CrewAI vs n8n (2026): Pricing, Features & Verdict

Why choose CrewAI

• Higher Analytics score (7/10 vs 5/10)
• Higher Ease of Use score (6/10 vs 5/10)

Why choose n8n

• Higher Cost score (8/10 vs 7/10)
• Higher Scalability score (7/10 vs 6/10)
• Better fit for small business (6/10 vs 4/10)

CrewAI

Pros

• The core framework is MIT-licensed and free to self-host, so teams can prototype multi-agent systems without licensing costs.
• The role-based Crews model and the event-driven Flows model give developers a clear structure, with sequential and hierarchical task processes.
• It works with most LLMs through LiteLLM or native providers, and it supports MCP servers, A2A, and more than 40 built-in tools.
• AMP includes OAuth-based connectors for Salesforce, HubSpot, Slack, and Gmail, plus REST API and webhook triggers.
• Enterprise buyers can deploy in their own VPC or on their own infrastructure, with SSO, RBAC, and PII redaction.

Cons

• No public price exists between the free 50-execution Basic tier and a custom Enterprise quote.
• Reviewers report heavy token use, because every agent step is an LLM call and multi-agent crews add to the cost.
• Python knowledge is effectively required, and reviewers describe the learning curve as steep for newcomers.
• Independent reviews say long-running pause and resume, horizontal scaling, and error recovery need custom work.
• Smaller open-source models, around 7B parameters, reportedly struggle with CrewAI's function calling.
• Compliance claims (SOC 2 type, HIPAA) vary between third-party sources and should be confirmed directly with the vendor.

n8n

Pros

• Billing is per complete workflow run rather than per step, so long multi-step AI workflows cost the same as simple ones, and every plan includes unlimited users.
• The free Community Edition and the self-hosted Business plan let teams keep data and AI workflows on their own infrastructure.
• AI building blocks include agent nodes with memory and tools, vector-store RAG, an embeddable chat trigger, MCP connections, evaluations and human approval steps.
• Supports multiple LLM providers, including OpenAI, Anthropic and Google, in the same workflow, plus JavaScript code nodes and an HTTP node for APIs without a native connector.
• Reviews are strong, with an average of about 4.7 out of 5 on G2 and 4.6 on Capterra.

Cons

• The learning curve is the most cited complaint on G2, with about 41 reviews mentioning it. Non-developers struggle with JSON mapping and debugging.
• The Business plan with SSO and Git costs about EUR 800 a month and is self-hosted only, a big jump from Pro at EUR 60.
• Self-hosters handle updates, security patches and scaling themselves. Queue mode needs Redis and separate workers.
• The Sustainable Use License limits embedding n8n in a commercial SaaS product without a paid agreement.
• Starter and Pro include only forum support. Dedicated, SLA-backed support requires Enterprise.