Zapier vs. Make vs. n8n: Picking the Right Automation Engine for Your AWS Stack
Zapier, Make, and n8n compared for 2026 — pricing, AWS integrations, self-hosting, and a decision guide to help you pick the right automation engine for your stack.
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Zapier vs. Make vs. n8n: Picking the Right Automation Engine for Your AWS Stack
Introduction
Every team running on AWS eventually hits the same wall: the glue work. Someone needs to push a new S3 object into a processing pipeline, sync a DynamoDB table with a CRM, or fire off a Lambda whenever a support ticket lands in Slack. You could write that glue code yourself with Lambda, EventBridge, and Step Functions — and for some jobs, you should. But for the huge middle tier of "connect app A to app B, transform the data, and don't wake anyone up at 2 a.m. when it breaks," a dedicated automation platform usually gets you there faster.
Three names dominate that conversation: Zapier, the category creator built for speed and breadth; Make (formerly Integromat), the visual-canvas platform that trades some simplicity for control; and n8n, the open-source, node-based tool that lets you self-host and code your way out of any corner. All three now sit comfortably alongside AWS rather than in competition with it — each can trigger Lambda functions, read and write to S3 and DynamoDB, and increasingly run inside AWS infrastructure via EC2, ECS, or Fargate.
This guide breaks down how each platform actually works, what it costs once you account for real usage (not just the marketing page), how each one plays with AWS services, and which one fits which kind of team. Pricing and feature details for SaaS automation tools shift often — figures below reflect publicly available information as of mid-2026 and are worth re-verifying against each vendor's pricing page before you commit budget.
Zapier: The Breadth Champion
Core functionality
Zapier pioneered no-code automation back in 2011, and its "Zap" model — a trigger in one app followed by one or more actions in others — is still the mental model most people picture when they hear the word "automation." <cite index="28-1">The company was founded in Columbia, Missouri by Wade Foster, Bryan Helmig, and Mike Knoop, and has grown into a roughly five-billion-dollar company built on a remarkably small amount of outside funding.</cite> Recent additions have pushed Zapier well past simple if-this-then-that logic: Paths add conditional branching, Zapier Tables and Interfaces add lightweight database and front-end capability, and Zapier Agents introduce autonomous, AI-driven task execution across the app catalog.
Ease of use
This is Zapier's home turf. The builder walks you step by step through choosing a trigger, mapping fields, and testing each action before it goes live. There's essentially no learning curve for a linear workflow, which is why Zapier remains the default recommendation for non-technical teams who need something working today rather than next week.
The trade-off shows up once workflows get complicated. Zapier has no native looping construct — iterating over a list of items requires a separate "Looping by Zapier" app, which adds cost and complexity. Multi-branch logic is possible with Paths, but stacking more than a few conditional branches inside one Zap tends to get unwieldy fast, and teams often end up splitting logic across several Zaps rather than one clean canvas.
Integration capabilities
Breadth is Zapier's clearest differentiator: sources place its catalog at roughly 7,000–9,000 supported apps, comfortably ahead of both competitors. For AWS specifically, Zapier offers native connectors for core services — S3 (create/trigger on new files), SES (transactional email), SNS, DynamoDB (write records from any trigger), and the ability to invoke Lambda functions as an action step. For services without a native connector, Zapier's webhook and "Code by Zapier" steps can call any AWS API directly.
Pricing and cost structure
Zapier's pricing runs on tasks — every action a Zap performs counts as one task, and a five-step Zap running 150 times a month can consume its entire monthly allotment on its own. Reported 2026 tiers look roughly like this:
Plan | Approx. price | Included tasks | Notes |
|---|---|---|---|
Free | $0 | 100/month | Two-step Zaps only, 15-minute polling |
Professional/Starter | ~$20–30/month (annual) | 750/month | Multi-step Zaps, filters, webhooks |
Team | ~$70–104/month | 2,000/month | Shared workspaces, SSO, up to 25+ users |
Enterprise | Custom | Custom | SAML SSO, dedicated support, custom limits |
The hidden cost most teams miss: task overage bills at a premium rate above your plan's base rate, AI-powered steps now consume multiple tasks per run under a model-based rate introduced in mid-2026, and Zapier bills exclusively in USD, which adds card-conversion friction for teams outside the US. Annual billing is meaningfully cheaper than month-to-month across every tier.
Use cases and target audience
Zapier is the right default for solo founders, marketing and ops teams, and any organization whose automations look like "when a form is submitted, create a CRM record and post to Slack." It's the fastest path from zero to a working automation and the safest choice when the team building it has no engineering background.
Deployment environment
Cloud-only, fully managed. There is no self-hosted or on-premises version of Zapier — everything runs on Zapier's infrastructure, which simplifies operations but means your workflow data passes through a third party by design.
Advanced features
Zapier's error handling centers on automatic retries and "autoreplay," which can re-run just the failed step in a multi-step Zap rather than the whole thing — clean and low-configuration, but with limited control over custom recovery logic. Zapier Copilot, an AI assistant, can help diagnose failed Zaps and draft new ones from natural-language prompts. Security and compliance features (SSO, audit logs, admin controls) are gated to Team and Enterprise tiers.
Growth trajectory
Zapier has spent the last two years pivoting from pure workflow automation toward an AI-agent platform, consolidating Zaps, Tables, Interfaces, and Agents into unified pricing, and adding MCP (Model Context Protocol) support to let its automations act as tools for AI systems — a direct response to competitive pressure from both Make and n8n's faster-moving AI feature sets.
Make: The Visual Power-User's Platform
Core functionality
Make (rebranded from Integromat in 2022) replaces Zapier's linear step list with a visual canvas: modules connected by lines, with routers for branching, iterators for looping over arrays, and aggregators for collapsing multiple items back into one. <cite index="26-1">The platform positions itself deliberately between Zapier's simplicity and n8n's technical depth</cite>, and that positioning shows up in almost every feature comparison — Make natively handles nested JSON, array manipulation, and multi-branch parallel paths in ways that would require workarounds or add-on apps in Zapier.
Ease of use
The canvas view is Make's biggest usability win: you can see an entire automation's logic at a glance, trace how data moves between modules, and hand the diagram to a new team member without narrating every branch verbally. That visual clarity comes with a real learning curve — building your first non-trivial scenario takes noticeably longer than building an equivalent linear Zap, and concepts like iterators and aggregators need some upfront understanding before they click.
Integration capabilities
Make's app catalog runs to roughly 2,000–3,000 native integrations plus thousands of pre-built scenario templates — smaller than Zapier's, but sources consistently note that Make's individual integrations tend to expose more of a connected app's functionality than Zapier's equivalent, since Make often maps closer to the underlying API. For AWS, Make offers dedicated HTTP and webhook modules alongside more limited native AWS-service modules than Zapier; most deep AWS work (Lambda invocation, S3 operations, DynamoDB reads/writes) goes through Make's generic HTTP/API request module rather than a purpose-built connector, which is more flexible but requires understanding the target AWS API directly.
Pricing and cost structure
Make bills in operations (later rebranded "credits" in an August 2025 pricing change) — each module execution, including routers, filters, and iterations, consumes one unit. Because a single Zapier "task" often corresponds to several Make operations, headline price comparisons ("Make is 3–5x cheaper than Zapier") need to be read carefully; the real comparison depends on how many modules a given workflow touches, not the sticker price alone.
Plan | Approx. price (annual) | Included operations/credits | Notes |
|---|---|---|---|
Free | $0 | 1,000/month | 2 active scenarios |
Core | ~$9–11/month | 10,000/month | Unlimited scenarios |
Pro | ~$16–19/month | 10,000+/month | Priority execution, custom variables |
Teams | ~$29–34/month | Higher tiers | Multi-user collaboration |
Enterprise | Custom | Custom | SSO, SCIM, audit logs, overage protection |
A genuinely useful 2026 feature: unused operations on paid plans can roll over into the following month, which softens the impact of seasonal or uneven usage. Additional operation packs, when purchased outside a plan's included allotment, carry a markup over the plan-rate price.
Use cases and target audience
Make fits SMBs and mid-market teams with someone technical enough to think in modules and data structures, but without dedicated developer resources for custom code. It's the natural next step for teams that hit Zapier's logic ceiling — nested conditionals, array processing, multi-step data transformation — without wanting to take on the operational overhead of self-hosting.
Deployment environment
Cloud-only, similar to Zapier, though Make is a European company with EU-based infrastructure by default, which matters for teams with GDPR data-residency requirements.
Advanced features
Make's error handling is more explicit than Zapier's: you build dedicated error-handling routes directly on the canvas, defining exactly what happens when a module fails — retry, fallback path, alert, or log — rather than relying on default retry behavior. This requires more upfront design work but gives real control over failure modes, which matters for workflows where a silent failure has business consequences (an unrouted lead, a missed invoice). Make also supports inline JavaScript within modules for on-the-fly data transformation, a capability Zapier doesn't offer natively.
Growth trajectory
Make has focused its 2025–2026 roadmap on AI: a built-in AI assistant ("Maia") that helps build scenarios from natural language, native modules for OpenAI, Anthropic's Claude, Google Gemini, and Stability AI, and a broader push toward "Make AI Agents" for autonomous workflow construction — largely mirroring the AI arms race happening across all three platforms.
n8n: The Open-Source, Developer-First Option
Core functionality
n8n takes a node-based approach closer to a visual programming environment than a simple automation builder — think Node-RED rather than a linear Zap list. <cite index="26-1">This structure offers exceptional technical flexibility but requires more upfront investment to learn than either competitor.</cite> Crucially, n8n is open source under a "fair-code" license: you can self-host it for free, inspect and modify the code, and write custom nodes or JavaScript/Python directly inside a workflow step — none of which is possible on Zapier or Make.
Ease of use
n8n is the most technical of the three by a clear margin. The payoff is that almost nothing is off-limits: a "Code" node lets you drop into JavaScript or Python mid-workflow when the visual nodes run out of expressiveness, and the same workflow logic (loops, branches, error triggers) that requires add-ons in Zapier is native here. Teams without any developer on staff will likely find the ramp steeper than it's worth; teams with even light engineering capability tend to find n8n faster to extend once the initial learning curve is behind them.
Integration capabilities
n8n's official node catalog is smaller on paper than Zapier's or Make's — estimates run from several hundred to just over 1,000 built-in integrations — but because n8n exposes an HTTP Request node and full custom-code capability on every plan, including the free self-hosted edition, it can talk to essentially any REST API, AWS included, without waiting for an official connector. n8n ships dedicated nodes for AWS S3, DynamoDB, Lambda, SNS, SES, and several other core services, and its HTTP Request node covers anything not natively wrapped. A meaningful architectural point for AWS-centric teams: n8n can be deployed inside your own AWS account — on EC2, in a Docker container on ECS/Fargate, or behind your existing ALB and RDS instance — so the automation engine and the AWS resources it orchestrates can share a VPC, IAM roles, and security groups instead of communicating over the public internet.
Pricing and cost structure
n8n bills by execution — one complete workflow run, regardless of how many steps or nodes it contains — which is the most favorable model of the three for long, multi-step workflows. A 20-step workflow costs the same as a 2-step one on n8n; the same workflow would burn through many more Zapier tasks or Make operations.
Option | Approx. price | Included volume | Notes |
|---|---|---|---|
Community Edition (self-hosted) | Free | Unlimited executions | You pay only for the server (~$4–10/month on a basic VPS); no SSO/advanced RBAC |
Cloud Starter | ~€20–24/month | ~2,500 executions/month | Unlimited workflows and users on every cloud tier |
Cloud Pro | ~€50–60/month | ~10,000 executions/month | |
Self-hosted Business | ~€600–800/month | ~40,000 executions | SSO, environments, Git-based version control |
Enterprise | Custom | Unlimited | Dedicated infrastructure, execution-based custom pricing |
The self-hosted Community Edition is the standout option for cost-sensitive, technically capable teams: it's the same core software as the paid tiers, with only enterprise features (SSO, LDAP, advanced permissions, dedicated support) gated behind a license. The honest caveat, echoed across nearly every self-hosting write-up: you're trading a software bill for an operations responsibility — someone has to own server updates, backups, TLS renewal, and the occasional 2 a.m. disk-space alert.
Use cases and target audience
n8n suits teams with data-privacy or data-residency requirements, high automation volume where per-task or per-operation billing gets expensive fast, and any organization with in-house engineering capacity willing to trade a steeper learning curve for full control. It's increasingly common as the automation layer behind internal AI agent systems, given its native LangChain integration and large library of AI-specific nodes.
Deployment environment
This is n8n's defining differentiator: cloud (fully managed, EU-hosted by default), self-hosted on your own infrastructure (a VPS, Docker, Kubernetes, or directly on AWS EC2/ECS/Fargate), or a hybrid where n8n runs in your VPC alongside the AWS services it orchestrates. Neither Zapier nor Make offers a self-hosted option at all.
Advanced features
n8n's error handling is the most configurable of the three: dedicated error-trigger workflows, conditional failure routing, and full custom retry logic are all available, though — as with Make — that power comes with more configuration responsibility than Zapier's zero-config retries. Self-hosted n8n is SOC 2 compliant and supports encrypted credential storage and role-based access control; cloud data for the hosted product is stored on EU servers in Frankfurt.
Growth trajectory
n8n's growth over the past eighteen months has been the most dramatic of the three. <cite index="61-1">Since a prior valuation of $350 million, the company's annual recurring revenue grew past $40 million, with usage increasing tenfold year-over-year</cite>, culminating in <cite index="60-1">a $180 million Series C round in October 2025 led by Accel, with participation from Nvidia's venture arm, at a $2.5 billion valuation</cite>. That valuation has since climbed further: <cite index="62-1">SAP took a strategic stake in n8n in May 2026, pushing its valuation to $5.2 billion and making it Germany's most valuable AI company</cite>, with <cite index="62-1">Mercedes-Benz cited as an early enterprise adopter building automation on the platform</cite>. The shift toward AI-native workflows has been just as sharp on the product side — n8n 2.0 added native LangChain integration and dozens of AI-specific nodes, and by mid-2026 the majority of workflows built on the platform reportedly involve an AI agent step of some kind.
Key Differentiators
Dimension | Zapier | Make | n8n |
|---|---|---|---|
Billing unit | Task (per action) | Operation/credit (per module run) | Execution (per workflow run) |
App/node catalog size | Largest (~7,000–9,000) | Mid-sized (~2,000–3,000) | Smallest official catalog, unlimited via HTTP/code |
Learning curve | Shallowest | Moderate | Steepest |
Self-hosting | No | No | Yes (free, open source) |
Visual logic (branching/loops) | Limited natively, add-ons needed | Strong, native routers/iterators | Strong, native + full custom code |
Error handling | Automatic, low-configuration | Explicit, visual, more setup required | Most granular, fully custom |
Best cost profile at scale | Weakest — task cost compounds with steps | Strong — operation cost 3–5x lower than Zapier for equivalent work | Strongest — execution-based billing decoupled from step count |
Data residency control | Zapier-managed only | Make-managed (EU by default) | Full control if self-hosted |
AWS-native deployment | Not applicable (SaaS only) | Not applicable (SaaS only) | Can run inside your own AWS VPC (EC2/ECS/Fargate) |
Automating Alongside AWS: A Quick Comparison
All three platforms can trigger from and act on core AWS services, but the depth and deployment model differ meaningfully:
- Zapier and Make both connect to AWS as external SaaS callers — your AWS services stay entirely inside your account, and the automation platform reaches in from the outside via API credentials you issue. This is simplest to set up and requires no infrastructure of your own, but it means workflow data leaves your VPC boundary and every AWS action is billed as a Zapier task or Make operation regardless of how internal the work is.
- n8n, by contrast, can run inside AWS — as a container on ECS Fargate behind your existing load balancer, on a standalone EC2 instance, or wired into your VPC alongside RDS and other internal services. For teams already running production workloads on AWS, this collapses the network boundary between the automation layer and the resources it touches, and lets you enforce IAM and security-group policy on the automation engine itself rather than trusting an external SaaS vendor's credential handling.
If your workflows mostly connect AWS to external SaaS tools (Slack, HubSpot, a payment processor), any of the three will work; the SaaS-vs-self-hosted question matters far more once the automation itself is handling sensitive internal data.
Decision Guide
Choose Zapier if: you have no in-house technical resources, need to move fast, and your workflows are mostly linear (trigger → one or two actions) across a very broad set of apps. Budget for task overages once you're running more than a handful of active Zaps.
Choose Make if: your team is comfortable with a visual canvas but doesn't have developers to spare, your workflows involve real branching or data transformation, and cost efficiency at moderate-to-high volume matters more than having the single largest app catalog.
Choose n8n if: you have engineering capacity, want to avoid vendor lock-in and per-task billing entirely, need data to stay inside your own infrastructure (compliance, security, or cost reasons), or you're building AI-agent-driven automation and want native LangChain support. Self-hosting on a small AWS EC2 instance or via ECS Fargate is a well-trodden path if you're already an AWS shop.
A pattern that shows up repeatedly across implementation write-ups: teams often start on Zapier because it's fastest to get a first automation running, hit a logic or cost ceiling within six to twelve months, and migrate to Make or n8n once workflows get more complex or expensive to run at volume. Starting with a realistic estimate of your execution volume and workflow complexity — rather than the platform with the flashiest homepage — tends to save that migration step.
Conclusion
There isn't a single "best" automation platform in 2026 — there's a best fit for a given team's technical depth, budget, and appetite for infrastructure ownership. Zapier remains the fastest on-ramp and the widest net for connecting to the broader SaaS ecosystem. Make offers a genuinely strong middle ground: visual power without needing a developer on staff. And n8n has emerged as the platform of choice for technically capable teams — especially those already on AWS — who want execution-based pricing, full self-hosting control, and the flexibility to drop into code whenever the visual builder isn't enough. All three are converging fast on AI-agent capability, so the gap that matters most going forward may be less about "what can it connect to" and more about who controls the infrastructure the automation runs on.
Sources
- Error handling and workflow logic: Alltomate — Make vs Zapier for Business Automation , Logic Inc. — Zapier vs Make , Synclovis — From Zapier to Make
- AWS integration and self-hosting: n8n.io — AWS DynamoDB and AWS S3 integration , Automation Ace — Amazon/AWS Zapier Integrations , DEV Community — Self-Hosting n8n on AWS ECS Fargate with Terraform
Pricing figures reflect publicly reported rates as of mid-2026 and change frequently on all three platforms; confirm current pricing directly with Zapier, Make, and n8n before budgeting.
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