Directory Data & AnalyticsData Integration & ETLMake (Integromat)

Make (Integromat)

About Make (Integromat)

Make provides a visual platform for building and managing AI agents and automations. The product connects apps, data sources, and AI models. Workflows can be built by prompt, drag-and-drop, or from AI chat tools. Make connects to over 3,500 apps. The company states that Make is part of Celonis. Make has operated since 2015.

Products

Workflow Builder: Builds automations by prompting, drag-and-drop, or MCP.
Observability: Shows a live view of the automation stack, including third-party resources.
Custom code: Allows fine-tuning of scenarios with JavaScript or Python.
MCP Server: Builds and runs automations from Claude, ChatGPT or Gemini.
AI Agents: Lets users set goals, provide tools, define limits, and see the reasoning of agents.
Governance: Manages access and compliance.

Used for

Building AI agents and automations from a visual interface.
Connecting apps, data sources, and AI models into workflows.
Monitoring workflow executions in real time.
Extracting structured text and metadata from files.
Bringing live web data into automations.

Who it is for

Used by teams ranging from startups to global enterprises.
Used by organizations across all verticals and globally.
Offers a plan for businesses with under 20 employees.

From Make (Integromat)'s own website, read 2026-10-23. How we build listings.

How to evaluate Data Integration & ETL

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Make (Integromat). It comes from our Data Integration & ETL/ELT buyer guide.

25%

Connector Breadth & Reliability

Coverage of your actual sources (SaaS apps, databases, files, events), depth of each connector (full vs. partial schemas, history), automated schema-drift handling, custom-connector SDK, and who maintains connectors when an upstream API changes

20%

Transformation Model & Engineering Workflow

ELT (in-warehouse SQL/dbt) vs. ETL (engine-side) vs. visual push-down; version control, testing, CI/CD, and lineage; reusability and modularity of models; and fit with your team’s SQL-vs-Python-vs-low-code balance

20%

Real-Time, CDC & Streaming

Log-based change-data-capture, achievable freshness, replication from operational databases, incremental-load handling, schema-evolution on streams, exactly-once semantics, and reverse-ETL/activation back to operational systems

15%

Governance, Lineage & Data Quality

Column-level lineage across the pipeline, in-line data-quality and validation, catalog/metadata integration, masking and PII handling, audit trails, and master-data or data-contract support for regulated environments

10%

Deployment, Security & Sovereignty

SaaS vs. self-hosted vs. hybrid/customer-VPC, where data and credentials reside, SOC 2 / ISO 27001 / HIPAA posture, private networking and BYO-cloud options, RBAC/SSO, and open-source vs. proprietary lock-in

10%

Cost Model & Predictability

Pricing unit (rows/MAR, capacity, credits, compute-hours, per-connector, per-seat), how cost behaves as volume and source count grow, warehouse-compute cost the tool pushes onto you, and whether spend is forecastable against your data growth

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Quick Facts

www.make.com Pricing page

CategoryData & Analytics

SubcategoryData Integration & ETL

DeploymentCloud (AWS hosting)

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