“The next unicorn will not write more code. It will wire more tools together.”
The market is quietly voting with its wallet: no-code automation is not a side project anymore, it is a core growth lever. Zapier and Make (formerly Integromat) now sit on top of thousands of SaaS products, pulling the strings in sales, marketing, support, finance, and operations. For a startup, the choice between Zapier and Make can change your burn rate, your hiring plan, and how fast you can test new revenue experiments. This is not just about features. It is about how you compound operational leverage over the next 24 months.
The question founders ask in board meetings sounds simple: “Should we standardize on Zapier or Make for automation?” A better version is: “Where do we want our automation brain to live, and what does that mean for speed, cost, and reliability as we grow from 0 to 1, then 1 to 10?” Zapier sells convenience to business teams. Make sells control to operations and product-minded teams. Both eat into the same budget: engineering time, ops headcount, and SaaS spend.
The trend is not clear yet, but early signals point to a split market. Startups under $2M ARR skew toward Zapier because onboarding is fast and non-technical teams can ship automations in a few hours. Product-led and API-heavy teams, especially in Europe, adopt Make when workflows reach 10+ steps, branch heavily, or touch finance and data infrastructure. Investors look at this choice as a proxy for how a team thinks about process: do they accept higher SaaS spend for speed, or trade time now for cost savings and control later.
Both tools promise the same headline value: “Connect apps, move data, save time.” The business value hides in the details: trigger reliability, error handling, time to ship a new flow, cost per 1,000 runs, and how painful it is to maintain things after your first operations hire walks in and inherits 150 legacy automations. The ROI lives in fewer context switches, lower payroll for repetitive work, faster response to leads, and tighter data loops between departments. If you get the tool choice wrong, you do not just waste subscription fees. You slow down iteration on your go-to-market engine.
Where Zapier and Make Came From (And Why That Matters)
Investors care about origin stories because they predict product direction. Zapier was born in the US, selling to marketers and small businesses that needed simple “if this then that” flows. Make came out of a more technical European scene with a focus on complex data flows and visual logic. That DNA still shows up in how both tools feel when you build automations.
Zapier pushed hard into breadth of integrations and ease of setup. Its strategy: be everywhere, for everyone, and remove as many decisions from the user as possible. That is why you see so many templates, prebuilt “Zaps,” and plain language descriptions. The company optimizes for the non-technical marketer or founder who wants something working before lunch, not a perfect system next month.
Make aimed at depth and control. The interface looks more like a visual programming board. Instead of hiding complexity, it puts logic, branching, and data transformation front and center. This pulls in operations managers, data-minded founders, and engineers who do not want to write code for every glue task, but still want strong control over how data moves.
The business value of these different directions is clear:
“Zapier is trying to be the spreadsheet of automation. Make is trying to be the Figma of automation logic.”
Spreadsheets spread because they lower the bar. Figma spread because it let power users collaborate while staying close to the craft. In automation, Zapier plays the spreadsheet role. Make plays the Figma role.
Retro Specs: How Automation Looked Around 2005
To understand why Zapier and Make matter for your startup, it helps to look back at how teams wired tools together around 2005, before “no-code automation” was a phrase you saw in pitch decks.
Back then, a sales ops workflow might look like this:
– PHP scripts on a shared server polling an IMAP inbox every 5 minutes
– Cron jobs pushing CSVs into a MySQL database
– A desktop Outlook plugin exporting contacts for manual import into a CRM
– Custom VBA scripts in Excel for basic data cleaning
If you wanted Salesforce to talk to your billing system, you either bought an expensive middleware product or hired a consultant to build and maintain custom integrations. Startups rarely did this. They hired people to copy data instead.
“In 2005, automation meant you had one person in IT who knew how the cron jobs worked. If they left, no one touched anything for fear parts of the company would stop working.”
User reviews from that period reflect the frustration. Here is a paraphrased view from a tech forum circa 2005, about trying to sync tools:
“I just need leads from our website to go into GoldMine and then into QuickBooks. Right now someone prints emails and retypes them twice. Every ‘integration’ quote we get is five figures and takes months.”
For small teams, the cost of wiring tools together in 2005 was so high that many never automated at all. The cost was not just in money. It lived in risk and in the knowledge bottleneck around one or two “script people.”
Then vs Now: Automation Stack
Here is a simple comparison that shows how far things moved.
| Automation Aspect | 2005 Typical Setup | 2025 Typical Setup (Zapier / Make) |
|---|---|---|
| Integration method | Custom scripts, cron jobs, vendor-specific plugins | Visual workflow builder, hosted connectors to hundreds of apps |
| Owner | IT or one “script person” | Marketing, sales, ops, founders, plus engineering for complex flows |
| Typical cost | Thousands upfront, weeks of work | Subscription from free tier up to a few hundred per month to start |
| Error handling | Manual log checks, email alerts from broken scripts | Built-in run history, alerts, automatic retries, visual debugging |
| Change cycle | Change request, developer time, test, deploy | Non-technical edit, test in UI, go live in minutes |
This shift is why investors now ask how you automate. Ten or twenty hours a week of manual glue work per employee adds up quickly. Automation platforms reduce that without asking you to hire backend engineers for every internal workflow.
Zapier vs Make: Core Product Philosophy
Zapier: Opinionated Simplicity
Zapier wraps its product around a simple object: the “Zap.” A Zap usually has:
– One trigger
– One or more actions
– Optional filters or simple paths
The platform pushes you toward linear flows. A lead fills a form. That triggers a Zap. The Zap sends lead data to your CRM, your email tool, and your Slack channel. You might have one or two branches, but the mental model stays “when X happens, do Y and Z.”
The market response to this philosophy has been strong in SMB and mid-market segments. Non-technical teams ship working automations quickly. Hiring managers like this because:
– They do not need a dedicated automation engineer early on
– They can hand Zapier projects to marketing ops or sales ops
– Onboarding new team members is quick because the UI is simple
The business value is speed. New lead sources, new tools, and new campaigns plug in quickly. That speed translates into more experiments per quarter, which often translates into faster growth.
Make: Visual Logic and Control
Make describes automations as “scenarios.” A scenario is a visual map where each node is a module (an app or a function) and lines represent data flow. You can:
– Branch heavily
– Loop over arrays
– Map complex JSON payloads
– Insert routers, aggregators, and iterators
The interface trades immediate simplicity for long-term clarity when things get complex. It feels closer to visual programming than to “recipes.”
This draws a different buyer:
– Operations managers who think in systems
– Engineers who do not want lock-in to one vendor logic model
– Data-minded founders who care about field-level transformations
The business value here is in control per dollar. You can often run more complex workflows at a lower cost point, with better control of how each field travels through your system.
Expert Opinions: Who Picks What, And Why
“For non-technical revenue teams that just want leads moving, Zapier is easier to support. Once a startup hits 50+ employees and begins to care about internal tools as a product, Make starts to win more deals.” – Growth advisor at a B2B SaaS VC
“Make feels closer to how we think about data flows as engineers. We can map arrays, handle complex webhooks, and keep logic transparent. Zapier is fine for simple stuff, but our billing workflows would be unreadable there.” – Staff engineer at a fintech startup
“Zapier got us from zero to functioning operations without hiring another developer. When our volume scaled and our workflows became more like mini-apps, we started running cost models and Make looked better at high run counts.” – COO at a DTC subscription brand
These views point to a pattern: the “right” platform changes as volume, complexity, and team composition change.
Zapier vs Make: Pricing Models Then vs Now
Zapier and Make both started with simple subscription tiers and per-task or per-operation limits, then expanded toward higher-volume, enterprise-style contracts.
Here is a simplified comparison that highlights the business trade-offs. Pricing changes often, but the structure looks roughly like this.
| Factor | Zapier (2025 snapshot) | Make (2025 snapshot) |
|---|---|---|
| Free tier | Limited Zaps, lower update frequency | Limited operations, some advanced features accessible |
| Billing unit | “Tasks” (every successful step counts) | “Operations” (every module execution counts) |
| Entry paid plan | Roughly tens of dollars per month | Similar low starting price; often more operations included |
| High-volume plans | Higher per-task cost at scale; strong support | Often lower per-operation cost at high volume; complex plans |
| Overage risk | Many small tasks can burn limit quickly | Complex scenarios can multiply operations unexpectedly |
| Typical SMB spend | From free up to mid-three figures monthly | From free up to mid-three figures monthly |
For a founder, the key question is not just “which is cheaper?” The better question is “how predictable is our automation spend if we grow 5x in 12 months?”
– Zapier tends to be easier to reason about for simple, linear workflows across many tools
– Make can be cheaper for dense, complex workflows where you pack a lot of logic into each scenario
If your business model counts gross margin in dollars per automation run, you must model this out early.
Workflow Design: Simple vs Complex Use Cases
When Zapier Shines
Zapier performs well when:
– You have many different apps, each with modest volume
– Each workflow has 2 to 5 steps
– You want non-technical team members to build and own flows
Examples:
1. New leads
– Trigger: New form submission in Typeform
– Actions:
– Create contact in HubSpot
– Add row to Google Sheet for tracking
– Send Slack alert to sales
2. E-commerce notifications
– Trigger: New order in Shopify
– Actions:
– Send Discord message to community
– Add buyer to email list in Klaviyo
– Create a support ticket if order value crosses a threshold
3. Internal alerts
– Trigger: New bug in Jira with priority P0
– Actions:
– Post to engineering Slack channel
– Create incident channel
– Add entry to “Incidents” Airtable base
For these flows, the business value is very direct: faster reaction times, fewer missed leads, and better visibility. Zapier templates make setup fast, which reduces time-to-value.
When Make Shines
Make excels when workflows:
– Have complex branching logic
– Need loops or array processing
– Handle heavy JSON payloads
– Touch internal databases or data warehouses
Examples:
1. Finance reconciliation
– Trigger: Daily schedule
– Scenario:
– Fetch transactions from Stripe, PayPal, and your bank feed
– Normalize fields into a single schema
– Match against your internal orders database
– Flag mismatches and push them into an Airtable review queue
– Send summary report to finance Slack channel
2. Enrichment and routing
– Trigger: New signup event from your product via webhook
– Scenario:
– Call Clearbit or other enrichment service
– Branch by company size and industry
– Write enriched data to your CRM, analytics tool, and data warehouse
– For certain customer segments, auto-create tasks in your sales engagement tool
3. Complex data transforms
– Trigger: CSV uploaded to Google Drive
– Scenario:
– Parse hundreds of rows
– Clean fields, split names, standardize country codes
– Batch insert into your internal API
– Handle retries for partial failures
In these cases, the visual scenario map in Make provides long-term clarity. New team members can see each branch and transformation in one canvas. That helps during audits and when you want to treat automation as part of your product.
Reliability, Debugging, and Vendor Risk
No automation tool is perfect. Both Zapier and Make experience outages, connector quirks, and vendor API changes. The question is how each platform helps you handle that risk.
Zapier Reliability Profile
Zapier focuses on:
– Simplified run logs for non-technical users
– Emails when Zaps fail repeatedly
– Built-in retries for certain errors
– “Zap history” where you can inspect each task
This works well for teams that mainly want to know: “Did this lead get into HubSpot?” without going too deep into HTTP status codes.
Risk areas:
– Many Zaps built by many people can lead to sprawl
– Old Zaps may break silently when app APIs change, if no one maintains them
– Debugging dense, multi-step Zaps with many nested paths can still feel cramped
Make Reliability Profile
Make leans into:
– Detailed scenario run history with step-level data
– Visual replay of runs
– Clear handling of errors at each module
– Tools for partial success and “continue on error” logic
This gives more confidence to technical owners who want to trace data at each hop. For workflows that touch billing, compliance, or analytics, that level of visibility can be the difference between a quick fix and a week of chaos.
Risk areas:
– Power users can build extremely complex scenarios that are hard to understand for new team members
– High operation counts per run can trigger pricing surprises
– Advanced modules and custom API calls raise the bar for non-technical users
From a business risk view, Make often suits teams that treat automation as infrastructure. Zapier suits teams that treat automation as flexible glue.
Team Structure: Who Owns Automation?
Your choice between Zapier and Make often mirrors who you expect to own automation in your company over the next two years.
Marketing- and Sales-led Teams
For many SaaS and e-commerce startups:
– Marketing runs a growing stack: ads, landing pages, email, analytics
– Sales runs CRM, outreach tools, call tracking
– RevOps is a part-time hat for someone until later
In this setup, Zapier tends to win because:
– The learning curve is lower for non-technical staff
– There is a huge library of recipes that look like “connect this marketing tool to that CRM”
– Vendors ship Zapier integrations first in many cases
The trade-off: you might accept higher per-task costs and less granular control in exchange for “it just works for the team we have today.”
Product- and Data-led Teams
Founders who care deeply about product, engineering, and data from day one often set a different tone:
– They centralize event tracking
– They invest early in a data warehouse and internal APIs
– They see internal tooling as a competitive edge
Here, Make often feels closer to the way the team already thinks. Automations might:
– Pull from internal APIs
– Push events into custom systems
– Respect internal data models
The business value is consistency. Instead of each team gluing tools independently, you create a shared automation layer that respects product boundaries.
Integration Coverage: Breadth vs Depth
Both Zapier and Make connect to hundreds, in some cases thousands, of apps. Their marketing pages showcase logos. The more important questions are:
– How deep is the integration for the apps you care about?
– Who maintains it: the platform or the vendor?
– How often do triggers poll, and how fast are instant webhooks?
Zapier often wins on raw count of supported apps and prebuilt “Zaps.” That makes sense given its age and market focus. Many smaller SaaS vendors ship a Zapier connector as their first automation integration. For a startup plugging into niche tools, that coverage can matter more than anything else.
Make invests heavily in depth for each module and in making custom HTTP and webhook modules accessible. If an app is not supported, a technical user can still wire it in via its API, with full control over headers, query parameters, and mapping.
From a business angle:
– If your stack is made of popular SaaS apps, Zapier’s breadth and templates can cut weeks off your launch schedule
– If your stack includes custom tools, internal APIs, or niche B2B software, Make’s “build your own connector” style tools can avoid vendor lock-in
You can also hedge: use Zapier for generic glue and Make for specialized internal workflows. The cost is that you now maintain two platforms, which has its own overhead.
Performance, Latency, and Volume
Automation performance matters when it affects:
– Lead response time
– Customer support SLAs
– Internal reporting and alerts
Both platforms support webhook-based triggers, which fire close to real time. Polling-based triggers run on intervals, which can introduce delays.
In practice:
– For most marketing and sales workflows, a few minutes delay is acceptable
– For fraud checks, payment flows, or real-time product features, you often cannot afford that latency
For those critical workflows, many startups still choose to write custom code or use internal message queues (for example, Kafka, RabbitMQ, or a serverless event bus) and reserve Zapier or Make for lower-stakes tasks around the edges.
The right way to think about it:
– Zapier and Make work well for “near real time” business operations
– They are not a replacement for in-app, real-time features
Volume also plays a role. At low volume, both tools feel cheap and responsive. At high volume, hidden constraints appear:
– API rate limits from connected tools
– Task or operation quotas
– Delays when queues fill during peak hours
Serious teams monitor not just their own metrics, but the health pages and status feeds of their automation vendors.
Data Governance, Security, and Compliance
Investors and enterprise customers now ask early about data security in your automation stack. Both Zapier and Make invest heavily in security features, but you should still check:
– Where data is stored and processed
– Which compliance certifications the vendor holds (SOC 2, ISO 27001, etc.)
– How access control works for team members
– How secrets (API keys, tokens) are handled
From a practical view:
– If your automation touches health data, financial data, or regulated personal data, you need your legal and security teams in the loop before standardizing on any third-party tool
– You may decide to keep certain flows in your own infrastructure and use Zapier or Make for less sensitive tasks
The business trade-off lives between risk exposure and development speed. Every founder must set their own line, but investors will ask for a clear logic behind that line.
Then vs Now: Automation Skills Inside Startups
The role of “automation builder” inside a company has changed a lot since 2005.
| Aspect | 2005 Typical Startup | 2025 Startup with Zapier / Make |
|---|---|---|
| Who builds automations | IT admin, one engineer, or external consultant | Marketing ops, sales ops, product ops, engineers, founders |
| Required skills | Scripting (PHP, Perl, bash), server admin | Understanding of tools and processes, basic logic, some API knowledge for advanced flows |
| Knowledge sharing | Tribal knowledge, scripts on servers, sparse docs | Shared workspaces, visual scenarios/Zaps, version comments |
| Bus factor (single point of failure) | High: one person holds critical knowledge | Lower: more people can read and adjust flows, though complexity can reintroduce risk |
| Career path | No clear role; often side work | Dedicated roles: RevOps, Business Automation Engineer, Internal Tools PM |
For a founder, this shift matters for hiring strategy. You can delay certain engineering hires if you bet on strong no-code automation and hire someone who lives between tools and process.
Practical Selection Criteria: Zapier vs Make
Instead of arguing in the abstract, you can phrase the choice as a series of practical questions.
1. Volume and complexity
– Do you expect many simple workflows or fewer complex ones?
– Is your data mostly simple records, or do you work with nested objects and arrays?
2. Team skill mix
– Who will build and maintain automations over the next 12 to 24 months?
– How comfortable are they with logic, APIs, and data types?
3. Stack composition
– Are your core tools mainstream SaaS products with good public integrations?
– Do you rely on internal APIs or niche vendor APIs?
4. Cost sensitivity
– What is your expected growth in automation volume?
– Do you want easier cost modeling for many small tasks, or do you expect heavy processing per workflow?
5. Strategic posture
– Do you treat automation as competitive infrastructure, or as operational glue?
– Do you see yourself building internal tooling as a strategic function?
Your answers will not give a formula, but they will reveal a bias toward one platform.
Running Both: When a Hybrid Approach Makes Sense
Some of the more mature startups and scaleups quietly run both Zapier and Make.
Typical split:
– Zapier: customer-facing, go-to-market workflows that need frequent changes from non-technical teams
– Make: data-heavy or critical internal workflows maintained by operations and engineering
Business reasons:
– You let marketers ship campaigns and experiments without waiting for ops
– You keep billing, analytics, and product data flows under stricter control
– You reduce risk by not tying every process to a single vendor
The cost is complexity:
– Two platforms to secure and monitor
– Two skill sets to hire and train
– Two sets of logs to inspect during incidents
If you go hybrid, you need clear ownership boundaries. For example: “Anything that touches revenue recognition runs in Make. Anything that touches campaign alerts runs in Zapier.”
What Investors Actually Look For
Investors rarely care whether you picked Zapier or Make for its own sake. They care about what that choice signals.
They read:
– How you talk about internal process
– How quickly you can change core flows
– How fragile or resilient your operations are
A team that has:
– Clear automation diagrams
– Sensible naming conventions
– Documented ownership for each key workflow
looks less risky than a team where:
– Multiple tools connect to everything
– No one knows which automation creates which record
– Cost spikes every quarter without explanation
So the tool debate matters less than your discipline in using whichever product you pick.
Where The Market Might Go Next
The trend is not clear yet, but some signals are visible:
– More SaaS vendors add native automation (for example, embedded Zapier-like engines in CRMs and help desks)
– Developers adopt event-driven internal systems, which may overlap with what Zapier and Make handle today
– Data teams push for centralization of business logic in warehouses or internal services
Zapier responds by adding more “native-feeling” features: tables, interfaces, and simple databases. Make pushes toward deeper logic and API control. Both fight to become the central nervous system for operations.
For a startup founder, the risk is not picking the “wrong” winner. The risk is outsourcing so much core knowledge into opaque automations that no one understands how your business actually runs.
If you treat automation as a first-class product concern, you can get strong ROI from either Zapier or Make. If you treat it as magic, you will eventually pay the price in outages, confusion, and hidden costs that creep into every quarterly review.