ChatterKB vs Make: What’s the difference?

Feature	ChatterKB	Make
Pricing model	Flat subscription, unlimited workflows	Pay per operation (module run)
Workflow builder	Plain-language prompts	Scenario modules (visual)
Knowledge management	Built-in KB grows over time	None
AI approach	Memory-first, context-aware	Minimal / external AI calls
Integrations	Internal tools + 5,000+ via Zapier MCP	≈1,300 native modules

About ChatterKB

ChatterKB transforms conversations into intelligent, self-improving workflows. By combining natural-language automation with a persistent knowledge graph, it lets anyone—technical or not—deploy powerful processes in minutes.

Highlights:

Natural-language workflow creation & debugging
Institutional memory that compounds with every run
5,000+ integrations via Zapier MCP plus built-in internal tools
Predictable subscription pricing with unlimited executions

About Make

Make (formerly Integromat) is a visual integration platform where users connect “modules” on a canvas to build scenarios that move data between apps. Pricing is based on the number of operations each module performs, and advanced features often require larger plans.

Key points:

Drag-and-drop scenario builder with custom expression language
Cloud-only offering (no self-host)
Charges for every operation, which scales costs quickly for data-heavy flows
Limited native AI functionality—requires external APIs and custom logic

Pricing Comparison

ChatterKB

- Free Plan: $0 - 1 KB, 20 credits/mo
- Lite Plan: $12/mo - 1 KB, 100 credits/mo
- Plus Plan: $25/mo - 3 KBs, 350 credits/mo
- Pro Plan: $50/mo - 5 KBs, 800 credits/mo
- Business Plan: Starting at $250/month - Custom KBs, credits, and users
- 20% discount on all plans with annual billing
- All plans include unlimited knowledge storage
- No hidden fees or premium upsells - all internal tools included
- 30-day money-back guarantee on annual plans

Make

- Free plan with 1,000 operations / month
- Core: $9 / month → 10,000 ops
- Pro: $16 / month → 10,000 ops + advanced features
- Teams: $29 / user / month (10,000 ops per seat)
- Additional operations billed separately; every module action counts as an op

Automate knowledge-driven workflows in minutes—not hours.

Try ChatterKB Free

Enterprise

ChatterKB

Data Sovereignty Options: Control where your data lives with flexible deployment options that allow you to keep sensitive information in your own databases and servers while leveraging ChatterKB’s processing capabilities
Data Residency Controls: Specify geographic regions for data storage and processing to meet compliance requirements
Tenant Isolation: Multi-tenant architecture with complete data separation between business units or client accounts
Audit Logging: Comprehensive activity tracking for compliance and security monitoring, with exportable logs for SIEM integration
API-First Architecture: Well-documented APIs for seamless integration with existing enterprise systems and custom applications
Batch Processing: Handle large-scale data operations efficiently for enterprise data volumes
Role-Based Access Controls: Granular permission settings for system features based on organizational roles
Team Collaboration: Built-in collaboration features for cross-functional teams
Enterprise Billing: Flexible enterprise billing options including invoicing
Support: Dedicated customer success with responsive SLAs

Make

Cloud-Only Hosting: All data lives in Make’s AWS environment
Basic Roles: Multiple account roles but limited granular permissions
No Data Residency Guarantees: Region selection not available on standard plans
No Persistent Knowledge Layer: Requires external KM systems

Ease of Use

ChatterKB

ChatterKB eliminates the need for flowcharts entirely:

Write, Don’t Diagram: Simply describe the outcome—ChatterKB handles the logic.
Zero Learning Curve: New hires build workflows on day one, no training required.
Conversational Debugging: If a workflow fails, just explain what happened; the system self-corrects.
Contextual Suggestions: Recommendations improve as ChatterKB learns your terminology and historical decisions.

Make

Module Canvas: Users must connect each module and configure data mapping.
Expression Language: Custom syntax needed for transforms and filters.
Training Required: Steeper learning curve for non-technical teams.

AI Automation

ChatterKB

ChatterKB embeds AI into every layer via its memory-first architecture:

Persistent Knowledge: Each run enriches an organization-wide knowledge graph.
Natural Language Creation: Entire workflows are generated, tested, and refined through conversation.
Adaptive Execution: Flows self-adjust based on real-time context and newly ingested information.
Unified Experience: No switching between AI tools and workflow designer—the interface is conversational from start to finish.

Make

No Native AI Nodes: Must call external APIs or webhook services.
Stateless Runs: Scenarios don’t retain institutional memory.
Manual Error Handling for AI failures.

Workflow Reliability

ChatterKB

ChatterKB’s memory-centric architecture delivers superior reliability:

Self-Correcting Workflows: Workflows automatically adapt to new information and recover from errors
Reasoner-Based Architecture: Specialized components handle different tasks with focused context
Intelligent Replanning: When unexpected situations arise, workflows can replan steps without human intervention
Comprehensive Monitoring: Built-in monitoring ensures workflows complete successfully even during infrastructure changes

Make

Fixed Paths: Scenarios follow predetermined branches.
Manual Error Handling with extra modules.
Restart Required when unexpected data appears.

Long-Term Value

ChatterKB

ChatterKB delivers increasing returns over time:

Institutional Memory: Every interaction builds your knowledge graph, making future workflows smarter
Relationship-Based Learning: The system automatically connects related concepts across conversations
Reduced Training Costs: New team members benefit from accumulated knowledge and context
Workflow Evolution: Existing workflows improve automatically as your knowledge base grows

Make

Static Scenarios require manual updates.
No Built-In Knowledge Retention.