n8n vs. Make, Zapier, Temporal and More: Which Automation Platform Wins?

A comparison published by the n8n blog pits n8n against eight workflow automation platforms, covering Make, Zapier, Temporal, Apache Airflow, Microsoft Power Automate, Pipedream, ZenML, and Workato. The guide scores each platform across five criteria: deployment model, execution reliability and pricing, integration depth, agentic AI readiness, and observability. The verdict depends almost entirely on your hardest constraint, whether that is self-hosting, cost at scale, native AI agents, or technical complexity.

What happened

Platform	Deployment	Integrations	Agentic AI
n8n	Self-hosted or cloud, fair-code license	1,000+ plus community nodes	Native agents, MCP support
Make	Cloud only	3,000+	AI agent builder, MCP
Zapier	Cloud only	9,000+	Agents (paid add-on, capped at 400/month on free)
Temporal	Self-hosted or cloud, open-source	Code SDKs only	Via code SDK
Apache Airflow	Self-hosted or managed, open-source	Python operators	No native agents
Power Automate	Cloud plus on-premises gateway	Native Microsoft + connectors library	Copilot agents
Pipedream	Serverless cloud	3,000+ plus custom code	LLM and agent builder
ZenML	Self-hosted or cloud, open-source	ML stack integrations	ML pipelines only
Workato	Cloud, enterprise iPaaS	1,000+ enterprise apps	Agent Studio

The n8n blog frames the choice around five evaluation criteria that affect teams for three to five years: where the platform runs and who governs it; how it handles failed or partial runs and what that costs; how deep its integrations and authentication options go; how ready it is for agentic AI workflows; and how visible its internals are so you can diagnose and control costs.

Platform by platform: what each one actually does

Make (formerly Integromat)

Make is a visual builder aimed at teams that need more logic than the simplest tools allow, without writing code. It recently added AI agents and a Model Context Protocol (MCP) server for driving LLM steps. Pricing runs on a per-operation model, meaning a busy multi-step scenario can drain a budget fast. Role-based access control (RBAC) is only available on higher tiers, which causes friction when multiple users share workflows on cheaper plans. Cloud-only, no self-hosting.

Zapier

Zapier connects over 9,000 apps and has the simplest interface of the group. Its free tier covers basic cloud automations, but teams with more than 25 users move to enterprise pricing charged per task. Running an automation thousands of times monthly gets expensive quickly, and there is an annual task cap. Agent usage above 400 runs per month requires a paid plan.

Temporal

Temporal is a code-first durable execution engine, meaning it retries and checkpoints long-running distributed workflows in code rather than through a visual canvas. You write workflows in Go, Java, TypeScript, or Python. There is no pre-built app catalog and no native AI layer. It is not suitable for non-technical teams or simple automation needs, but it is the strongest pick for mission-critical, high-stakes orchestration.

Apache Airflow

Airflow is an open-source scheduler built around DAGs (directed acyclic graphs, essentially a map of tasks and dependencies). It runs scheduled pipelines with retries and rich logs. There are no native AI agents, and managed deployment adds cost. It remains the standard for data engineering teams running Python-based pipelines.

Microsoft Power Automate

Power Automate runs in the cloud with an optional on-premises data gateway. Pricing is per user or per flow, and it leans heavily on the Microsoft ecosystem. Copilot agents are available for AI steps. It is the natural fit for teams already inside Microsoft 365, but less compelling outside that stack.

Pipedream

Pipedream is a serverless cloud platform priced per credit, combining invocation cost and compute time. It connects over 3,000 apps and supports custom code alongside an LLM and agent builder. Execution logs are available. No self-hosting option.

ZenML

ZenML is purpose-built for ML pipelines, not general-purpose app-to-app automation. It is open-source and supports self-hosted or cloud deployment with metadata tracking. It does not offer a typical app catalog, and agentic AI support is limited to ML pipeline patterns.

Workato

Workato is an enterprise iPaaS (integration platform as a service) with usage-based pricing, over 1,000 enterprise app connectors, recipe lifecycle management, and an AI agent layer called Agent Studio. It targets large organisations and comes with enterprise dashboards for governance. Cloud-only.

Why it matters

Picking the wrong platform now means either paying to migrate later or living with pricing that punishes growth. Per-task and per-operation models (Zapier, Make) look cheap at low volume but compound quickly. Cloud-only tools remove your ability to keep data on your own infrastructure, which matters for regulated industries or clients with strict data residency requirements.

Agentic AI support, where a platform can run multi-step AI workflows autonomously rather than just triggering a single LLM call, is the newest fault line. Most platforms bolt it on. n8n and Make have native MCP support; Zapier caps agent use on lower plans; Temporal and Airflow leave it entirely to your code. If AI agents are central to what you are building, that gap matters a lot. For teams already exploring AI integration, the choice of orchestration layer will shape what you can actually ship.

Our take

The guide is written by n8n, so read it with that in mind. That said, the five-criteria framework is genuinely useful and the platform summaries are broadly accurate. The honest version of the recommendation is this: if your team writes code and needs reliability above all else, look at Temporal. If you are in the Microsoft stack, Power Automate wins on integration depth. If you need self-hosted, fair-priced, AI-native automation without a purely code-first setup, n8n is a serious option.

Where we see clients go wrong most often is choosing Zapier at low volume and then discovering the per-task bill at scale. The free tier is a sales funnel, not a production architecture. The same applies to Make’s per-operation model. Before committing, run your highest-volume workflow through the pricing calculator at the volume you expect in 12 months, not today.

For teams evaluating workflow automation as a managed service rather than an in-house build, the platform debate matters less than having someone who knows the failure modes. The tools in this list all have them.

What to do about it

1. Identify your single hardest constraint: self-hosting requirement, AI agent depth, integration count, or maximum acceptable per-run cost.
2. Cross off every platform that does not meet that constraint. Do not compromise on your primary requirement.
3. Run your busiest real workflow through each shortlisted platform’s pricing calculator at 12-month projected volume.
4. Spin up a free trial or self-hosted instance and test your second and third most complex workflows, not just the demo use case.
5. Check observability before you commit: can you see failed runs, trace errors, and predict the monthly bill from the dashboard alone?

The platform you can actually debug at 2am is the one worth paying for.

Building an automation like this? Most client workflows we ship run on Make (referral link, it supports our reporting). If you would rather have it built and monitored for you, that is our workflow automation service.

Source: n8n Blog