n8n or Relevance AI for a workflow that mixes fixed steps and AI agent decisions

Four documented criteria for building a workflow that mixes fixed steps and AI agent decisions.

One journey, one decision to make

Take a common process. A form arrives, a record is created in the CRM, an AI step researches the company and drafts a reply, a person approves the message, and every action is logged. Some steps must run the same way every time. One step needs judgment. The question is which product is the better place to build the whole thing.

n8n and Relevance AI are both filed under workflow automation and AI agents, but they start from different places. n8n starts from a workflow editor that can be hosted or self-hosted. Relevance AI starts from AI agents deployed as a team. Neither is a universal winner. The better fit depends on how much technical control you want, who will build, and how much oversight you need.

Sources: n8n.io · relevanceai.com · N8n (usedby.ai) · Relevance ai (usedby.ai)

Orchestration: where the fixed steps live

n8n describes an editor where you can write JavaScript or Python anywhere in a workflow, see inputs and outputs beside each step, re-run a single step and replay or mock data. Its pricing page lists an AI Agent node, an MCP server trigger, an MCP client and human approval for tool calls. In practice, fixed steps and the agent step sit on the same canvas.

Relevance AI presents Invent, a builder it says helps people identify what to automate and turn expertise into reliable systems. It also shows an autonomy framework from assisted copilots (L1) to agents that run on events and signals (L3) and self-improving ones (L4). How a strict sequence of fixed steps is expressed was not detailed on the pages we read, so it is worth seeing in a demo.

Sources: n8n.io · Pricing (n8n.io) · relevanceai.com

Model choice and evaluation

n8n's pricing page lists AI models without API keys from the Starter plan, light and metric-based evaluations, and LangSmith or OpenTelemetry tracing for self-hosted setups. The exact list of supported models was not in the pages we reviewed, so ask which models you can use and whether you can bring your own keys.

Relevance AI says agents use whichever model fits the job, naming OpenAI, Claude and Gemini. Its homepage shows a sample dashboard with a different model, evaluation score and cost per task. That is a product illustration, not a measured result.

Sources: n8n.io · Pricing (n8n.io) · relevanceai.com

Integrations and custom tools

n8n's plans include all integrations (nodes), custom API requests over HTTP or GraphQL, triggers from webhooks and queues, and custom nodes when self-hosted. ChatHQ's case study describes building its own interface on top of the n8n backend with custom nodes, which shows how far that flexibility goes.

Relevance AI's integrations page advertises more than 2,000 integrations, event-based triggers and off-the-shelf or custom actions, with Slack and Salesforce named. Its homepage says 1,000+ apps, so the figures differ across pages. Confirm the specific apps you need rather than relying on either number.

Sources: n8n.io · Pricing (n8n.io) · Chathq (n8n.io) · relevanceai.com · Integrations (relevanceai.com)

Hosting and oversight

n8n says you can deploy with Docker, read the source on GitHub, use a hosted version or run fully on-prem. It lists SSO with SAML and LDAP, RBAC, audit logs and log streaming to a SIEM. On the pricing page, Starter is $20 a month and Pro $50, both billed annually and hosted by n8n. Business is $800 a month, billed annually, and self-hosted only. Enterprise is by quote. n8n says it charges for full executions, not individual steps.

Relevance AI's trust page lists SOC 2 Type II and GDPR, a data processing addendum, and subprocessors including AWS and MongoDB Atlas in the US, EU and Australia. Its homepage cites audit logs, full agent tracing, human-in-the-loop approvals and data residency. Whether it can be self-hosted is not mentioned in the pages we read, and its pricing is not covered here.

Sources: n8n.io · Pricing (n8n.io) · relevanceai.com · trust.relevanceai.com

What customer workflows show

Zembl, an Australian energy consultant, used Relevance AI to build an agent that researches inbound leads, contacts them and collects bills. At first the team approved every email, feeding the agent five leads a day, then ten, then fifteen. Later they added agents for Salesforce updates, SMS after bounces and energy comparisons. Zembl reports a 30% rise in customer conversion with the same team, and average call time falling from about 22 minutes to roughly nine. These are vendor-published, self-reported results.

Bordr, a Portugal relocation service, used n8n to connect Airtable, Postmark, Paperform and Stripe. Its workflows add orders, email customers, create tasks for a law firm partner and generate power of attorney PDFs. Dropsolid used n8n to sync CRM data into Mautic and refresh a daily dashboard. These n8n cases are classic deterministic automation, not agent decisions, so they show the orchestration strength rather than AI judgment. The two sets of cases measure different things and should not be compared as a benchmark.

Sources: Zembl (relevanceai.com) · Bordr (n8n.io) · Dropsolid (n8n.io)

Which to choose, and what to ask

These are editorial recommendations, not vendor claims. If your team is technical and wants code on demand, self-hosting or tight control over each step, start with n8n. If business teams want to describe an agent role and supervise it with approvals and tracing, start with Relevance AI. If you need both, one pragmatic route is to keep deterministic steps in n8n and test whether the agent step belongs there too.

Before deciding, ask both vendors to build your journey from the first trigger to the final log. For n8n, ask for the supported model list. For Relevance AI, ask about self-hosting, pricing and how fixed steps are enforced. No company in the data we reviewed is listed as using both, so you will not find a shared reference customer.

Sources: n8n.io · relevanceai.com