Make (Make.com): Complete Platform Profile

Make — known as Integromat before its 2022 rebrand — is a visual automation platform that occupies a distinctive position in the AI agent and automation market: more powerful and flexible than Zapier for complex workflows, more accessible than n8n for non-developers, and increasingly capable as an AI orchestration layer as it adds native AI modules, HTTP/webhook routing, and multi-step data transformation.

Originally built as a technical Zapier alternative for users who needed conditional logic, data mapping, and complex workflow branching, Make has evolved into a platform where sophisticated automation builders can orchestrate AI agents, call LLM APIs, process structured data, and build workflows that behave like rudimentary agents — all through a visual drag-and-drop canvas without writing traditional code.

This profile examines Make's architecture, core capabilities, AI features, pricing tiers, honest limitations, and how it fits into the enterprise AI automation landscape for buyers evaluating automation platforms alongside dedicated agent frameworks.

Browse the complete AI agent platform directory to compare Make with other enterprise automation and agent platforms.

Overview#

Vendor: Make (formerly Integromat, acquired by Celonis in 2020)
Category: Visual Automation Platform
Founded: 2012 (as Integromat), rebranded to Make in 2022
Headquarters: Prague, Czech Republic (EU)
Pricing Model: Freemium with paid tiers; operations-based metering

Make is a European-headquartered platform, which is relevant for GDPR compliance and EU data residency considerations. The platform is cloud-hosted with EU and US data center options, and its European origins have driven stronger data privacy controls compared to some US-headquartered competitors.

The platform serves a broad user spectrum: freelancers and solopreneurs building their first automation workflows, technical operators at scale-ups managing hundreds of automated processes, and enterprise teams building internal process automation that requires more structural control than Zapier's linear model but more accessibility than self-hosted n8n.

In the context of AI agents, Make is not a purpose-built agent platform in the way that Amazon Bedrock Agents or Salesforce Agentforce are. Rather, it is an automation orchestration platform where AI capabilities — LLM API calls, text processing, image generation, data classification — are modules within a broader automation workflow. The distinction matters: Make builds automation workflows that include AI steps; dedicated agent platforms build autonomous AI systems that include tool-calling capabilities.

For many practical enterprise use cases — content processing pipelines, data enrichment workflows, AI-augmented customer communication — this distinction collapses, and Make serves the purpose effectively at lower cost and complexity than dedicated agent infrastructure.

Core Features#

Visual Scenario Builder#

Make's defining feature is its visual scenario canvas — a flow-based editor where automation scenarios are constructed by connecting modules (triggers, actions, data transformers, routers) with visual edges. Unlike Zapier's linear step model, Make's canvas supports:

Parallel execution paths: Routes can fork and process multiple paths simultaneously
Iterator and aggregator modules: Process arrays of items (loop through a list of records, aggregate results)
Error handling paths: Define specific actions when individual module steps fail, rather than failing the entire scenario
Filters and routers: Conditional branching based on data values, applying different logic to different data states

This canvas model means Make scenarios can represent genuinely complex business logic — not just "if webhook fires, do X" but multi-branch, conditional, iterative workflows that model real process complexity. For automation builders with technical instincts but no coding background, it is the most expressive visual automation tool available.

Integration Library#

Make connects to more than 1,500 applications through a combination of:

Native app modules: Pre-built, Make-maintained connectors for major platforms (Salesforce, HubSpot, Google Workspace, Slack, Notion, Airtable, Shopify, Stripe, and hundreds more)
HTTP/Webhook module: Connect to any REST API without a native integration — critically important for connecting to AI APIs (OpenAI, Anthropic, Perplexity, Groq) and custom internal APIs
Custom app development: Make's developer platform allows building proprietary app integrations for internal systems

The HTTP module deserves particular attention: it allows Make to function as an API orchestration layer for any service with an HTTP endpoint. This is how most Make users connect to LLM APIs — by calling OpenAI's API directly through the HTTP module, extracting response JSON, and routing the output into subsequent automation steps.

AI Modules and OpenAI Integration#

Make has progressively added native AI functionality:

OpenAI Modules: Native Make modules for OpenAI Chat Completions, image generation (DALL-E), audio transcription (Whisper), and embedding generation — without requiring the HTTP module for setup. These modules simplify authentication, handle JSON parsing automatically, and are maintained by Make as OpenAI updates its API.

Text Parser and Data Transformer Modules: Built-in text processing capabilities (regex extraction, string manipulation, JSON parsing) that work alongside AI module outputs to extract structured data from unstructured LLM responses — implementing basic structured output patterns without requiring the LLM to produce perfect JSON.

Vector Store Integrations: Native integrations with Pinecone, Weaviate, and Qdrant allow Make scenarios to implement retrieval-augmented generation patterns: embed a document, store in a vector database, retrieve relevant chunks on user query, and pass to an LLM for generation — all connected in a visual scenario.

These capabilities allow Make to serve as an accessible implementation environment for RAG pipelines and multi-step AI workflows that would otherwise require Python scripting or dedicated agent framework infrastructure.

Scheduling and Event Triggers#

Make scenarios execute via multiple trigger mechanisms:

Scheduled runs: Scenarios run at configured intervals (every minute to every day) for batch processing workflows
Instant webhooks: External systems trigger scenario execution via webhook — enabling event-driven architectures where Make processes data in real time as it arrives
Watch triggers: Many native app modules support change detection (watch for new emails, new CRM records, new form submissions)
Manual execution: On-demand scenario runs for testing and ad-hoc processing

The instant webhook trigger is particularly important for AI workflows: a customer submits a form, the webhook fires, Make calls an LLM to classify or enrich the submission, and writes the result back to a CRM — all in seconds.

Data Store (Built-in Database)#

Make includes a lightweight built-in database called Data Stores — key-value storage that scenarios can read and write to persist state across executions. This primitive persistence layer allows simple agent-like behaviors: tracking whether a record has been processed, storing conversation history for multi-turn interactions, or caching external API results.

Data Stores are not a production database substitute, but they enable stateful automation patterns without requiring an external database for simple persistence needs.

Error Handling and Monitoring#

Make provides scenario-level error handling through dedicated error routes — visual branches that execute only when upstream modules fail. This allows scenarios to implement retry logic, send error notifications, log failures to external monitoring, or take recovery actions without scenario failures causing data loss.

The monitoring dashboard shows scenario execution history, error logs, data volume processed, and operational consumption (the billing unit). For operations teams running production automation workflows, the execution log provides sufficient observability for standard reliability monitoring.

Pricing and Plans#

Make's pricing is operations-based — each module execution in a scenario consumes "operations," and plans are differentiated by monthly operation limits and feature access:

Free Plan:

1,000 operations per month
2 active scenarios
15-minute minimum scheduling interval
Suitable for learning and light personal automation only

Core Plan:

Starting at approximately $9/month for 10,000 operations
Unlimited active scenarios
1-minute minimum scheduling interval
Suitable for individual users with moderate automation needs

Pro Plan:

Starting at approximately $16/month for 10,000 operations (higher limits at higher price points)
Priority scenario execution
Custom variables and data stores with higher limits
Team collaboration features

Teams Plan:

Starting at approximately $29/month (base)
Multi-user team workspaces
Scenario ownership and sharing controls

Enterprise Plan:

Custom pricing negotiated with Make sales
Dedicated infrastructure options
SSO/SAML integration
Advanced audit logging
SLA guarantees
HIPAA compliance options (relevant for healthcare automation)

The operations-based model means AI-heavy workflows can consume operations quickly — each module in a scenario, including LLM API calls, counts as one or more operations. Scenarios calling LLMs with long response chains should be benchmarked for operation consumption before assuming plan limits are sufficient. See the n8n vs Make vs Zapier comparison for a detailed pricing model comparison across platforms.

Strengths#

1. Most Expressive Visual Automation Canvas
Among major no-code/low-code automation platforms, Make's canvas supports the most complex workflow logic — parallel branches, iterators, aggregators, error paths — without dropping into code. This expressiveness enables automation patterns that Zapier's linear model cannot represent.

2. HTTP Module Universality
The ability to call any REST API through the HTTP module effectively makes Make's integration library unlimited. Any AI service, internal API, or emerging platform with an HTTP endpoint can be integrated into Make scenarios without waiting for a native module — critical for teams using newer LLM providers or custom internal services.

3. European Data Residency and GDPR Alignment
For EU-based enterprises and organizations processing EU personal data, Make's European infrastructure and privacy controls offer compliance alignment that US-headquartered automation platforms require more configuration to achieve.

4. Cost-Effective for Complex Automation
Compared to building equivalent automation logic with custom code or managing self-hosted n8n infrastructure, Make's paid plans offer significant cost efficiency. Organizations replacing manual processes costing staff hours per week find compelling ROI even at Pro or Teams tier pricing.

5. Active Development Pace
Make has shipped AI modules, new native integrations, and scenario performance improvements at high velocity since the Celonis acquisition. The platform roadmap has maintained relevance as AI automation has become central rather than peripheral to automation platform value.

Limitations#

1. Not a Dedicated Agent Platform
Make builds automation workflows with AI steps — it does not implement autonomous agent reasoning loops, tool selection, or multi-step planning natively. For genuine autonomous agent use cases (agents that independently decide what to do next based on environment state), dedicated platforms like Amazon Bedrock Agents or LangGraph are more appropriate. The human-in-the-loop and autonomous decision-making patterns that define modern agents are not Make's primary design target.

2. Scenario Complexity Ceiling
Very large scenarios with dozens of modules become difficult to maintain visually. The canvas does not scale gracefully to enterprise-grade workflow complexity — organizations managing hundreds of interconnected scenarios need disciplined naming conventions and modular design discipline that the platform does not enforce.

3. Execution Speed Constraints
Make's scenario execution is not real-time in the way a deployed API is real-time. Even with instant webhooks, scenario execution overhead (module initialization, connection handling) adds latency compared to code running on dedicated compute. Latency-sensitive applications (real-time customer chat, sub-second API responses) are not well-served by Make's execution model.

4. Version Control and Collaboration Gaps
Make lacks native Git integration or scenario version control. Enterprise teams managing production scenarios need external version control processes — exporting scenario blueprints manually or using Make's limited built-in version history. This is a meaningful operational gap for teams applying standard software development practices to automation.

Ideal Use Cases#

Content Operations Automation
Marketing and content teams automating content enrichment, classification, distribution, and performance tracking — calling LLM APIs to generate summaries, extract keywords, classify sentiment, and route content to appropriate downstream channels.

AI-Augmented CRM and Sales Operations
Sales operations teams enriching inbound leads with AI-generated research summaries, scoring, and personalized outreach drafts — triggered by CRM webhook events and writing results back without human involvement.

Data Enrichment Pipelines
Operations teams processing large volumes of unstructured data (emails, support tickets, form submissions) through LLM classification steps and routing structured outputs to databases, spreadsheets, or downstream systems.

Multi-Tool Workflow Orchestration
Small to mid-size businesses and startups orchestrating complex workflows across many SaaS tools — where the cost and complexity of dedicated integration infrastructure is not justified but the workflow logic exceeds Zapier's single-path model.

Getting Started#

Prerequisites:

Make account (free tier available at make.com)
API credentials for services to be integrated
OpenAI or other LLM API key if using AI modules

High-Level Approach:

Create a Make account and start with a template from the Make Template Library for your use case
Build a simple trigger-action scenario to understand the canvas model before adding complexity
Add the OpenAI module (or HTTP module for other LLM APIs) as an action step in your workflow
Configure data mapping between preceding module outputs and the LLM prompt template
Parse LLM response output using the JSON Parser or Text Parser module for structured data extraction
Add routing and error handling logic for production reliability
Set scheduling or webhook triggers and test with live data in Make's scenario debugger

Make's learning curve for complex scenarios is real — invest time in the official Make Academy and scenario templates before building complex production workflows. The AI automation workflow guide demonstrates applied workflow patterns applicable to Make's execution model.

How It Compares#

vs. Zapier:
Zapier wins on simplicity and breadth of native integrations for simple one-trigger-one-action automations. Make wins on workflow complexity, conditional logic, iteration, and data transformation for multi-step workflows. Advanced automation users typically migrate from Zapier to Make when workflow complexity outgrows Zapier's linear model. See the n8n vs Make vs Zapier comparison for a detailed feature and pricing breakdown.

vs. n8n:
n8n offers greater flexibility through JavaScript node execution, self-hosting options, and deeper technical customization — but requires more technical capability to operate effectively. Make wins on accessibility and visual experience; n8n wins on programmability and self-hosting economics at scale. Organizations with in-house technical teams often choose n8n for the control; organizations without dedicated automation engineers often choose Make for the balance of power and accessibility.

vs. Dedicated AI Agent Platforms:
Platforms like Amazon Bedrock Agents or Salesforce Agentforce are purpose-built for autonomous AI agent reasoning with memory, planning, and tool orchestration. Make is purpose-built for visual automation workflow orchestration. For production autonomous agents with complex reasoning requirements, dedicated agent platforms are more appropriate. For automation workflows that include AI as one of many processing steps, Make is well-suited. The open-source vs commercial agent frameworks comparison provides context on when to build vs. buy in the agent space.

Bottom Line#

Make occupies a practical sweet spot for AI-augmented automation: sophisticated enough to build genuinely complex workflows with conditional logic, iteration, and multi-system integration, yet accessible enough that non-developers can build and maintain production automation with reasonable investment in platform learning.

Its role in the AI automation landscape is clearer when understood correctly: Make is an automation orchestration platform that can include AI steps, not an AI agent platform that can include automation steps. For many real business use cases — content pipelines, data enrichment, CRM augmentation, multi-system workflow coordination — this distinction is academic. The workflow achieves the same outcome either way.

The platform's European data residency, freemium accessibility, HTTP module universality, and visual canvas expressiveness make it one of the more versatile platforms for teams that need more than Zapier's simplicity but fewer than n8n's infrastructure demands.

Enterprise buyers evaluating Make for large-scale production automation should focus evaluation on: operation consumption modeling for their expected workflow volumes, scenario complexity management practices for the scenarios they intend to build, and the gap between Make's collaborative features and their team's software development process expectations.

Measure the business impact of your automation programs using the AI agent ROI measurement framework to build a consistent case for continued investment.