Relevance AI vs n8n: Agents, Workflows, Cost, and Control

Relevance AI and n8n can both automate work with AI, but they start from different control models. Relevance AI starts with agents and multi-agent Workforces: you define roles, tools, knowledge, handoffs, and guardrails. n8n starts with an explicit workflow graph: triggers, branches, transformations, credentials, retries, and actions are wired as nodes, with AI agents available inside that graph.

Neither is universally better. Choose Relevance AI when business operators need to assemble and supervise agent teams quickly. Choose n8n when a technical owner needs deterministic orchestration, broad integration control, and a cloud or self-hosted deployment choice. Some systems use both, but only when the extra boundary has a clear owner.

The current comparison in one table

This corrects a common shortcut: n8n is not simply “open source and unlimited.” Its documentation calls it fair-code licensed. Community Edition can be self-hosted without Cloud execution metering, but infrastructure, upgrades, backups, observability, security, and external APIs remain your responsibility.

Relevance AI: agent teams as the product surface

Relevance AI's core object is an Agent equipped with instructions, tools, and knowledge. Several agents can be connected into a Workforce on a visual canvas. Handoffs can be AI-decided, fixed, or conditional, which allows a research agent to pass work to a writer and then a reviewer without expressing every decision as a low-level integration node.

The platform also includes a no-code tool builder, app/API integrations, knowledge sources, triggers, schedules, escalation paths, approvals, run history, and evaluation features. This is broader than a prompt wrapper: the buyer is paying for a managed agent runtime and an operator-facing control plane.

The tradeoff is abstraction. Agent-selected paths are useful when inputs vary, but they are harder to reason about than a fixed graph. For money movement, record deletion, regulated decisions, or high-volume customer communication, narrow tools and explicit approvals matter more than an agent's flexibility.

Current pricing structure

Relevance AI's live pricing page currently lists:

• Free: 200 Actions per month, one Workforce, one build user and project;
• Pro: $29 monthly, or $19 per month when billed annually, with 2,500 monthly Actions on the monthly plan;
• Team: $349 monthly, or $234 per month when billed annually, with plan allowances and collaboration features;
• Enterprise: custom pricing and controls.

Vendor Credits cover model usage and are separate from Actions. Extra Actions and credits can be purchased. Since plans and allowances change, model a pilot from exported run data and link procurement to the live pricing page.

One Action is a unit of agent work and may represent a simple email or a multi-step tool workflow. That makes it more outcome-shaped than per-step billing, but it does not remove model charges or fan-out: a Workforce can call sub-agents and tools, consuming both Actions and credits.

n8n: orchestration first, agents inside the graph

n8n is a workflow automation platform. It connects triggers to application nodes, HTTP requests, data mapping, code, conditions, sub-workflows, error paths, and queues. AI Agent nodes can call tools, use retrieval or memory patterns, and pause for human approval before selected tool calls. Evaluations and tracing support vary by plan and deployment.

This architecture is strongest when the expected path must be visible: receive a webhook, validate fields, enrich a record, call a model, require approval, update the CRM, then notify an owner. AI can handle an ambiguous step without owning the whole control flow.

The flexibility carries operational risk. Credentials, webhooks, code nodes, community nodes, file-system access, and arbitrary API calls enlarge the attack surface. n8n provides a security-audit command, but self-hosting means your team must act on it, patch the instance, protect the database and encryption key, and design backups.

Current pricing structure

n8n Cloud and paid self-hosted plans meter completed workflow executions, not each node step. Limits also cover concurrency, history, storage, projects, environments, and governance features. The exact plan prices and allowances vary by billing region and term, so use the live pricing table.

Community Edition is a standard self-hosted version available from GitHub. It does not give you every paid collaboration or governance feature, nor does “no Cloud execution bill” make production free. Add server, database, backups, monitoring, incident response, upgrades, and third-party model/API charges.

Which is cheaper?

There is no honest answer without a workload. Their units do not map one-to-one.

For Relevance AI:

total = subscription + Action top-ups + Vendor Credits/BYOL model cost + integrations + review labor

For n8n Cloud:

total = plan/executions + model/API cost + overages + build and review labor

For self-hosted n8n:

total = license tier if any + infrastructure + model/API cost + engineering + security + support

An agent task that calls three specialists is not directly comparable to one n8n execution with 20 nodes. Price the same business outcome—such as one approved lead-enrichment record—not platform units in isolation.

Choose by task and team

Choose Relevance AI when

• operators rather than developers own the automation;
• variable inputs benefit from role-based agents and adaptive handoffs;
• managed hosting and a fast agent template path matter more than infrastructure control;
• call, meeting, or Workforce features in the selected plan match the use case;
• the team can constrain tools and inspect escalations, histories, and evaluations.

Choose n8n when

• a technical owner needs explicit branching, transformations, retries, and error paths;
• the workflow touches many APIs or needs custom nodes/code;
• self-hosting, network placement, or infrastructure integration is a requirement;
• the AI step should remain one bounded component inside a deterministic process;
• the team can operate the instance or prefers n8n Cloud to avoid that work.

Choose neither when

• the work is primarily files, documents, and one-off delegation rather than repeatable event pipelines;
• no one owns monitoring and incident response;
• an existing SaaS automation already completes the task safely;
• the process is still changing so quickly that automating it would freeze bad assumptions.

For the wider distinction, see workflow builders versus AI workspaces and whether to build or buy agentic systems.

Governance and deployment questions that decide the result

Data location is not the whole privacy answer

Relevance AI is vendor-hosted and advertises SOC 2 Type II and GDPR compliance. n8n can be self-hosted. Neither fact alone settles privacy. Both may send selected data to model providers and connected SaaS applications. Map every processor, credential, log, knowledge store, and backup.

Self-hosting gives control and duties

n8n self-hosting lets a team choose region, network, database, and operational controls. It also transfers patching, encryption-key protection, database availability, queue operations, log retention, disaster recovery, and node review to that team. Community nodes and code execution deserve particular scrutiny.

Agent autonomy needs bounded tools

In either platform, use read-only credentials first. Separate retrieval from mutation. Require approval for sending, deleting, publishing, purchasing, or changing customer records. Define idempotency keys and retry behavior so a timeout does not duplicate an external action.

Collaboration features are plan-specific

Do not assume self-hosted Community Edition includes SSO, Git environments, advanced role controls, log streaming, or enterprise support. Do not assume a Relevance AI Free or Pro workspace includes Team/Enterprise evaluations, end-user access, or governance. Verify the precise plan before architecture approval.

A reproducible two-week pilot

Test one workflow on both platforms, not two showcase templates. A useful candidate is inbound lead qualification with an approval gate.

1. Freeze the input and outcome. Use 50 historical leads with a known disposition. Output one structured recommendation, evidence, and a draft follow-up.
2. Set the same tools. CRM read access, approved web research, and a sandbox destination. No production writes in week one.
3. Define success before building. Accuracy, unsupported claims, completion rate, median/95th-percentile time, review minutes, and cost per approved lead.
4. Build the natural way. In Relevance AI, use role agents and Workforce handoffs. In n8n, use explicit nodes and isolate the AI Agent to ambiguous decisions.
5. Inject failures. Missing CRM fields, expired credentials, rate limits, duplicate webhooks, model timeouts, and malicious text in a scraped page.
6. Add approval. A person must approve the email and CRM write. Confirm that rejection, editing, and timeout states are recoverable.
7. Measure maintenance. Change the scoring rule and replace one integration. Record how long the update and regression test take.
8. Calculate full cost. Include platform units, model/API charges, infrastructure, build time, review, and failed runs.

Pick the platform whose failures are understandable and recoverable by the team that will own it—not the platform that produces the prettiest first demo.

Migration and lock-in risks

Relevance AI logic lives in agent instructions, tools, knowledge, Workforce connections, and platform-specific histories. n8n logic lives in exported workflow JSON, credentials, nodes, expressions, code, and deployment configuration. Neither export is a portable business process by itself.

Keep an external process specification containing schemas, decision rules, prompts, evaluation cases, approvals, integration contracts, and rollback steps. Store data in systems you control where practical. Wrap critical third-party APIs behind stable interfaces. Test model replacement separately from workflow migration.

If combining the tools, define one as the system of orchestration. A reasonable pattern is n8n owning triggers, validation, retries, and writes while Relevance AI handles a bounded research or classification task. Avoid a circular design where both retry and delegate to each other.

Verdict

Relevance AI is the more direct choice for a business team that wants managed, role-based agents and Workforces. n8n is the stronger choice for a technical team that wants explicit automation logic, extensibility, and deployment control. Both can perform agentic and deterministic work; the difference is which abstraction is primary and who carries the operational burden.

Do not decide from integration counts or a “no-code versus developer” label. Run the same workflow, price the same approved outcome, test the same failures, and verify the exact plan features. The right platform is the one your team can safely understand, operate, and change six months after launch.

Official sources

• Relevance AI pricing
• Relevance AI introduction and core concepts
• Relevance AI Workforces
• n8n pricing
• n8n documentation and fair-code description
• n8n security audit