Make.com 料金 2026：完全なコスト＆クレジットガイド

Quick Summary: Make.com transitioned from operations-based to credit-based billing in August 2025, with pricing starting at a free tier offering 1,000 credits monthly. As of November 2025, Make offers Core, Pro, Teams, and Enterprise plans with monthly credit allotments ranging from 10,000 to 8 million credits, with extra credits now uniformly priced at a 25% premium whether purchased manually or through auto-purchasing.

Automation pricing feels like navigating a minefield. One month your workflows hum along smoothly within budget. The next? You’re staring at a bill that’s tripled because you crossed an invisible threshold.

Make.com’s pricing structure underwent a fundamental transformation in 2025 when the platform shifted from operation-based billing to a credit system in August 2025. The change generated community discussion and questions. Credits don’t map one-to-one with operations for every workflow type.

Here’s the thing though: understanding Make’s pricing isn’t just about knowing the monthly subscription cost. It’s about predicting how many credits your specific workflows will consume, when you’ll hit limits, and whether those limits will force you onto more expensive plans.

This guide breaks down Make.com’s 2026 pricing structure, the credit system mechanics, real-world cost comparisons, and the hidden expenses most teams discover only after they’re locked in.

The Credit System: How Make.com Bills in 2026

Make.com replaced operations with credits as the billing unit effective August 2025. The platform made this shift to accommodate AI integrations that consume resources differently than traditional app connections.

But credits and operations aren’t synonyms.

For non-AI applications, the conversion remains straightforward: 1 operation equals 1 credit. Connect your CRM to your email platform, trigger an action, consume one credit. Simple.

Third-party AI applications like OpenAI, Anthropic Claude, and Google Gemini also follow this pattern initially—1 operation equals 1 credit. However, modules with automatic AI provider connections include token usage in that single credit charge.

Make’s built-in AI tools calculate credits differently. The platform’s proprietary AI provider charges based on both tokens and operations combined. Custom AI provider connections (paid plans): credits are based on operations only (1 credit per operation). Tokens are billed directly by the AI provider (OpenAI, Anthropic, etc.).

This variability means two workflows running the same number of times can consume vastly different credit amounts depending on whether they leverage AI features and how heavily those features process data.

What Consumes Credits (And What Doesn’t)

Not every action in Make depletes your credit balance. Testing workflow steps? Unlimited. Filtering and formatting data? Unlimited. Getting an error on a step? Doesn’t count against your quota.

According to the official documentation, checking for new data in trigger apps also doesn't consume credits in most cases—though community discussions suggest nuances around specific apps like Airtable that may poll for changes more aggressively.

The credit system charges for completed actions that successfully execute and transfer data between applications. Failed operations typically don't consume credits, which provides some protection against runaway costs from misconfigured workflows.

Make.com Pricing Tiers Breakdown (2026)

Make structures its pricing across four primary tiers plus a free option. Each tier targets different automation scales and team sizes.

Free Plan: Testing the Waters

The free tier provides 1,000 credits monthly—enough to run basic workflows and familiarize yourself with the platform. Workflow execution intervals run every 15 minutes, which limits real-time automation capabilities.

This plan suits individual users exploring automation or running low-frequency workflows. Expect to hit the credit ceiling quickly if you're automating daily business processes.

Core Plan: Small Business Starting Point

As of November 6, 2025, the Core plan includes up to 300,000 credits per month. The platform adjusted this allocation from previous limits as part of pricing updates that took effect November 6, 2025.

The Core tier reduces execution intervals to 1 minute (and custom AI provider connections are now available on all paid plans, including Core). This democratization of AI capabilities represents a significant value shift for small automation users.

Pro Plan: Mid-Volume Automation

The Pro plan provides up to 8 million credits per month as of November 2025.

Execution intervals drop further, and teams gain access to advanced features like priority support and increased API rate limits. Custom AI providers remain available, with token consumption factored into the credit calculations.

Teams and Enterprise: High-Volume Operations

Teams and Enterprise tiers are customized offerings that scale beyond Pro's 8 million credit maximum, with plans tailored to specific organizational needs.

Enterprise clients typically work directly with Make's sales team to structure pricing around projected automation volumes, geographic deployment requirements, and compliance needs.

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November 2025 Pricing Adjustments: What Changed

Make implemented significant pricing adjustments effective November 6, 2025. The changes affected extra credit costs, baseline credit allocations for Core and Pro plans, and feature availability across tiers.

Extra Credit Cost Standardization

Previously, manually purchasing extra credits and enabling auto-purchasing resulted in different per-credit costs. The November update unified these pricing structures.

Both manual and automatic extra credit purchases now include a 25% premium above base plan credit rates. This standardization simplifies cost prediction but eliminates the previous discount available through certain purchase methods.

For teams regularly exceeding their plan allocations, this 25% surcharge accumulates quickly. A workflow consuming 80,000 credits monthly on a plan with 60,000 included credits would pay the premium rate on those additional 20,000 credits every billing cycle.

Credit Limit Revisions

Core and Pro plans received adjusted credit limits as part of the November changes. Core plan now includes up to 300,000 credits per month. The Pro plan includes up to 8 million credits per month effective November 6, 2025. The revisions generally increased baseline credit allowances, providing more headroom for growing automation needs without immediately forcing upgrades to higher tiers.

Custom AI Provider Democratization

Perhaps the most significant change: custom AI provider connections became available across all paid plans, not just premium tiers.

This means Core plan users can now connect directly to OpenAI, Anthropic, or other AI services using their own API keys. Credit consumption still applies based on token usage, but the capability barrier dropped substantially.

Real-World Cost Comparisons: Make vs Competitors

Pricing makes sense only in context. How does Make's credit-based model stack up against alternative automation platforms?

Make vs Zapier: The 13x Cost Difference

According to analysis from 2026 comparing automation platforms, Make delivers 10,000 operations for approximately €9 (or USD equivalent) compared to Zapier's 750 tasks at €19.99. That's a dramatic cost disparity for similar workflow execution.

The gap widens at scale. Zapier's task-based pricing forces teams through rapid tier jumps as automation volume grows. A workflow starting at 100 monthly operations typically expands to 2,000+ within six months as teams discover new automation opportunities.

One logistics company case documented on LinkedIn saw monthly automation costs escalate from €19.99 to €299, then €799 on Zapier as usage grew from 2,000 to higher volumes. Migration discussions began around 2,000 monthly operations when the economics became undeniable.

The cost advantage for Make becomes particularly stark at high volumes. Running 200,000 operations monthly on Zapier requires Company tier plans exceeding €500 monthly. The equivalent credit consumption on Make typically falls within significantly lower pricing tiers.

Make vs n8n: Open Source Trade-offs

n8n introduces a different economic model entirely: self-hosted deployment with no per-operation charges. Teams comfortable managing infrastructure can run unlimited workflows at fixed hosting costs.

Running 200,000 operations monthly on n8n's self-hosted option requires only server infrastructure costs, which vary based on provider and configuration.

But self-hosting carries hidden costs. Server maintenance, security patches, scaling infrastructure, and troubleshooting deployment issues consume technical resources. For teams without dedicated DevOps capacity, these operational burdens can outweigh the direct cost savings.

n8n's cloud offering splits the difference, providing managed hosting with per-workflow pricing rather than operation-based billing. Teams pay for active workflows rather than execution volume, which benefits high-frequency automations.

Make vs Activepieces: Community vs Commercial

Activepieces offers a free Community edition for self-hosted deployments with no task limits. The commercial Standard plan charges $5 per active flow monthly after 10 free active flows, with unlimited runs included.

The Standard plan includes unlimited runs, AI agents, 10 free active flows, and costs $5 per active flow per month for flows beyond the 10 free included.

This per-workflow pricing model provides predictable costs regardless of execution frequency. A workflow running 100,000 times monthly costs the same $5 as one running 100 times—a stark contrast to Make's credit consumption model.

The trade-off? Activepieces has a smaller ecosystem of pre-built integrations compared to Make's extensive app library.

Hidden Costs and Pricing Surprises

Advertised pricing rarely tells the complete cost story. Several factors inflate actual automation expenses beyond base subscription fees.

The 25% Extra Credit Premium

Teams that consistently exceed plan allocations pay a permanent 25% surcharge on additional credits. Unlike one-time overages, this premium applies every billing cycle for chronically under-provisioned plans.

A team paying €50 monthly for their base plan who regularly consumes an additional 50% credits (€25 worth) faces the base cost plus a 25% premium on extra credits, totaling €50 + (€25 × 1.25) = €81.25 monthly—a 62.5% increase over the advertised tier price.

Make recommends migrating to higher tiers when regular usage exceeds plan limits, since upper-tier credits carry lower per-credit costs. But many teams hesitate to commit to more expensive plans, choosing instead to absorb the premium charges.

AI Feature Credit Multiplication

AI-powered workflows consume credits at accelerated rates due to token usage. A traditional CRM-to-email workflow might consume 1 credit per execution. The same workflow enhanced with AI content generation could consume 5-10 credits per execution depending on prompt length and model selection.

Teams migrating existing workflows to incorporate AI features often experience 3-5x credit consumption increases without corresponding increases in workflow execution frequency. This catches automation managers off-guard when monthly usage suddenly spikes.

Polling vs Webhook Cost Differences

Community discussions reveal that certain trigger types consume credits more aggressively than expected. Polling-based triggers that check for new data on intervals may generate credit charges even when no new data exists to process.

Community discussions reference concerns about Airtable watch records triggers consuming credits on inactive bases, though whether this represents standard behavior or a platform quirk remains debated.

Webhook-based triggers generally avoid this issue since they activate only when external systems push new data. Structuring workflows around webhooks rather than polling can reduce phantom credit consumption substantially.

Development and Testing Overhead

While Make doesn't charge credits for testing individual workflow steps, development activities still consume resources. Complex workflows require dozens of test executions during building and debugging phases.

Teams running development, staging, and production environments must account for credit consumption across all environments. A workflow consuming 5,000 credits monthly in production might consume another 2,000-3,000 credits during ongoing refinement and optimization.

When Make's Pricing Makes Sense (And When It Doesn't)

No automation platform fits every use case optimally. Make.com's pricing structure works better for certain workflow patterns than others.

Ideal Scenarios for Make

Make delivers strong value for teams running moderate-to-high volumes of traditional automation workflows without heavy AI components. The 10,000-100,000 credit monthly range offers competitive pricing against Zapier and other task-based platforms.

Organizations wanting visual workflow builders without coding requirements benefit from Make's interface design. Non-technical teams can construct and maintain automations without developer support.

Businesses requiring complex branching logic, error handling, and data transformation within workflows find Make's advanced features worth the investment. The platform supports sophisticated automation patterns that simpler tools can't accommodate.

When to Consider Alternatives

Teams with technical capacity should evaluate n8n's self-hosted option for cost optimization at scale. Running 500,000+ operations monthly often becomes more economical on self-managed infrastructure despite operational overhead.

Very high-frequency workflows benefit from per-workflow pricing models like Activepieces rather than per-operation charging. A workflow executing 50,000 times daily consumes credits relentlessly on Make but costs a flat monthly fee on workflow-based pricing.

AI-heavy automation pipelines may face better economics on platforms offering flat-rate AI access or bundled token allowances. Make's token-based credit consumption can escalate costs quickly for natural language processing and content generation workflows.

Budget-constrained startups running basic automations might find Zapier's free tier (100 tasks monthly) or n8n's self-hosted community edition more appropriate starting points despite their limitations.

Optimizing Make.com Costs: Practical Strategies

Teams committed to Make can implement several tactics to control credit consumption and avoid bill shock.

Right-Size Your Plan Selection

The 25% extra credit premium makes under-provisioning expensive. Teams consistently consuming 110-120% of their plan allocation should upgrade to the next tier rather than paying recurring overage charges.

Calculate your break-even point. If the next tier costs €20 more monthly but eliminates €30 in recurring extra credit charges, the upgrade delivers immediate ROI.

Conversely, teams using only 60-70% of their plan allocation should consider downgrading. Make's pricing structure doesn't penalize right-sizing—you can adjust tiers monthly based on actual consumption patterns.

Prefer Webhooks Over Polling

Restructure workflows to use webhook triggers rather than polling-based checks wherever possible. Webhooks activate only when events occur, eliminating background polling that may consume credits during inactive periods.

Most modern SaaS applications support webhook notifications for key events. The initial setup requires slightly more configuration than polling triggers, but the credit savings compound over time.

Consolidate Related Operations

Each discrete operation consumes a credit. Workflows that could combine multiple API calls into batched requests reduce total credit consumption.

Instead of processing individual records one-by-one through separate workflow executions, aggregate records and process them in batches. A workflow processing 100 records individually consumes 100+ credits. The same workflow processing those records in 10-record batches consumes only 10 credits.

Monitor AI Token Usage

AI-powered workflows consume credits based on token counts, which correlate directly with input and output length. Shorter, more focused prompts consume fewer tokens than verbose instructions.

Optimize prompts to be concise and specific. Remove unnecessary context from AI inputs. Limit output length parameters to only what workflows actually need.

Track which AI operations consume the most tokens through Make's usage dashboards. A single inefficient AI step might account for 60-70% of total workflow credit consumption.

Implement Smart Filtering

Filtering steps don't consume credits, but the operations that follow them do. Place aggressive filters early in workflows to prevent unnecessary downstream processing.

A workflow that triggers on all database changes but only needs to process records meeting specific criteria should filter immediately after the trigger. Processing 1,000 records then filtering wastes 800+ credits if only 200 records actually meet the criteria.

Schedule Low-Priority Workflows

Real-time execution isn't always necessary. Low-priority automations can run on longer intervals (hourly or daily rather than every 5 minutes) to batch operations and reduce total execution count.

A workflow checking for new form submissions every 5 minutes executes 288 times daily. The same workflow checking hourly executes 24 times daily—a 92% reduction in credit consumption if each check constitutes a billable operation.

Migration Considerations: Switching Platforms

Automation lock-in creates real switching costs. Teams considering platform migrations should account for more than just pricing differences.

Technical Migration Effort

Rebuilding workflows on new platforms consumes substantial time. A moderately complex workflow with 15-20 steps typically requires 4-8 hours to reconstruct and test on a different platform, accounting for differences in module behavior and data formatting.

Organizations running 20+ production workflows may require significant migration effort, typically estimated at 4-8 hours per moderately complex workflow. Factor this opportunity cost into platform selection decisions.

App Integration Availability

Make supports thousands of pre-built app integrations. Alternative platforms may lack connectors for niche applications your workflows depend on.

Audit your current integration requirements against prospective platforms' app ecosystems before committing to migration. Discovering a critical integration gap mid-migration creates painful choices between rebuilding business processes or aborting the platform switch.

Team Training and Adoption

Non-technical teams familiar with Make's visual interface may struggle with code-heavy alternatives like n8n. Training time, reduced productivity during learning curves, and potential resistance to change add hidden migration costs.

Conversely, technical teams moving from Zapier or Make to n8n often experience productivity gains once past initial learning curves. The ability to write custom code directly in workflows unlocks automation patterns impossible in pure low-code environments.

Data Historical Continuity

Automation platforms typically don't export complete execution histories, logs, or workflow run data. Migrating platforms means losing historical visibility into past automations.

For compliance-sensitive industries requiring audit trails, this loss of historical data may create regulatory complications. Archive critical execution logs before migration or plan overlapping periods where both platforms run simultaneously.

Make.com Pricing FAQ

Looking Ahead: Make.com Pricing Trajectory

The shift to credit-based billing signals Make's strategic focus on AI integration. The platform positioned this change as necessary infrastructure for fairly pricing diverse resource consumption patterns.

That reasoning holds merit. AI operations genuinely consume more computational resources than simple data transfers. Token-based pricing reflects actual costs more accurately than flat per-operation rates.

But the transition also creates complexity. Teams must now estimate not just operation counts but token consumption patterns when forecasting costs. That requires deeper technical understanding than previous operation-counting models.

Expect continued pricing adjustments as Make refines the credit system based on real-world usage patterns. The November 2025 changes to Core and Pro credit allocations demonstrate the platform's willingness to modify pricing structures post-launch.

The democratization of custom AI providers across all paid plans represents a significant value addition for lower-tier users. This suggests Make is competing aggressively for the small-to-medium business segment rather than focusing exclusively on enterprise clients.

Future pricing evolution will likely track broader automation market trends: increasing pressure from open-source alternatives like n8n, continued competition with Zapier's market dominance, and the integration of increasingly sophisticated AI capabilities that may warrant new pricing tiers.

Final Recommendations

Make.com's pricing delivers strong value for non-technical teams running 10,000-100,000 operations monthly who need visual workflow design and extensive pre-built integrations. Make positions itself in the market between Zapier's higher costs and n8n's technical requirements.

Start with the Core plan to establish baseline usage patterns. Make's credit monitoring dashboards provide visibility into actual consumption within the first billing cycle. Adjust tier selection based on real data rather than estimates.

Watch for the 25% extra credit premium. If regular consumption exceeds 85-90% of plan allocations, upgrade proactively rather than absorbing recurring overage charges.

Teams with technical capacity managing 200,000+ operations monthly should seriously evaluate n8n's self-hosted option. The operational overhead pays for itself through dramatically reduced per-operation costs at scale.

For AI-heavy workflows, monitor token consumption aggressively. Optimize prompts for conciseness and limit output lengths to necessary parameters. AI feature convenience carries real cost implications that compound across high-frequency executions.

Migration decisions should weigh total cost of ownership beyond subscription fees: development time for rebuilding workflows, team training investments, lost historical data, and potential productivity dips during transitions.

Most importantly, periodically reassess platform selection as automation needs evolve. The optimal platform for 5,000 monthly operations may not serve 50,000 operations cost-effectively. Automation strategy should adapt as business requirements scale.

Check Make.com's official pricing page for current plan details and regional availability, as pricing structures continue evolving based on market dynamics and feature releases.