Complete pricing guide for Make.com. Compare all plans, analyze costs, and find the perfect tier for your needs.
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Pricing sourced from Make.com · Last verified March 2026
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View Full Features →Make is more polished and user-friendly with 1,500+ integrations and better error handling. n8n has dedicated AI agent nodes and vector store operations that Make lacks. Make is cloud-only; n8n can be self-hosted. Choose Make for business teams wanting reliable AI automation; n8n for technical teams wanting AI-specific features and self-hosting.
Not natively. Make can call embedding APIs and vector store APIs via HTTP modules, but there's no built-in RAG pipeline management. For simple RAG (embed a query, search vectors, pass to LLM), you can build it manually. For production RAG with document processing and retrieval optimization, use a dedicated platform and trigger it from Make.
Each module execution counts as one operation. A scenario with 5 modules processes one item = 5 operations. If it processes 10 items in one run = 50 operations. AI module calls (OpenAI, Anthropic) count as 1 operation each. Data store operations, router operations, and filter evaluations also count. Plan your scenarios with operation efficiency in mind.
Make supports parallel execution and can process thousands of items per scenario run. However, operation-based pricing means high-volume AI workflows get expensive quickly. For high-volume processing, consider batching, caching (using data stores), and running heavy AI processing in external services triggered by Make.
AI builders and operators use Make.com to streamline their workflow.
Try Make.com Now →Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
Compare Pricing →Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
Compare Pricing →Graph-based workflow orchestration framework for building reliable, production-ready AI agents with deterministic state machines, human-in-the-loop capabilities, and comprehensive observability through LangSmith integration.
Compare Pricing →SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.
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