n8n vs Make

Pricing

Make now uses credits as its billing unit. For most non-AI modules, one operation currently consumes one credit; some AI and advanced features can consume credits differently based on usage such as tokens. Count the modules and feature-specific credit rules in the intended scenario and verify the current pricing page.

n8n Cloud prices by workflow execution and currently includes unlimited steps within an execution. That unit differs from Make credits, so a fair comparison must model the same scenario rather than compare headline quotas. Neither model is universally cheaper.

Self-hosted n8n removes a per-execution Community license meter for permitted use but adds infrastructure and operating costs. Make has no self-hosted edition, so the decision also includes whether the team wants to own the runtime.

Features

Make's primary surface is the scenario canvas: modules connect through visible data paths, with routers, iterators, aggregators, run inspection, and error-handling controls. This favors teams that want branching and data movement represented visually.

n8n's strength is composition. The editor exposes branching, merging, looping over arrays, sub-workflows that call other workflows, and a Code node that accepts JavaScript or Python with full access to upstream data. IF, Switch, and Merge nodes give you the control flow Make builds with Routers, and the Execute Workflow node lets one workflow call another with typed inputs and outputs — a pattern Make scenarios cannot express cleanly.

Integrations

Make offers packaged apps for mainstream business SaaS, an HTTP module, and a Custom Apps framework. Coverage and module depth change over time, so verify the required triggers and actions in the current catalog.

n8n combines native and community nodes with HTTP Request and Code nodes. External modules in Code nodes require the appropriate self-hosted configuration; this is an escape hatch, not a guarantee that every missing connector is effortless.

Self-hosting

Self-hosting is the cleanest structural difference. n8n ships a Docker image, a Compose stack, and a Helm chart. SQLite covers small installs; Postgres is recommended for production; Redis enables queue mode so multiple worker containers pull jobs in parallel. The license is the Sustainable Use License — free for internal use and modification, with restrictions on running a competing hosted n8n service — invisible for the cases most teams care about.

Make has no self-hosted edition. There is no community release, no on-prem enterprise package, and no public roadmap commitment to add one. Every scenario run goes through Make's cloud, which is fine for the majority of workloads and a hard stop for teams that need data residency in a specific jurisdiction or an air-gapped environment.

n8n queue mode distributes executions across workers with Redis and a supported database configuration. Capacity depends on execution shape, concurrency, worker resources, database performance, and external APIs. The trade is owning backups, upgrades, monitoring, and incidents.

AI

n8n includes an AI Agent node, LangChain wrappers, a chat memory node, and direct integrations with the major vector stores (Pinecone, Qdrant, Supabase pgvector, Weaviate). You can build a retrieval-augmented chat flow, a tool-using agent, or a multi-step LLM chain on the same canvas you use for SaaS glue, and the model nodes accept OpenAI, Anthropic, Google, Mistral, and self-hosted providers through Ollama. Sub-workflows can be exposed to the agent as callable tools, which turns building an agent loop into a wiring job rather than an orchestration code job.

Make has shipped AI Agents, an OpenAI module, an Anthropic module, and a growing set of generative AI helpers (text, image, transcription). For a scenario that calls a model in one or two modules — summarise an email, classify a ticket, draft a reply — the experience is fast and the result feeds the rest of the scenario cleanly. For a tool-using agent that loops until it succeeds, a RAG flow with a private vector store, or a chain that branches on classifier output, the building blocks are thinner and the visual model is less suited to the recursion an agent loop implies.

A useful filter: n8n exposes agent, memory, and tool nodes inside its graph; Make exposes AI modules and agents within its managed scenario model. Choose from the control primitives and integrations required, not an assumed delivery-speed advantage.

Migration

Neither product imports the other's format. Migration is a manual rebuild: document the source, recreate and test representative inputs, prevent duplicate effects during any parallel period, and cut over against defined acceptance criteria.

Routers, iterators, and aggregators have conceptual equivalents in n8n conditions, loops, merges, and code. Workflows using product-specific code, sub-workflows, AI nodes, or connector semantics may require redesign. Migration time depends on those boundaries rather than module count.

Who should choose n8n

n8n fits teams with technical ownership, workflows that touch internal systems alongside SaaS, and a preference for the option to self-host. Execution-based billing can differ materially from Make credits for some workflow shapes, but the cost result requires a current workload model. Code and AI nodes add flexibility; self-hosting adds operational responsibility.

Who should choose Make

Make fits teams that want a managed visual canvas, run mostly business-SaaS automation, and do not want to operate infrastructure. Its credit model must be evaluated against the modules and AI features the scenario uses. The operational boundary is clear: Make owns the runtime, while the customer owns scenario design and usage management.