Toffu vs Make: Conversational AI vs Visual Workflow Builder
Marketing automation has hit a fork in the road. On one side, you've got traditional visual workflow builders like Make that make you map out every single step in detailed diagrams. On the other side, conversational AI tools like Toffu let you just describe what you want in plain English and handle all the technical mess behind the scenes.
I've watched too many marketing teams burn out trying to wrangle Make's operation-based pricing and endless debugging sessions. It's become obvious that the future belongs to systems that actually understand what you're trying to accomplish, not ones that force you to become a technical architect.
Here's why smart marketers are ditching visual workflows for prompt-based automation that just works.
What Makes Make Different (And Frustrating)
Make (formerly Integromat) is a visual automation platform that connects over 1,000 apps through drag-and-drop. You build "scenarios" by connecting modules on a canvas, creating flowcharts that run automated tasks when something triggers them.
Here's how a typical Make workflow goes:
- Something triggers the scenario (new form submission, email received, etc.)
- Data flows through various modules that transform, filter, or route information
- Actions happen across different apps and services
- The workflow finishes or branches into multiple paths
Make calls itself "no-code" automation, but in practice, complex scenarios still require understanding data structures, error handling, conditional logic, and API limitations - you're just working with visual interfaces instead of traditional code.
The Hidden Costs That Nobody Talks About
After digging through Make's community discussions and user feedback, some pretty ugly pain points emerge that Make's marketing definitely doesn't advertise:
Operations-Based Pricing Will Bankrupt Your Budget
Make charges you for every single operation - each module execution eats into your monthly limit. This creates this twisted situation where debugging costs the same as actual production runs.
Operation based pricing is unbelievably stupid. Writing a = 1 costs me the same as querying 100k cells from a database. This makes any remotely structured approach (error handling, data validation, separation of concerns) extremely operation inefficient.
People are literally burning through their entire monthly operation limits just trying to debug scenarios. It makes developing anything complex ridiculously expensive.
Debugging Becomes a Money Pit
Unlike normal development where you can test and iterate for free, every debugging attempt in Make costs you operations. This creates this horrible cycle:
- Scenarios break in production
- Debugging costs operations
- Teams get scared to test thoroughly
- More bugs slip through
I have consumed several times a month's worth of operations just by debugging a scenario.
The "No Split and Merge" Deal Breaker
One of the biggest complaints is Make's complete inability to split data flows and merge them back together - which is pretty basic functionality for most business workflows.
Error handling per a node like they have it is horrible. Also not being able to split and merge back together for workflows.
Other Fun Problems Include:
- Impossible to search for help: The generic name "Make" makes googling solutions a nightmare
- Terrible error handling: Error messages are vague and troubleshooting is painful
- No auto-save: Lose your work and start from scratch
- Deployment hell: Moving scenarios between environments breaks connections and settings
- Documentation gaps: Complex scenarios have zero proper examples
That it's called 'make.' Impossible to google
How Toffu Just Fixes All This
Toffu takes a completely different approach. Instead of forcing you to map out technical workflows, you just describe your marketing goals in normal English. The AI handles all the technical complexity - data connections, transformations, error handling, optimization, everything.
For example, instead of spending hours building a complex Make scenario with dozens of modules to analyze competitor content, you'd just say:
"Track our top 5 competitors' blog publishing frequency, analyze their most engaging content themes, and send me a weekly report with actionable insights for our content strategy."
Toffu automatically:
- Figures out the right data sources
- Handles all API connections and rate limiting
- Processes and analyzes everything
- Generates insights and recommendations
- Delivers results however you need them
No modules to configure, no operations to count, no debugging sessions that drain your budget.
Why Conversational Automation Actually Works
After switching from visual workflow builders to Toffu's prompt-based approach, the differences are night and day:
Zero Technical Overhead
You don't need to learn REST APIs, webhook configurations, or data transformation syntax. If you can write a text message, you can create powerful automations.
Implementation in Seconds
While Make scenarios take hours or days to build and test, Toffu implementations happen instantly. Need to change something? Just clarify your request - no rebuilding required.
No Operations Anxiety
Unlike Make's pricing that punishes experimentation, Toffu encourages you to try different approaches, test variations, and optimize without worrying about burning through limits.
Smart Error Handling
When Make scenarios break, you have to diagnose issues module by module. When Toffu hits problems, it automatically adapts and finds alternative approaches, often fixing things before you even notice.
Actually Understands Context
Most importantly, Toffu gets the marketing context behind your requests. It doesn't just execute steps - it optimizes for your actual business goals.
AI agents are redefining the automation landscape. Unlike traditional bots that strictly follow predefined scripts, AI agents can interpret context, learn from data and make real-time adjustments.
Real Examples: Same Task, Totally Different Experience
Here's how these platforms handle identical marketing tasks:
Social Media Competitor Analysis
The Make Way:
- Create webhook triggers for each competitor's social accounts
- Build data extraction modules for each platform's API
- Configure data transformation modules to normalize different data formats
- Set up conditional logic to handle API rate limits and errors
- Create aggregation modules to compile data across platforms
- Build reporting modules to format and deliver insights
- Test extensively (burning operations with each test)
- Deploy and monitor for breakages
Total time: 6-10 hours of technical work Operations consumed: 50-100 operations per test cycle
The Toffu Way: "Analyze our top 3 competitors' social media performance across LinkedIn, Twitter, and Instagram for the past month. Compare engagement rates, posting frequency, and content themes. Identify what's working best for each competitor and suggest content opportunities for our brand."
Total time: 30 seconds to write the prompt Operations consumed: None - it's just conversation
Lead Nurturing Automation
The Make Way: Build a complex scenario with multiple branches handling different lead scores, engagement levels, and behavioral triggers. Configure email modules, CRM integrations, scoring logic, and conditional paths. Debug extensively when leads don't flow through expected paths.
The Toffu Way: "Create a lead nurturing sequence that adapts based on engagement levels. Send educational content to cold leads, case studies to warm leads, and schedule sales calls for hot leads. Adjust messaging and timing based on industry and company size."
The pattern's obvious: Make turns you into a workflow architect, while Toffu lets you focus on marketing strategy.
When Make Might Still Make Sense
Despite all its problems, there are a few scenarios where Make's visual approach might still be worth it:
Highly Regulated Industries
Organizations with strict compliance requirements might need the explicit audit trail that visual workflows provide, though AI systems are getting more transparent every day.
Existing Make Infrastructure
Teams that have already sunk tons of time into Make expertise and have working scenarios might stick with what they know rather than switch.
Very Simple, Repetitive Tasks
For basic data transfers between two apps with zero complexity, Make's visual interface can be straightforward - though this hardly justifies the learning curve for real marketing needs.
Legacy System Integration
Some ancient systems might need very specific technical configurations that are easier to control visually, though modern AI systems handle these edge cases better and better.
But honestly, these use cases are becoming pretty rare exceptions.
The Future is Obviously Conversational
We're seeing a massive shift in how automation works. The visual workflow paradigm - where humans have to translate their intentions into technical diagrams - is getting replaced by conversational systems that just understand what you want.
This isn't just about convenience - it's about removing the technical barriers that stop marketers from automating their most valuable work. When you can describe what you want instead of engineering how to build it, automation becomes accessible to everyone.
The Gap is Growing
Teams using conversational automation are reporting way faster implementation times, lower maintenance headaches, and better business results. Meanwhile, organizations stuck with visual workflow tools spend more time debugging than strategizing.
Best Practices for Prompt-Based Automation
To get the most out of conversational systems like Toffu:
Focus on Outcomes: Instead of describing processes, focus on the results you want. "Increase qualified lead generation from content marketing" not "Set up a content workflow."
Give Business Context: Help the AI understand your industry, audience, and goals. This context enables much smarter automation decisions.
Iterate Through Conversation: Don't expect perfection on the first try. Refine your requests based on what you get back, just like you would with a human teammate.
Think Business Objectives: Frame requests around business goals rather than technical tasks. "Improve customer retention" not "Send automated emails."
Making the Switch
The choice between Make and Toffu really comes down to where you want to spend your time and energy.
Make forces you to become a technical architect, learning visual programming, debugging complex scenarios, and constantly managing operation costs. You'll spend more time troubleshooting workflows than optimizing marketing results.
Toffu lets you stay focused on marketing strategy and business outcomes. Just describe what you want to accomplish, and the AI handles all the technical complexity. You can iterate quickly, experiment freely, and scale without worrying about operation limits or debugging costs.
For marketing teams that want to automate without becoming automation experts, the choice is pretty clear. The future belongs to systems that understand what you want to achieve, not ones that make you engineer how to achieve it.