n8n vs Zapier vs Make vs Swfte 2026: Full Feature, Pricing & Lock-in Comparison

The workflow automation market split into two camps over the last eighteen months. On one side: the classical iPaaS leaders—Zapier, Make (formerly Integromat), and the open-source upstart n8n—which started as connector engines and have since bolted on AI nodes. On the other: a new wave of AI-native orchestrators—Swfte, Dify, Coze, LangFlow, Flowise—which started from LLMs and are now adding the connector library, governance, and observability that classical iPaaS has had for a decade.

If you are choosing in 2026, the question is no longer "which one has the most apps." Zapier wins that fight and will keep winning it. The real question is which platform fits the workload you actually have: a finance team gluing SaaS tools, a developer team running a self-hosted automation backbone, an ops team building thousand-step scenarios, or a product team putting LLMs into customer-facing flows.

This comparison scores all four head-to-head across 30+ features, prices each at three operating scales, and introduces an Automation Lock-In Index—a 0–10 score that captures how trapped you become once your workflows live inside a vendor.

The 30-Second Decision Matrix

If you read nothing else, this table:

None of these are wrong defaults. They are different products solving different problems, and most teams over a certain size end up running two of them in parallel.

The Four Platforms in One Sentence Each

• Zapier is the consumer-grade automation layer for SaaS, optimized for someone who has never written a line of code and wants to connect Gmail to Airtable in ninety seconds.
• Make is the visual scenario builder for ops people who think in flowcharts, with per-operation pricing that rewards efficient design.
• n8n is the open-source, self-hostable workflow engine for engineering teams that want connector breadth without per-task fees and the freedom to fork.
• Swfte is the AI-native orchestrator that treats LLM calls, multi-provider routing, and governance as first-class primitives rather than nodes bolted onto a 2015-era iPaaS.

Everything below is a more careful version of those four sentences.

Feature Matrix: 30+ Capabilities Compared

Scoring legend: Yes = full native support, Partial = supported but limited, No = not supported, Add-on = available via paid tier or third-party.

Reading the matrix: Zapier's connector count is unmatched and probably uncatchable. n8n and Swfte cluster together on developer experience, observability, and AI primitives. Make sits in the middle on most things and pulls ahead on visual scenario design—a category that does not appear in feature matrices but matters enormously to people who think in pictures.

Pricing Reality at Three Scales (May 2026)

Headline pricing tiers, taken from each vendor's published page in May 2026. Always verify before procurement—prices change quarterly.

2026 Public Pricing per Tier

Several caveats: Zapier's "task" is a single action step inside a Zap, so a 5-step Zap consumes 5 tasks. Make's "operation" is similar but generally smaller-grained—a single HTTP call, a single record processed in an iterator. n8n Cloud counts "executions" (one workflow run = one execution regardless of step count), which is structurally cheaper for multi-step workflows. Swfte counts "runs" similarly to n8n. These units are not directly comparable, which is why the chart below normalizes to cost per 1,000 workflow runs.

Effective Cost per 1,000 Workflow Runs

Effective Cost per 1,000 Workflow Runs (May 2026)
                          Light       Mid        Heavy
                       (10K/mo)   (100K/mo)   (1M/mo)
n8n (self-host)          $4         $1         $0.20  (infra only)
n8n Cloud                $20        $12        $8
Zapier Pro               $73        $58        $45
Zapier Team              $103       $80        $62
Make Pro                 $30        $19        $11
Swfte Workflows          $25        $15        $9
Source: vendor pricing pages, May 2026 (assumes 5-step avg workflow)

Same data as a horizontal bar chart for the mid-volume scenario:

Cost per 1K Runs at 100K runs/month (May 2026)
n8n self-host  | $1
n8n Cloud      |############ $12
Make Pro       |################### $19
Swfte          |############### $15
Zapier Pro     |########################################################## $58
Zapier Team    |################################################################################ $80
                0       20      40      60      80     100

The pattern is consistent across scales: per-task pricing (Zapier) is structurally expensive for multi-step workflows, per-operation pricing (Make) is competitive at moderate scale, and per-execution pricing (n8n Cloud, Swfte) wins for workflows that do many things per run. Self-hosting n8n is dramatically cheaper at scale if you have engineers willing to operate it, which is the assumption that breaks down most often. A part-time engineer at $80/hr who spends 4 hours/month on n8n maintenance costs $320—more than a Pro Cloud plan handling the same load.

The Gumloop analysis makes a related point about self-hosted AI platforms: the operating cost is rarely zero, and the question is whether the cost is denominated in dollars or engineering hours.

The Automation Lock-In Index

Pricing is one cost. The other—usually larger and almost never modeled—is the cost of getting out. Once a workflow lives inside a vendor's runtime, switching means rebuilding it. The Automation Lock-In Index is a 0–10 score that captures how trapped you become.

Sub-scores (sum to 10):

• Workflow Portability (0–3): Can you export your workflows in a documented, executable format? Can a competitor import them? 3 = full open spec, 0 = proprietary binary.
• Connector Standards (0–2): Are connectors written against an open spec (OpenAPI, MCP, etc.) or a proprietary SDK? 2 = standards-based, 0 = fully proprietary.
• Self-Host Option (0–2): Can you run the engine yourself, with or without enterprise license? 2 = free self-host, 1 = paid self-host, 0 = SaaS only.
• Data Egress Cost (0–2): What does it cost to extract your run history, secrets, and execution data? 2 = free export, 0 = data hostage.
• Pricing Predictability (0–1): Will your bill suddenly 5x because of a connector change or unit reclassification? 1 = predictable, 0 = surprise bills documented.

A higher score is better (less lock-in). Note: the score does not measure platform quality—it only measures escape velocity.

Lock-In Scores

Automation Lock-In Index — Higher is Better (less lock-in)
n8n     |###################################################################################### 9
Swfte   |######################################################################### 8
Make    |##################################### 4
Zapier  |################## 2
         0    1    2    3    4    5    6    7    8    9   10

The headline reading: n8n is the most portable, Swfte close behind, Make middling, Zapier the most locked-in. This is unsurprising—Zapier's product is the connectors, and the connectors are not portable. The justifications:

• Zapier (2): Workflows export only as Zap descriptions, not executable artifacts. Connectors are proprietary. No self-host. Data export possible but tedious. Pricing has changed unit definitions twice in the last three years.
• Make (4): Scenarios export to JSON, but the JSON references Make-specific module IDs that don't run anywhere else. No self-host. Pricing is reasonably stable.
• n8n (9): Workflows are JSON, the runtime is open-source, and you can fork the entire codebase. The only points lost are connector standards (n8n's connector format is its own) and some friction around exporting credentials.
• Swfte (8): Workflows are JSON with a documented schema, connectors lean on OpenAPI and MCP where possible, self-host is enterprise-tier (not free), and pricing has been stable since launch. Loses points to n8n on the free self-host axis.

Lock-in isn't always bad—Zapier's tight integration is part of why it works for non-technical users, who don't want to think about portability. But for engineering teams, an Index below 5 should be treated the same way as any other vendor risk.

Where n8n Wins

n8n is the right choice when at least two of these are true:

• You have engineers who can run the service.
• You want to avoid per-task or per-operation pricing.
• You need to inspect, modify, or fork the platform.
• You have privacy or sovereignty constraints that make SaaS difficult.
• Your workflows include sensitive data that you'd rather not route through a vendor.

The economics are striking: a single $40/month VPS can run tens of thousands of workflow executions, with horizontal scaling via queue mode for higher loads. The n8n team has continued to invest in AI primitives—LangChain nodes, vector store nodes, agent loops—and the gap between n8n and AI-native platforms has narrowed considerably since 2024. Jimmy Song's open-source AI agent comparison places n8n as the strongest classical-iPaaS contender for AI workloads, ahead of Zapier and Make on agent flexibility but behind purpose-built tools like Dify and Swfte on multi-provider routing depth.

n8n's published pricing—free Community, paid Cloud tiers, custom Enterprise—is documented at n8n.io/pricing. It is the cheapest option at scale by an order of magnitude if and only if you treat operating the platform as zero-cost, which most teams shouldn't. Put another way: n8n self-host is the cheapest line item but rarely the cheapest total cost of ownership unless you already have SREs.

For an example of how an enterprise team might combine self-hosted n8n with AI-native workloads, see our enterprise workflow automation playbook.

Where Zapier Wins

Zapier's moat is connector count and UI accessibility, and both are formidable. With 6,000+ apps, Zapier is almost guaranteed to have whatever obscure SaaS tool you use. The 2026 lineup adds AI-assisted Zap building, where you describe a workflow in natural language and Zapier proposes the steps—a feature that genuinely lowers the barrier for non-technical users.

Zapier wins decisively for:

• Non-technical operators: Marketing managers, sales ops, customer success—people who need automation but won't ever open a code editor.
• Niche SaaS tools: If you live in HoneyBook or Calendly or Pipedrive, Zapier has connectors n8n and Make don't.
• Speed of first value: From signup to working Zap is often under five minutes.
• Templates and community: Tens of thousands of public Zap templates accelerate common patterns.

Zapier loses for:

• Cost at scale: Per-task pricing punishes multi-step workflows and high-volume use.
• Complex topology: Paths exist but feel grafted on; deep branching gets unwieldy.
• AI orchestration: AI nodes exist but multi-step agent workflows are awkward.
• Lock-in: As scored above, Zapier has the lowest portability of the four.

Zapier's pricing is at zapier.com/pricing. The Pro tier covers most small-business needs; Team and Company unlock multi-user and SSO.

Where Make Wins

Make's signature is the scenario canvas: a visual flowchart where every module, route, and aggregator is laid out spatially. For workflows with twenty-plus steps and conditional branching, Make's UI is genuinely the best in this comparison—it remains readable where Zapier's linear list and n8n's node graph become cluttered.

Make wins for:

• Visual scenario design: Long, branching workflows are easier to reason about visually.
• Per-operation pricing efficiency: A scenario that handles 100 records in one run costs less per record than 100 separate Zaps.
• Iterators and aggregators: First-class primitives for processing arrays.
• Mid-volume operators: Companies running 10K–500K operations/month land in Make's pricing sweet spot.

Make's per-operation pricing is at make.com/en/pricing. The Core tier ($10.59/mo) is competitive against n8n Cloud Starter for low volumes, and the Pro tier adds scheduling and priority execution.

Where Make is weaker: AI orchestration. Make has LLM modules but lacks the multi-provider routing, governance, and agent primitives of newer platforms. For teams whose workflows are mostly classical iPaaS (CRM updates, file processing, notifications), Make is excellent. For teams whose workflows are mostly LLM-driven, Make starts to feel like a 2018 product with AI bolted on.

Where Swfte Wins

To be honest about what Swfte is and isn't: Swfte is the youngest of the four, has the smallest connector library (280+ vs. Zapier's 6,000+), and is the wrong choice if your primary problem is "connect Gmail to Airtable." Anyone telling you otherwise is selling you something.

Swfte's wedge is AI-native orchestration with governance. Where the other three started from connectors, Swfte started from LLMs and built the connectors second. The differences that result:

• Multi-provider LLM routing: Native cost, latency, and quality routing across OpenAI, Anthropic, Google, DeepSeek, Mistral, and self-hosted models. The other three platforms have LLM nodes but not routers.
• Governance baked in: PII detection, prompt audit trails, model approval policies, and per-tenant cost caps as platform features rather than afterthoughts.
• Workflows engine: A durable workflow runtime with retries, branching, and human-in-the-loop steps that's designed around LLM patterns (token streaming, partial completions, agent loops).
• MCP and OpenAPI first: Connectors are written against standards where possible, which is part of why Swfte's lock-in score is lower than Make's despite being a younger product.

Swfte loses to:

• Zapier on connector count: Not close.
• n8n on price at scale (self-hosted): Not close, if you have ops staff.
• Make on visual scenario complexity: Make's canvas is still the best for huge flowcharts.

The honest summary: pick Swfte when AI is the workflow, not a step in the workflow. For a deeper architectural comparison with Dify—Swfte's most direct AI-native competitor—see Dify vs Swfte: open-source agent platforms. For broader enterprise positioning, see enterprise AI automation platform comparison 2025.

AI/LLM Integration Depth

The most volatile category in this comparison. Every platform is shipping AI features quarterly, and a static comparison ages quickly. As of May 2026:

n8n and Swfte cluster together at the top of this category, which mirrors the broader market structure: open-source and AI-native platforms have invested heavily in LLM primitives, while Zapier and Make have added LLM features but kept them on the periphery.

Two external comparisons worth reading on this dimension: Tovie's analysis of agent platforms covers Dify and n8n at depth, and the Gumloop Dify alternatives roundup covers the broader AI-native landscape. Both are useful for triangulating where the AI-native platforms sit relative to classical iPaaS.

For practical workflow patterns across all four platforms, our AI automation workflow templates library catalogs reference designs.

Migration Paths Between Platforms

Most teams end up migrating at least once, usually because they outgrew Zapier on cost or wanted self-host. Practical migration notes:

• Zapier → n8n: Hardest, because Zaps don't export as executable workflows. Plan to rebuild manually. The n8n community maintains a partial converter that handles ~60% of common Zaps automatically; the rest is hand work. Budget 1–2 hours per non-trivial Zap.
• Zapier → Make: Similar pain to n8n. No automated converter that's reliable. The upside: Make's per-operation pricing usually delivers 40–70% savings at the same workload, which makes the rebuild cost back quickly.
• Make → n8n: Easier than Zapier migrations because Make scenarios export to JSON. Still requires manual mapping of module IDs to n8n nodes.
• n8n self-host → n8n Cloud (or vice versa): Trivial. Same export format, same engine.
• Anything → Swfte: Manual rebuild today. We are working on a Make scenario importer; it covers basic linear flows and ships incrementally. Treat manual migration as the assumption.
• Swfte → n8n / elsewhere: Workflow JSON exports are documented; classical iPaaS steps map cleanly. AI-specific steps (multi-provider routing, governance policies) won't translate—you'd lose those features whether you migrated to n8n, Make, or Zapier.

The general rule: migration cost roughly tracks the inverse of the Lock-In Index. Higher index = easier to leave.

Total Cost of Ownership at Scale

For a team running ~250K workflow executions per month with 5 average steps per execution (a realistic mid-market load), the all-in 2026 picture:

Two surprises in this table that always come up in procurement reviews: n8n self-host is not the cheapest option once you account for SRE time, and Swfte Workflows Team is competitive with Make at this scale despite being a newer product. The n8n Cloud Pro tier is often the right compromise for teams that want n8n's portability without owning the operational burden.

What to Do This Quarter

Five to seven concrete actions, split by company size.

If you are a startup (1–20 people):

1. Default to Zapier for non-engineering automations. The team's time matters more than the bill until you cross ~$5K/month in Zapier spend.
2. Stand up n8n Cloud Starter ($24/mo) for engineering automations. Use it for anything with sensitive data, custom code, or more than 5 steps.
3. Evaluate Swfte for AI-specific workflows—customer support triage, content pipelines, agent flows. Don't try to make Swfte your general iPaaS; that's not what it is.

If you are a scale-up (20–200 people):

4. Audit your Zapier spend. If it's over $3K/month, run a 30-day pilot of Make or n8n Cloud on the highest-volume Zaps. Document the migration cost honestly—it's usually 20–40 hours of engineering time, paid back in 3–6 months.
5. Centralize AI workflows on a single AI-native platform. Spreading LLM logic across Zapier, Make, and n8n nodes makes governance impossible. Whether you pick Swfte, Dify, or stay with n8n's AI nodes, pick one and consolidate.

If you are an enterprise (200+ people):

6. Score every automation platform you use against the Lock-In Index. Anything below 5 needs a documented exit plan. This is not paranoia—it's the same vendor-risk discipline you apply to databases and identity providers.
7. Separate the iPaaS layer from the AI orchestration layer. Zapier or Make (or n8n) handles the SaaS plumbing; an AI-native platform handles LLM workloads with governance, audit trails, and multi-provider routing. Trying to do both in one tool either compromises the iPaaS or compromises the AI.

The honest closing observation: there is no single best platform in this comparison. Zapier, Make, n8n, and Swfte are good at different things, and the teams that win in 2026 use two of them deliberately rather than one of them by default.