AI Workflow Automation: OpenAI AgentKit vs Make vs Zapier
Compare OpenAI AgentKit, Make, and Zapier for AI workflow automation. Discover which platform best fits your business needs with our comprehensive feature, pricing, and capability analysis.
Key Takeaways
AgentKit Integrations: Dozens (MCP connectors)
Make Integrations: 2,500+ apps with 30K+ actions
Zapier Integrations: Largest app library
Starting Price: $9-30/mo (operations-based)
Executive Summary
The workflow automation landscape underwent a seismic shift in 2025 with OpenAI's October 6 launch of AgentKit, challenging established players Zapier, Make, and n8n. While traditional automation platforms excel at task-based trigger-action workflows, AgentKit introduces a fundamentally different paradigm: cognitive orchestration with reasoning capabilities.
Conversational agents with reasoning, rapid prototyping, customer-facing AI
7,000+ apps, quick setup, business automation, extensive ecosystem
$9/mo entry, operations-based pricing, visual builder, growing AI capabilities
Here's the critical insight: these platforms rarely compete head-to-head. AgentKit scores 14.5 out of 28 capabilities (52%) compared to Zapier's 7 out of 28 (25%) in agentic features, yet Zapier dominates in integration breadth. The good news? They work together—Zapier's MCP hooks integrate seamlessly with AgentKit's reasoning layer.
OpenAI AgentKit: AI-First Agentic Automation
Launched on October 6, 2025, OpenAI AgentKit represents a new category: agentic automation platforms built around reasoning rather than rules. According to Sam Altman, AgentKit delivers "everything you need to build, deploy, and optimize agent workflows with way less friction."
Core Components
- Visual canvas for multi-agent workflows (no code required)
- If-else logic, loops, conditional branching support
- Predefined templates: customer service, data enrichment, Q&A
- Versioning and preview runs for quick iteration
- Embeddable chat interface with 21 pre-built widgets
- Handles streaming responses, threading, theming
- Best-in-class UI for customer-facing agents
- Customizable to match brand identity
- Curated, compliance-friendly integrations via MCP
- Pre-built: Dropbox, Google Drive, SharePoint, MS Teams
- Consolidated data sources across workspaces
- Third-party MCP support for custom integrations
- Datasets for rapid agent eval building from scratch
- Trace grading for end-to-end workflow assessments
- Automated prompt optimization based on annotations
- Third-party model support (evaluate non-OpenAI models)
Real-World Performance
AgentKit introduces cognitive orchestration—the ability to plan, adapt, and reason across workflows rather than follow fixed paths. Unlike Zapier's linear trigger-action model, AgentKit handles multi-step dependencies, contextual understanding, and non-linear decision paths.
Strengths & Limitations
- • Rapid prototyping with integrated evaluation tools
- • Best-in-class conversational UI (ChatKit with 21 widgets)
- • Reasoning capabilities for contextual decision-making
- • Built-in guardrails and safety layers
- • Strong ecosystem within OpenAI's platform
- • Limited integration breadth (dozens vs thousands)
- • Currently in beta with unproven enterprise scale
- • Lacks source attribution (debugging complex chains difficult)
- • No self-updating knowledge bases
- • Primarily suited for conversational AI use cases
Make: Visual Workflow Automation with AI
Make (formerly Integromat) is a mature low-code automation platform that launched AI Agents in April 2025, positioning itself as a bridge between traditional workflow automation and the new agentic era. With 2,500+ app integrations and 30,000+ actions, Make excels at complex multi-step workflows.
Core Features
- Drag-and-drop interface for complex automation scenarios
- Routers, filters, iterators for conditional logic
- Built-in error handling and scheduling
- 15-minute minimum interval on free plan, minute-level on paid
- Goal-oriented automation with natural language understanding
- Choose your own LLM (OpenAI, Claude, or others)
- Reusable agents across multiple workflows with global prompts
- Real-time intelligence for adaptive decision making
Integration Ecosystem
Native connections to OpenAI, Anthropic, Google AI, Midjourney, ElevenLabs, and more—no external services required for many AI features
GDPR and SOC 2 Type II compliance, encryption, single sign-on (SSO) available on enterprise plans
Live map of every agent, app, and workflow with real-time analytics for bottleneck identification and performance optimization
Pricing Structure
| Plan | Price | Operations | Key Features |
|---|---|---|---|
| Free | $0/month | 1,000/month | Basic workflow builder, 2,000+ apps, 15-min minimum interval |
| Core | $9/month | 10,000/month | Unlimited scenarios, minute-level scheduling, Make API access |
| Pro | $16/month | 10,000/month | Priority execution, custom variables, full-text log search |
| Teams | $29/month | 10,000/month | Team permissions, scenario templates, multi-user |
| Enterprise | Custom | Custom | Enhanced security, always-on support, dedicated account manager |
Strengths & Limitations
- • Lowest starting price at $9/month (operations-based)
- • Strong visual workflow builder with error handling
- • 400+ AI app integrations without external services
- • Built-in GDPR and SOC 2 Type II compliance
- • AI Agents bridge traditional and agentic automation
- • AI Agents still in beta (not production-ready)
- • Steeper learning curve than Zapier for beginners
- • Auto-purchase of operations can cause unexpected costs
- • Fewer integrations than Zapier (2,500 vs 7,000+)
- • 15-minute minimum interval on free plan limits use cases
Zapier: Integration Powerhouse with AI Agents
Zapier pioneered no-code automation and remains the market leader with 7,000+ app integrations—more than any competitor. After rebranding Zapier Central to "Zapier Agents" in January 2025, the platform now positions itself as "the central nervous system for AI in the enterprise."
Core Features
- Linear trigger-action workflows ("when X, do Y")
- Multi-step Zaps on Professional plan and above
- 7,000+ app connections with pre-built templates
- Tables and Interfaces included free on all paid plans
- AI teammates that work independently across tech stack
- Conversational setup (no programming required)
- Access to 12 key business tools and company knowledge
- 50,000+ teams already using (rebranded from Central)
- Choose your own model: GPT, Anthropic, Gemini, Azure OpenAI
- Bring your own API key or use select models free
- AI-assisted prompt building with versions and testing
- Smart output fields that automatically format AI results
- Copilot: AI assistant for building workflows (announced Sept 2025)
- Enhanced enterprise governance tools for compliance
- 450+ AI integrations (30+ new in September 2025)
- Human in the Loop: smarter workflow control (2025 feature)
Pricing Structure
| Plan | Price | Tasks/Month | Key Features |
|---|---|---|---|
| Free | $0/month | 100 tasks | Two-step Zaps, AI power-ups, unlimited connections |
| Professional | $19.99/month | 750 tasks | Multi-step Zaps, premium apps, AI agents access |
| Team | $103.50/month | 2,000 tasks | Unlimited users, shared workspaces, premier support |
| Enterprise | Custom | Custom | Advanced admin, SSO, custom data retention, SLA |
Strengths & Limitations
- • Largest integration ecosystem: 7,000+ apps (unmatched)
- • Intuitive setup—easiest platform for beginners
- • 50,000+ teams using AI agents (proven adoption)
- • Tables & Interfaces free on all paid plans (2025)
- • Strong enterprise governance and compliance tools
- • Expensive at scale (pricing increases with task volume)
- • Limited support on lower tiers (critical for businesses)
- • Agents are independent executors, not multi-agent orchestration
- • Task-based pricing less predictable than operations
- • Lacks reasoning layers needed for complex AI workflows
Feature Comparison Matrix
Here's a comprehensive side-by-side comparison of OpenAI AgentKit, Make, and Zapier across key dimensions that matter for business automation:
| Feature | AgentKit | Make | Zapier |
|---|---|---|---|
| Integrations | Dozens (MCP connectors) | 2,500+ apps, 30K+ actions | 7,000+ apps (largest) |
| AI Capabilities | Cognitive orchestration, reasoning, multi-agent workflows | AI Agents (beta), goal-oriented, choose LLM | AI Agents (open beta), independent executors |
| Agentic Score | 14.5/28 (52%) | ~10/28 (36% estimated) | 7/28 (25%) |
| Pricing Model | OpenAI API pricing (pay-per-use) | Operations-based ($9-29/mo) | Task-based ($19.99-103.50/mo) |
| Visual Builder | Agent canvas (no-code, "Canva for agents") | Advanced visual builder with routers/filters | Simple workflow builder, easy for beginners |
| Conversational UI | ChatKit (21 widgets, best-in-class) | None (workflow-focused) | Agents conversational, Zaps workflow-based |
| Evaluation Tools | Built-in: datasets, trace grading, prompt optimization | Make Grid monitoring, real-time analytics | Basic task history and error logs |
| Learning Curve | Medium (AI-first thinking required) | Steep (powerful but complex) | Easy (most beginner-friendly) |
| Best For | Conversational AI, customer-facing agents, rapid prototyping | Complex workflows, cost-conscious, AI + automation hybrid | Quick integrations, business automation, largest ecosystem |
| Enterprise Ready | Beta (unproven at scale) | Yes (GDPR, SOC 2, SSO available) | Yes (advanced governance, SLAs) |
| Launch Date | October 6, 2025 | AI Agents: April 2025 (platform mature) | Agents rebrand: January 2025 (platform mature) |
| Free Tier | OpenAI API free tier applies | 1,000 ops/mo, 15-min intervals | 100 tasks/mo, two-step Zaps |
Pricing Analysis & Cost Calculator
Understanding the true cost of automation requires looking beyond monthly subscription fees to consider usage patterns, hidden costs, and scalability:
Monthly Cost Scenarios
Hidden Costs & Considerations
- API Costs: Included in standard OpenAI API pricing (GPT-4: $0.01/1K input tokens, $0.03/1K output)
- Connector Registry: Some third-party MCPs may have separate fees
- No Task Limits: Pure consumption-based (can be cost-effective or expensive)
- Beta Pricing: Current pricing may change after general availability
- Auto-Purchase: 10K operation blocks at 30% markup if limit exceeded
- Operations Counting: Each action/module in scenario counts (can add up quickly)
- AI Model Costs: If using external LLMs, separate API costs apply
- Best Value: Predictable costs if you disable auto-purchase
- Task Inflation: Each action counts as 1 task (multi-step Zaps consume rapidly)
- Premium Apps: Some integrations require Professional plan minimum
- AI Agent Costs: Unclear if separate pricing applies (still in beta)
- Scaling Costs: Many users report $200-500/mo at medium scale
- AgentKit: Requires AI/development knowledge ($5-10K training investment)
- Make: Steeper learning curve, may need consultant ($2-5K setup)
- Zapier: Easiest onboarding, minimal training needed ($500-1K)
- Maintenance: All platforms require ongoing monitoring and updates
1. Start Small: Begin with free tiers to understand usage patterns before committing
2. Disable Auto-Purchase (Make): Prevent unexpected overage charges
3. Consolidate Workflows: Combine similar automations to reduce task/operation counts
4. Monitor Usage: Set up alerts when approaching tier limits
5. Hybrid Approach: Use AgentKit for AI reasoning, Make/Zapier for integrations
AI Capabilities & Reasoning
The defining difference between these platforms lies in their approach to AI. Traditional automation follows fixed rules, while AI-powered automation adapts, reasons, and makes decisions based on context.
Automation vs. Intelligent Reasoning
- • Plans: Determines optimal workflow path based on context
- • Decides: Makes autonomous choices using reasoning
- • Adapts: Adjusts behavior dynamically based on results
- • Multi-Agent: Coordinates multiple specialized agents
- • Contextual: Maintains conversation history and state
- • Define Goals: Natural language understanding of objectives
- • Dynamic Adjustment: Modifies workflows based on conditions
- • Choose LLM: OpenAI-compatible models, Claude, custom
- • Reusable Agents: Global system prompts with scenario customization
- • Real-Time Intelligence: Responds to changing conditions
- • Trigger-Action: Linear "when X, do Y" model
- • AI Agents: Independent executors, not orchestrated
- • Model Choice: GPT, Anthropic, Gemini, Azure OpenAI
- • AI Power-Ups: Add AI steps within traditional Zaps
- • Limited Reasoning: Lacks governance and reasoning layers
When Reasoning Matters vs. Rules Suffice
| Scenario Type | Best Platform | Why |
|---|---|---|
| "When form submitted, send email" | Zapier | Simple trigger-action, no reasoning needed |
| "Route leads to sales rep based on 10 criteria" | Make | Complex conditional logic, routers/filters excel |
| "Answer customer questions using knowledge base" | AgentKit | Requires NLU, contextual reasoning, conversation memory |
| "Sync 5 apps when inventory changes" | Zapier | Fixed workflow, extensive integrations needed |
| "Analyze support tickets, prioritize, assign" | AgentKit | Needs sentiment analysis, reasoning, adaptive routing |
| "Process images, extract data, update CRM" | Make | Multi-step pipeline, AI + automation hybrid |
Use Cases & Implementation Examples
Here are real-world scenarios showing when each platform shines, with concrete implementation examples:
OpenAI AgentKit Use Cases
Build a conversational AI that answers customer questions, accesses knowledge bases, escalates complex issues, and maintains conversation context.
- • Agent Builder: Create Q&A agent with conditional logic
- • ChatKit: Deploy customer-facing chat interface
- • Connector Registry: Link to Zendesk, Intercom, knowledge base
- • Evaluation: Test with datasets, optimize with trace grading
Automate vendor selection, quote comparison, approval routing based on complex business rules and budget constraints (Ramp's real use case).
- • Built in hours instead of months
- • 70% reduction in iteration cycles
- • Live in 2 sprints vs. 2 quarters (traditional development)
Make Use Cases
Capture leads from forms, enrich with Clearbit, score using AI, route to appropriate sales rep, update CRM—all automatically.
- • Trigger: Webhook from Typeform/Google Forms
- • Module 1: Clearbit enrichment (company data)
- • Module 2: AI Agent scores lead quality (1-10)
- • Router: High scores → Sr. Rep, Low scores → Jr. Rep
- • Module 3: Create contact in HubSpot/Salesforce
- • Module 4: Send Slack notification to assigned rep
Monitor brand mentions across Twitter, Reddit, LinkedIn; analyze sentiment with AI; auto-respond to common questions; escalate negative sentiment.
- • Twitter/Reddit/LinkedIn APIs check for brand mentions every 5 mins
- • AI Agent analyzes sentiment (positive/neutral/negative)
- • Filter: Positive → Like/RT, Neutral → Track, Negative → Escalate
- • Auto-respond to FAQ patterns using AI-generated replies
- • Log all mentions to Airtable/Google Sheets for reporting
Zapier Use Cases
Keep customer data synchronized across Salesforce, Mailchimp, Google Sheets, Slack—automatically updating all platforms when any one changes.
- • Trigger: New/updated contact in Salesforce (or any of 5 apps)
- • Action 1: Update/create contact in Mailchimp
- • Action 2: Add/update row in Google Sheets
- • Action 3: Update HubSpot contact (if different CRM)
- • Action 4: Post update notification to Slack
- • Filter: Only sync if email exists (prevent incomplete records)
When Shopify order placed: create invoice in QuickBooks, send to fulfillment center via email/API, notify customer via SMS, update inventory in warehouse system.
- • Trigger: New paid order in Shopify
- • Action 1: Create invoice in QuickBooks Online
- • Action 2: Send order details to ShipStation (or fulfillment API)
- • Action 3: Send SMS via Twilio ("Order confirmed!")
- • Action 4: Update inventory in custom warehouse database
- • Action 5: Add customer to "Recent Buyers" segment in Klaviyo
Hybrid Approach: Combining Platforms
Use Case: Intelligent customer onboarding
Decision Guide: Which Platform to Choose
Making the right choice depends on your specific use case, technical capabilities, budget, and long-term automation strategy. Here's a decision framework:
- You need conversational AI for customer-facing applications
- Workflows require reasoning, contextual understanding, or adaptive decision-making
- You're prototyping AI agents and need rapid iteration
- Integration needs are limited and covered by MCP connectors
- You have technical resources comfortable with AI/API development
- You're already in the OpenAI ecosystem (ChatGPT Enterprise, API)
- Budget is a primary concern (lowest starting price at $9/mo)
- You need complex multi-step workflows with conditional logic
- Want to experiment with AI Agents while maintaining traditional automation
- Visual workflow builder is important for your team
- Need 400+ AI app integrations without external services
- GDPR/SOC 2 compliance is required (built-in)
- You need the largest integration ecosystem (7,000+ apps)
- Ease of use is critical—team has limited technical expertise
- Simple trigger-action workflows are your primary need
- Quick setup and fast time-to-value are essential
- You want to experiment with AI Agents alongside proven automation
- Enterprise governance and SLAs are required (Enterprise plan)
Implementation Roadmap
- • Start with Zapier or Make for simple, high-ROI workflows
- • Focus on quick wins: lead capture, email automation, data sync
- • Build team familiarity with automation concepts
- • Establish usage patterns and cost baselines
- • Layer in AI capabilities (Make AI Agents or Zapier AI)
- • Build more complex multi-step workflows
- • Evaluate cost vs. value—optimize or switch platforms
- • Identify use cases requiring reasoning (AgentKit candidates)
- • Introduce AgentKit for customer-facing conversational AI
- • Maintain Make/Zapier for integration-heavy workflows
- • Implement hybrid approach: AgentKit reasoning + Make/Zapier integrations
- • Continuous monitoring, optimization, and cost management
Conclusion
The October 2025 launch of OpenAI AgentKit marks a pivotal moment in workflow automation, introducing cognitive orchestration that fundamentally differs from the trigger-action model perfected by Zapier and Make. Rather than competing directly, these platforms serve complementary roles in the modern automation stack.
AgentKit excels at conversational AI and reasoning-driven workflows, scoring 52% on agentic capabilities compared to Zapier's 25%. However, Zapier's 7,000+ app ecosystem and Make's operations-based pricing model provide unmatched integration breadth and cost efficiency for traditional automation. The winning strategy isn't choosing one platform—it's understanding when to deploy each.
For businesses starting their automation journey, begin with Zapier or Make for quick wins and proven workflows. As your needs evolve, layer in AI capabilities through Make's AI Agents (in beta) or Zapier's AI features. When you encounter use cases requiring reasoning, contextual understanding, or customer-facing conversational interfaces, introduce AgentKit as a specialized tool while maintaining your existing automation infrastructure.
The platforms integrate seamlessly through MCP connectors and webhooks, enabling hybrid architectures that combine AgentKit's cognitive capabilities with Make/Zapier's integration ecosystem. This complementary approach delivers the best of both worlds: intelligent reasoning where needed, reliable automation everywhere else.
Need Help Choosing the Right Automation Platform?
Our team specializes in CRM & Automation and AI & Digital Transformation. We'll help you select, implement, and optimize the right platform for your business needs—whether it's AgentKit, Make, Zapier, or a hybrid approach.
Frequently Asked Questions
Related Articles
Explore more guides on AI automation, workflow optimization, and digital transformation