

Step 2: Map to Draft’n Run Components

Make Module	Draft’n Run Component
HTTP (AI APIs)	LLM Component
Data Store	State Management
Router	Conditional Branch
Iterator	Loop Component
Aggregator	Data Collector
Webhooks	API Triggers

Step 3: Rebuild with AI Enhancements

# Transform Make scenario to Draft'n Run
# Example: Customer support automation

original_make_modules: 12
draft_n_run_components: 4  # With AI doing the heavy lifting

workflow:
  - trigger: new_support_ticket
  - ai_classifier:
      model: gpt-4
      task: "Classify and prioritize"
  - conditional_router:
      rules: ai_classifier.category
  - response_generator:
      agent: support_agent

Step 4: Test & Optimize

• Run parallel testing
• Compare outputs
• Optimize AI prompts
• Deploy when confident

Common Make → Draft’n Run Migrations

Data Enrichment Pipeline

Before (Make):

• 8 modules: Webhook → HTTP (AI) → Parse → Format → HTTP (API) → Store → Notify → Response
• Complex error handling across modules
• Difficult to maintain

After (Draft’n Run):

• 3 components: Trigger → AI Agent → Action
• Built-in error handling
• Self-documenting workflow

Document Processing

Before (Make):

• Multiple HTTP calls to different AI services
• Manual chunking and processing
• No vector search
• High operational complexity

After (Draft’n Run):

• Single RAG component with built-in:
  • Document parsing
  • Intelligent chunking
  • Vector storage
  • Semantic search

Multi-Step Research

Before (Make):

• Linear scenario with many steps
• No intelligent orchestration
• Manual data passing
• Fixed execution order

After (Draft’n Run):

• Multi-agent workflow with:
  • Intelligent task delegation
  • Parallel execution
  • Dynamic routing
  • Context sharing

Feature Deep Dive

Why Draft’n Run is Better for AI

Make’s Approach:

• AI is an external service (HTTP modules)
• Manual prompt engineering in every scenario
• No context management
• Limited error handling for AI calls
• No specialized AI features

Draft’n Run’s Approach:

• AI is a first-class citizen
• Centralized prompt management
• Automatic context handling
• AI-aware error recovery
• Specialized components: RAG, agents, semantic search

Enterprise Features Make Lacks

Feature	Draft’n Run	Make
Self-hosting	✅ Full support	❌ Cloud only
Audit Logs	✅ Complete trail	⚠️ Basic history
SLA Guarantees	✅ Available	❌ Not offered
Custom Deployment	✅ Supported	❌ Not available
Version Control	✅ Git integration	⚠️ Limited
Staging Envs	✅ Multiple	⚠️ Limited

Start Your Migration Today

Frequently Asked Questions

How long does migration from Make take?

Most scenarios can be migrated in 1-2 hours each. Simple scenarios often become simpler with Draft’n Run’s AI components. We provide migration tools and support to help.

Can I migrate complex scenarios with many modules?

Yes! Complex scenarios often benefit most from migration. Our AI components can replace multiple Make modules, simplifying your workflows while adding intelligence.

What if I use Make's data stores?

Draft’n Run has equivalent state management. We can help migrate your data stores and ensure continuity. Many customers find our approach more flexible.

Will I lose my Make app integrations?

We support 100+ popular integrations. For missing integrations, you can use webhooks, HTTP calls, or our custom integration builder. Most AI scenarios need fewer direct integrations.

How do operation counts compare?

Draft’n Run counts workflow runs, not individual operations/modules. This means complex workflows cost the same as simple ones. Much more predictable pricing than Make’s operation counting.

Can I test scenarios before fully migrating?

Absolutely! Use our free tier (1,000 runs) to rebuild and test key scenarios. Run both platforms in parallel until you’re confident. No credit card required.

Is the visual builder as good as Make's?

Many users find our builder superior - it’s designed specifically for AI workflows. You get the same drag-and-drop experience plus AI-aware components and better debugging tools.

The Bottom Line

Make: Great for general automation with visual building Draft’n Run: Built for AI-first automation with visual building + intelligence

Make the switch and get:

• ✅ Native AI capabilities (not HTTP modules)
• ✅ Multi-agent orchestration
• ✅ Built-in RAG and vector search
• ✅ Enterprise features (self-hosting, audit logs)
• ✅ Better value for AI workflows
• ✅ Simpler workflows with more intelligence

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Migration support included | 60-day guarantee | No credit card required to start

Related Comparisons & Alternatives

Platform Comparisons:

• Draft’n Run vs n8n - AI-first vs general automation
• Make vs Zapier vs Draft’n Run - 3-way comparison
• Draft’n Run vs AirOps - AI workflow platforms
• LangChain vs AutoGPT - AI framework comparison

Alternative Platform Guides:

• Zapier Alternatives - Best Zapier alternatives for AI
• Make Alternatives - Make alternatives
• LangChain Alternatives - LangChain alternatives
• n8n Alternatives - Complete n8n alternatives

Draft’n Run Resources:

• AI Workflow Builder - Visual workflow builder
• AI Chatbot Platform - Production chatbots
• AI Automation - End-to-end automation
• Integration Library - 100+ integrations
• Pricing - Transparent plans
• Request Demo - Get started
• Contact Sales - Enterprise plans