n8n gives you the canvas.
We make the workflows actually run in production.

We work with what you have. The audit phase is specifically designed to evaluate your existing workflows before we touch anything. Some will be worth refactoring, some will need to be rebuilt, and some will be fine as-is. We give you an honest assessment before any build work begins, and we do not charge you to rebuild things that do not need it.

It depends on your data residency requirements, your internal DevOps capacity, and your budget. n8n Cloud removes the infrastructure burden but adds a per-execution cost at scale and limits some configuration options. Self-hosted gives you full control and is often cheaper at volume, but you own uptime, backups, and upgrades. We have built on both and will give you a straight recommendation based on your actual situation.

n8n's main practical advantages are the open-source core, the ability to self-host, no per-task pricing at volume, and the ability to write custom nodes in TypeScript. The trade-off is that it requires more technical investment to run well. Zapier and Make are faster to start but get expensive and hit capability ceilings. n8n is the right call when you have technical resources and want to own the infrastructure long-term. We are not the right fit if you want a Zapier-style quick fix with no ongoing ownership - go set that up yourself this afternoon. We are the right fit when you want the workflow engine to actually hold up in production and you are willing to invest in owning it.

Yes, but only if the instance is configured correctly. The default single-process mode is not built for high concurrency. You need queue mode enabled with a Redis or PostgreSQL backend, proper worker scaling, and execution pruning configured. We have seen self-hosted instances grind to a halt because nobody set up queue mode. This is a configuration problem, not an n8n limitation.

Yes, with the right guardrails. n8n's LangChain nodes make it straightforward to build agent loops, tool-calling workflows, and memory-backed chains. The risk is treating LLM output as trusted data and writing it directly to your CRM or database. We build validation and human-review steps into every AI workflow that touches a system of record, which is what separates a demo from something you can run in production.

An audit and architecture document for an existing instance typically takes one to two weeks. A net-new workflow build, depending on complexity and the number of integrations involved, runs two to six weeks through to production deployment. We do not give you a timeline until we have seen the actual scope, because n8n complexity varies enormously depending on what you are connecting and what your data looks like.

Yes. We offer a monthly retainer that covers monitoring, incident response, incremental workflow builds, and version upgrades as n8n releases new versions. Some clients take a clean handoff and maintain it internally. Others want a long-term partner. We are set up for both, and we will tell you honestly which makes more sense given your team's technical depth.