Pipedream vs Make(2026)
Pipedream is better for teams that need code-first philosophy. Make is the stronger choice if much cheaper than zapier. Pipedream is freemium (from $19/mo) and Make is freemium (from $9/mo).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Pipedream
Pipedream lets developers build event-driven workflows using Node.js, Python, Go, or Bash alongside 2,000+ pre-built integrations. Every step is real code you can inspect and customize.
Starting at $19/mo
Visit PipedreamMake
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 MakeHow Do Pipedream and Make Compare on Features?
| Feature | Pipedream | Make |
|---|---|---|
| Pricing model | freemium | freemium |
| Starting price | $19/mo | $9/mo |
| 2,000+ integrations | ✓ | — |
| Code steps (Node/Python/Go/Bash) | ✓ | — |
| Event sources | ✓ | — |
| SQL queries on data | ✓ | — |
| Built-in state | ✓ | — |
| HTTP triggers | ✓ | — |
| Scheduled workflows | ✓ | — |
| Visual workflow canvas | — | ✓ |
| 1,500+ integrations | — | ✓ |
| Complex data mapping | — | ✓ |
| Error handling routes | — | ✓ |
| Webhooks | — | ✓ |
| HTTP module | — | ✓ |
| Scenario scheduling | — | ✓ |
Pipedream Pros and Cons vs Make
Pipedream
Make
Deep dive: Pipedream
When to choose Pipedream
Pipedream is the right choice when the team is developer-first and wants the flexibility of writing code (Node.js, Python, Go, or Bash) within each workflow step while still having access to a large library of pre-built integrations. It sits between Zapier's pure no-code approach and n8n's self-hosted model: fully managed cloud infrastructure with a generous free tier, but with real code execution at every step. Pipedream's event source architecture, which can subscribe to webhooks, poll APIs, or listen to event streams and buffer events for processing, is a genuine differentiator that no competitor matches well. Choose Pipedream when the team needs to process webhook data with custom logic, query a database in the middle of a workflow, or call internal APIs with complex authentication. The free tier includes 10,000 invocations per day, which is far more generous than Zapier or Make. Avoid Pipedream when the team is non-technical and needs a drag-and-drop interface without touching code, or when the use case requires sub-second latency.
Real-world use case
A developer building a SaaS analytics dashboard uses Pipedream to aggregate data from multiple sources on a schedule. A cron-triggered workflow runs every hour: the first step queries the Stripe API for new charges using a Python code step with the stripe SDK, the second step pulls active user counts from PostHog via HTTP request, the third step merges the data and computes key metrics in a Node.js step, and the final step writes the results to a Supabase table via the built-in integration. The developer also sets up an event source that listens to Stripe webhooks and triggers a separate workflow for real-time churn alerts. The entire setup runs on the free tier because it processes fewer than 10,000 invocations per day. The tradeoff: the free tier has a 30-second execution timeout per step and 256 MB memory per step. A workflow that needs to process large CSV files or run heavy computations hits these limits and requires the paid plan at per month for extended resources.
Hidden gotchas
The free tier includes generous invocation limits but has a 30-second timeout per step and 256 MB memory per step, which is not enough for batch processing or data-heavy operations. Upgrading to the paid plan increases these limits but adds per-invocation pricing above the included quota. Cold starts on the free tier can add 1-3 seconds of latency to the first invocation after a period of inactivity, which matters for webhook-triggered workflows that need to respond quickly. The event source abstraction is powerful but can be confusing: an event source is a persistent listener that runs independently of workflows, consuming resources even when no workflow is subscribed to it. Orphaned event sources continue to run and consume quota. The workflow builder UI is functional but less polished than Zapier or Make for complex multi-branch logic. SQL queries on workflow data use a built-in SQL engine that only supports a subset of SQL syntax, and joins across data stores are not supported. Code steps have access to the workflow's data store but not to NPM packages beyond the pre-installed set, unless the team uses a custom Docker image on the paid plan.
Pricing breakdown
The free plan includes 10,000 invocations per day (roughly 300,000 per month) with 30-second timeout and 256 MB memory per step. The Basic plan at per month includes extended timeouts, more memory, and 10,000 daily invocations with higher resource limits. The Advanced plan at per month adds team features and custom domains. A team running 5 workflows with an average of 3 steps each, triggered 500 times per day, consumes 1,500 invocations daily, well within the free tier. The same workload on Zapier would consume 45,000 tasks per month, costing approximately on the Professional plan.
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.
Should You Use Pipedream or Make?
For most teams, Pipedream is the better default: it offers code-first philosophy and is freemium (from $19/mo). Choose Make instead if much cheaper than zapier matters more than less suitable for non-developers. There is no universal winner — the right pick depends on your budget, team size, and whether you value code-first philosophy or much cheaper than zapier more.
Choose Pipedream if…
- •Code-first philosophy
- •Generous free tier
- •Real-time event sources
Choose Make if…
- •Much cheaper than Zapier
- •Powerful data transformation
- •Visual debugger