Make (Make.com) Review: Visual Automation Platform (2026)
Make — formerly known as Integromat — is the automation platform that power users graduate to when Zapier starts feeling limiting. Its canvas-based scenario builder handles complex, multi-branch logic that would require significant workarounds on other platforms. Combined with competitive pricing and a growing AI module library, Make has become the default recommendation for technically comfortable users who want serious automation capability without writing code.
This review covers everything you need to know about Make in 2026: what it does well, where it struggles, and how to decide between Make, Zapier, and n8n.
Overview#
Make's core concept is the "scenario" — a visual workflow built on a canvas where you drag and connect modules. Each module represents an app or function: a trigger, a transformation, an API call, a conditional branch. The canvas shows the entire workflow at a glance, which is one of Make's biggest practical advantages over Zapier's linear step-by-step editor.
The platform connects more than 1,500 apps — a smaller library than Zapier's 6,000+, but covering all major business tools. Where Make compensates is depth: each integration typically exposes more API endpoints and configuration options than Zapier's equivalent.
Make added AI-specific modules beginning in 2024 — OpenAI, Anthropic, Perplexity, and local model integrations — positioning the platform as a visual layer for building AI-powered workflows. These modules have matured significantly and now support agentic patterns including tool-use loops and conditional AI routing.
Who it's for: Operations teams, power users, and technically comfortable non-developers who need complex automation logic. Agencies building automation workflows for clients. Small engineering teams who want automation without code. Anyone who has hit Zapier's limits on branching, data transformation, or cost.
Key Features#
1. Visual Scenario Builder#
The canvas builder is Make's defining feature and its biggest competitive advantage. You see the entire workflow — all branches, all modules, all data paths — simultaneously on one screen. Adding a branch doesn't push everything down; it spreads visually, making complex workflows readable.
Comparison with Zapier: Zapier's linear step editor is easier for beginners building simple automations. Make's canvas is dramatically better for anything with conditional logic, parallel paths, or loops. The cognitive difference becomes apparent the moment you need more than a simple "if this then that" structure.
Data transformation within scenarios is notably more powerful than Zapier. Make's built-in functions cover string manipulation, array operations, date formatting, and mathematical operations that Zapier requires a Code step (JavaScript) to achieve.
2. Operations-Based Pricing Model#
Make charges per "operation" — each module that executes in a scenario run. This is distinct from Zapier's "task" model, which also charges per module, but Make's per-operation prices are significantly lower.
This pricing model rewards power users who build complex workflows. A scenario with 20 modules that runs 1,000 times per month consumes 20,000 operations. On Make's Starter plan (10,000 operations for $9/month), you'd need to upgrade. On Zapier, a similar workflow at 1,000 runs would consume 20,000 tasks — exceeding the Professional plan (750 tasks) by a factor of 26.
The cost advantage for complex workflows is substantial. Most users migrating from Zapier to Make report 60-80% cost reductions for equivalent automation coverage.
3. AI Modules#
Make's AI module library has expanded considerably since the platform's AI push in 2024:
- OpenAI modules: Chat completions, Assistants API, image generation, embeddings
- Anthropic modules: Claude completions with full parameter control
- Google AI modules: Gemini, Vertex AI
- Perplexity modules: Search-augmented completions
- HTTP module: Connect to any AI API not natively supported
Building an AI agent loop in Make is achievable — you can construct cycles where AI output triggers conditional branches, which feed back into the AI module with updated context. It's more work than purpose-built agent platforms and lacks the native memory and state management that dedicated platforms provide. For the agent loop pattern in a visual builder, Make is the strongest option outside of dedicated agent platforms.
4. Data Structures and Advanced Routing#
Make handles arrays, JSON objects, and complex data structures natively. The Array Aggregator and Iterator modules process lists cleanly — splitting one API response into individual records, processing each in parallel, and aggregating results. This is workhorseautomation work that Zapier handles awkwardly.
Router modules allow true conditional branching: evaluate conditions on each route and execute only the matching path. Make's routers are significantly more flexible than Zapier's Filter, which only supports "continue if condition is met" rather than multi-branch logic.
5. Error Handling#
Make's error handling is the best in its category. You can add error handlers to individual modules — retry on failure, ignore and continue, route to an error-handling path, or roll back (for supported modules). The error handling overlay is visual, which makes it easier to reason about failure modes than Zapier's retry settings buried in configuration menus.
For production automations where failures are costly, Make's error handling capability is a genuine differentiator.
6. Make Community Edition (Self-Hosting)#
Make Community Edition (Make CE) is an open-source, self-hostable version of Make. For organizations with data residency requirements or significant volume where cloud operations costs become a concern, Make CE offers the same visual builder and module ecosystem on your own infrastructure.
Make CE requires technical resources to set up and maintain — it's not a one-click install. But it's a genuine option for enterprise buyers or regulated industries that can't use SaaS automation platforms.
Pricing#
| Plan | Monthly Price | Operations/Month | Key Features | |------|--------------|-----------------|--------------| | Free | $0 | 1,000 | 2 active scenarios | | Core | $9 | 10,000 | Unlimited scenarios | | Pro | $16 | 10,000 | Custom variables, full execution history | | Teams | $29 | 10,000 | Team features, roles | | Enterprise | Custom | Custom | SSO, SLAs, priority support |
Additional operations are purchased in blocks. The pricing scales much more favorably than Zapier for complex, high-volume workflows. The free tier is genuinely functional for learning and low-volume use — 1,000 operations covers simple workflows well.
Pros#
- Best visual canvas in the category — complex branching workflows are readable and maintainable in a way that linear editors cannot match
- Lowest cost per operation — significantly cheaper than Zapier for equivalent automation coverage, especially at complexity and volume
- Superior data transformation — built-in functions handle array operations, JSON manipulation, and complex data structures without requiring code steps
- Production-grade error handling — error routing, retry logic, and rollback capabilities that other platforms don't match
- Self-hosting option — Make CE gives enterprise and regulated-industry customers a viable on-premises path
Cons#
- Steeper learning curve — the canvas builder is more intuitive than code but harder to start with than Zapier's step editor; beginners often find it initially overwhelming
- Smaller integration library — 1,500+ apps versus Zapier's 6,000+; you'll occasionally find a tool Zapier supports that Make doesn't
- AI agent capabilities are constructed, not native — building true agent loops in Make requires creative workflow design; dedicated agent platforms are more capable for sophisticated AI reasoning
- Scenario debugging is less intuitive — tracing failures through a complex visual canvas can be harder than Zapier's step-by-step execution history, particularly for multi-branch scenarios
Who It's Best For#
Operations teams with complex automation needs: If your workflows require conditional branching, data transformation, loops, or error handling, Make is the right platform. The visual builder pays dividends for scenarios that would be multi-Zap chains in Zapier.
Cost-conscious teams at volume: Organizations automating thousands of records per day will find Make's operations pricing an order of magnitude more efficient than Zapier's task pricing. The migration payback period is typically under 60 days.
Agencies building client automations: Make's scenario templates, team workspaces, and client organization features make it practical for agencies managing automation workflows for multiple clients.
Not ideal for: Absolute beginners who want to get started in minutes (Zapier is faster to learn), teams requiring 6,000+ app integrations out of the box, or developers who prefer code-based automation (n8n or custom scripts are better fits).
Alternatives#
Zapier: Easier for beginners, larger integration library, more expensive at scale and complexity. See the full breakdown in the n8n vs Make vs Zapier comparison.
n8n: Open-source, self-hostable, code-friendly. Better for technical teams; weaker visual builder than Make. Useful comparison if self-hosting is a priority.
Relevance AI: Purpose-built for AI agents with memory and reasoning. For automation workflows with significant AI reasoning requirements, Relevance AI is more capable than Make's AI modules. See the Relevance AI review.
Final Verdict#
Rating: 4.5 / 5
Make is the best visual automation platform available in 2026 for users who need more than basic trigger-and-action automation. Its canvas builder, data transformation capabilities, error handling, and operations pricing make it the platform of choice for complex business workflows.
The learning curve is real, and the integration library is smaller than Zapier's. But for any user who has hit Zapier's limitations — on logic, on data transformation, or on cost — Make is the clear upgrade path.
For teams exploring AI automation alongside traditional workflow automation, Make's AI modules provide a practical starting point. For sophisticated agent reasoning and memory, a dedicated agent platform is still the better choice.
Browse the AI automation tool directory or compare specific automation platforms in the reviews section.
Frequently Asked Questions#
How hard is it to migrate from Zapier to Make? The concepts translate well — triggers, actions, and data mapping are similar. The main adjustment is the visual canvas versus Zapier's linear editor. Most users adapt within a week. The bigger effort is re-mapping field configurations, which requires time proportional to the number and complexity of your existing Zaps. Many automations can be rebuilt in Make in 30-60 minutes each.
Does Make support AI agent workflows? Make supports AI-powered workflows through its OpenAI, Anthropic, and other AI modules. You can construct loops where AI output conditionally routes back to AI processing — a basic form of the agent loop pattern. For fully autonomous agents with memory and complex reasoning, dedicated platforms are stronger. For AI-augmented business workflows (classify this input → route accordingly → take action), Make is capable and cost-effective.
What's the difference between Make.com and Make Community Edition? Make.com is the cloud-hosted SaaS platform — no infrastructure required, monthly subscription. Make Community Edition (CE) is the open-source, self-hostable version: same core functionality, runs on your own servers, requires DevOps resources to deploy and maintain. Make CE makes sense for organizations with data sovereignty requirements or very high operation volumes where cloud costs become prohibitive.
Can Make replace Zapier entirely for my team? For most teams, yes. The 1,500+ integrations cover the vast majority of business tools. You'll occasionally encounter a niche app that Zapier supports but Make doesn't. The HTTP module (for custom API calls) and webhooks fill many gaps. If you have specific integration dependencies, verify them in Make's integration library before committing to migration.