Make vs Windmill(2026)
Make is better for teams that need much cheaper than zapier. Windmill is the stronger choice if excellent for internal tooling. Make is freemium (from $9/mo) and Windmill is open-source (from $0).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Make
Make (formerly Integromat) offers a visual canvas for building complex multi-step automations with advanced data manipulation, error handling, and branching logic — at a fraction of Zapier's cost.
Starting at $9/mo
Visit MakeWindmill
Windmill is an open-source developer platform to build internal tools, workflows, and scripts. Write scripts in Python/TypeScript, chain them visually, and share with your team.
Starting at $0
Visit WindmillHow Do Make and Windmill Compare on Features?
| Feature | Make | Windmill |
|---|---|---|
| Pricing model | freemium | open-source |
| Starting price | $9/mo | $0 |
| Visual workflow canvas | ✓ | — |
| 1,500+ integrations | ✓ | — |
| Complex data mapping | ✓ | — |
| Error handling routes | ✓ | — |
| Webhooks | ✓ | — |
| HTTP module | ✓ | — |
| Scenario scheduling | ✓ | — |
| Open source | — | ✓ |
| Script editor (Python/TS/Go/Bash) | — | ✓ |
| Visual flow builder | — | ✓ |
| Auto-generated UIs | — | ✓ |
| Job queuing | — | ✓ |
| Secret management | — | ✓ |
| Self-hostable | — | ✓ |
Make Pros and Cons vs Windmill
Make
Windmill
Deep dive: Make
When to choose Make
Make is the right choice when the team needs Zapier-level integration breadth at a fraction of the cost, and when workflows involve non-trivial data transformations, branching, or error handling. Its visual canvas with drag-and-drop modules, routers, and iterators makes complex multi-path workflows significantly easier to build and debug than the equivalent in Zapier, which flattens everything into a linear step chain. Make is strongest for teams that sit between no-code users and full developers: comfortable enough to map JSON fields and write basic expressions, but not wanting to manage infrastructure. The built-in HTTP module and JSON parser mean most API integrations that lack a native module can still be wired up without external tools. Choose Make when the workflow complexity or task volume would make Zapier prohibitively expensive. Avoid it when the team is entirely non-technical and needs the simplest possible UX, or when the integration catalog must include niche vertical SaaS tools that only Zapier supports.
Real-world use case
An e-commerce team running a Shopify store uses Make to automate order fulfillment notifications, inventory sync, and customer feedback collection. A new order triggers a scenario that branches based on shipping destination: domestic orders route to a local 3PL API via HTTP module, international orders route to a different fulfillment partner. Both branches then update a Google Sheet inventory tracker, send a WhatsApp notification to the warehouse team via the Twilio module, and create a follow-up email in Brevo scheduled for 7 days post-delivery. The scenario has 12 modules across 3 branches and runs approximately 3,000 times per month. On Make Core at per month with 10,000 operations, this is comfortably within plan. The equivalent Zapier setup would consume roughly 36,000 tasks per month at 12 steps per trigger, pushing the cost past per month. The tradeoff: the initial build took the team about 4 hours compared to a likely 2 hours in Zapier, because Make requires explicit data mapping between modules where Zapier automatically surfaces fields from previous steps.
Hidden gotchas
Operations counting differs from Zapier tasks but is not always cheaper in edge cases. Each module execution counts as one operation, including filters that stop the flow. However, iterators that loop over an array count one operation per array element per downstream module, which can cause a single webhook trigger to consume hundreds of operations if it processes a list. Teams that automate batch events like processing all line items in an order must model the operation count as trigger_count multiplied by average_items multiplied by modules_after_iterator. The visual editor, while powerful, has a learning curve: routers, aggregators, and iterators are concepts that do not exist in Zapier and require understanding data flow rather than just connecting apps. Error handling routes are a major differentiator but must be explicitly configured per module. Without them, a module failure silently stops the scenario branch, and the team discovers missing data hours later. The built-in data store is limited to 10,000 rows on most plans and uses a proprietary query interface rather than SQL. Webhook response timing is tight: if a webhook trigger needs to return a response to the calling system, the entire scenario must complete within 40 seconds. Complex scenarios that exceed this window require an asynchronous pattern with a separate confirmation webhook, which the documentation mentions but does not walk through clearly.
Pricing breakdown
The free plan includes 1,000 operations per month and 2 active scenarios. The Core plan at per month includes 10,000 operations and unlimited active scenarios. The Pro plan at per month includes 10,000 operations plus priority execution and full-text log search. A team running 10 scenarios averaging 500 executions per month with 5 modules each consumes 25,000 operations monthly, requiring the Core plan with an add-on pack or the Teams plan at per month with 10,000 base operations plus purchased operation packs at per additional 10,000. Total cost would land around per month. The same workload on Zapier Professional would cost approximately per month in tasks.
Deep dive: Windmill
When to choose Windmill
Windmill is the right pick when the team needs to build internal tools, background jobs, and workflow automations in a code-first environment with the bonus of auto-generated UIs from scripts. It is strongest for engineering teams that already write Python, TypeScript, Go, or Bash scripts to automate internal processes and want a managed execution environment with a visual flow builder, job queuing, and secret management baked in. Unlike n8n, which leans toward SaaS-to-SaaS integrations, Windmill leans toward infrastructure automation, ETL pipelines, and internal tooling. The auto-generated UI feature turns any script into a shareable internal app with form inputs and output display, eliminating the need for a separate Retool or Appsmith deployment for simple admin tasks. Choose Windmill when the team's automation needs are more about running code reliably than connecting marketing SaaS tools. Avoid it when the team is non-technical or when the primary use case is connecting Salesforce to Slack.
Real-world use case
A data engineering team at a 50-person company deploys Windmill on their Kubernetes cluster to replace a collection of cron jobs and ad-hoc Python scripts. One flow pulls data from three PostgreSQL databases nightly, transforms it in a Python step using pandas, and writes the results to a Snowflake data warehouse. Another flow generates a weekly PDF report from a TypeScript script and emails it to stakeholders via the Resend API. The auto-generated UI lets the finance team trigger an ad-hoc report with custom date parameters without filing an engineering ticket. Total cost: /bin/zsh for the self-hosted community edition. The tradeoff: the team spent two days migrating existing cron jobs to Windmill flows, writing the YAML definitions, and configuring secret management. The Windmill-specific syntax for input/output schemas and resource types required reading documentation that is less extensive than n8n's or Zapier's.
Hidden gotchas
The learning curve for Windmill's type system and resource model is steeper than it appears. Every script must declare its input parameters with types, and the auto-generated UI reflects these types. Getting the type annotations right for complex nested objects requires understanding Windmill's custom type syntax, which differs from standard TypeScript or Python type hints. The flow builder uses a YAML-based definition that can be version-controlled, which is a strength, but editing flows in the UI and then exporting to YAML can produce merge conflicts when multiple team members work on the same flow. Job queue priority and concurrency limits are configurable but default to values that may not suit high-throughput workloads. Workers run in isolated environments, and the cold start for a Python worker that needs to install dependencies can take 10-30 seconds on the first execution. The SaaS integration library is intentionally minimal: Windmill expects developers to use HTTP requests or the native language SDKs rather than providing pre-built connectors for every SaaS tool.
Pricing breakdown
Windmill's free Community plan includes 1,000 executions/mo. The Team plan at $10/user/mo includes unlimited executions. Enterprise is custom-priced. Self-hosted is free and open-source under AGPLv3. The unique value: Windmill handles scripts (Python, TypeScript, Go, Bash), flows, and apps in one platform — replacing separate tools for automation, internal tooling, and workflow orchestration. A self-hosted instance costs $5-20/mo for infrastructure. The cost comparison: at 10K+ automations/mo, Windmill's unlimited Team plan ($10/user) is dramatically cheaper than Zapier ($49/mo for 2K tasks) or Make ($9/mo for 10K ops).
Should You Use Make or Windmill?
For most teams, Windmill is the better default: it offers excellent for internal tooling and is open-source (from $0). Choose Make instead if much cheaper than zapier matters more than steeper learning curve. There is no universal winner — the right pick depends on your budget, team size, and whether you value much cheaper than zapier or excellent for internal tooling more.
Choose Make if…
- •Much cheaper than Zapier
- •Powerful data transformation
- •Visual debugger
Choose Windmill if…
- •Excellent for internal tooling
- •Auto-generates UIs from scripts
- •Free self-hosted