Zapier AI vs Make.com - why both miss the point on AI automation

Everyone asks about the zapier ai vs make comparison. Wrong question.

The right question is whether you need middleware at all. After working with teams trying to automate workflows at Tallyfy, I’ve watched this pattern play out: start with Zapier because it is easy, hit limits, migrate to Make for more power, then realize you are just paying rent on complexity that should live in your actual systems.

Both platforms promise AI automation. Neither delivers what mid-size companies actually need.

The middleware tax

Here’s what automation platform pricing actually looks like once you scale past toy examples.

Zapier’s task-based model charges per action. That marketing agency pulling leads from Typeform to HubSpot? Started at $19.99 monthly. Hit 15,000 tasks and the bill jumps to enterprise tiers with zero added functionality. Just volume. Their newer Agents feature adds a separate “activities” billing layer on top of tasks, so AI workflows carry two cost meters running simultaneously.

Make switched from operations to credits in 2026, which looks cheaper initially at $9 monthly for 10,000 credits. But AI modules consume credits at dramatically different rates. A standard Google Sheets action costs 1 credit. A native AI transcription module? 50 credits per run. Complex AI workflows burn through credit allowances fast.

The pattern is identical: both platforms financially penalize sophistication.

This matters for AI workflows because AI needs multiple steps. Check context, make decision, take action, verify result, log outcome. That’s five operations minimum. Run it a thousand times and you are paying middleware rent on work that should cost you API fees only. Research on enterprise AI budgets shows most organizations underestimate total cost of ownership by 40-60%, and middleware fees are a big part of that gap.

What Zapier AI actually does (and does not)

Zapier’s AI features now include Copilot for building workflows, AI Agents (still in beta), custom chatbots, and Canvas for visual process mapping. The pitch is AI-powered orchestration across 8,000+ app integrations.

The reality is narrower. Zapier’s AI Agents work when 80% accuracy is acceptable. Agents can take actions only in apps you have explicitly connected, and they cap autonomous actions at 10 on free, 40 on pro before asking for human confirmation. For everything else, you are building traditional if-this-then-that flows with AI APIs bolted on.

Which is fine for simple stuff. Pull data, send to an LLM, post result somewhere. But that is not intelligent automation. That’s using AI as a glorified text processor in a rigid workflow.

The bigger limitation: Zapier still lacks true autonomous decision-making and complex multi-step reasoning. You get massive app coverage but limited ability to build actual intelligence into your workflows. And as anyone who has tried to build reliable AI agents knows, the gap between “demo works” and “production works” is enormous.

When an app updates its API, workflows break silently with no alerts, no rollback, hours of manual recovery. For AI workflows running critical business processes, that is unacceptable.

Make.com’s complexity trap

Make offers more power through its visual workflow builder. Over 2,400 integrations, custom API connections, conditional logic, data manipulation without external tools. They have also launched AI Agents and Maia, an AI-powered builder, plus Make Grid for visualizing your entire automation landscape. On paper, it sounds impressive.

The trade-off is complexity. Real user feedback tells the story: “Spent too much time wrestling with permissions and debugging error messages.” The datetime functions are “a true nightmare.” When one step fails, it stops the whole scenario.

For AI workflows, this gets worse. You’re chaining prompts together, handling variable outputs, managing context across steps. Make’s visual builder shows you all of it, which means you are debugging AI randomness in a flowchart that looks like tangled wires. And with the credit system, you can connect your own AI provider via API to avoid inflated AI credit charges, but then you are essentially building your own integration inside the platform that is supposed to handle integration for you.

And the support? Users report it is nearly non-existent. Documentation is spotty. You’re on your own when things break, which they will, because you are combining AI unpredictability with visual automation complexity.

The zapier ai vs make comparison misses this fundamental issue: both platforms assume deterministic workflows. AI is probabilistic. The mismatch creates problems neither platform was designed to solve.

Why middleware fails AI workflows

Traditional automation platforms were built for connecting apps through APIs with predictable inputs and outputs. The industry is moving toward agentic AI systems that can understand context, make independent decisions, and execute multi-step workflows autonomously. Gartner predicts 40% of enterprise applications will embed AI agents by end of 2026, up from less than 5% in 2025. Middleware platforms were not designed for this shift.

The core problems:

AI needs context from multiple sources. Middleware passes data between apps but does not maintain state across complex reasoning chains. You end up storing context in spreadsheets or databases, turning your automation into a data plumbing exercise. This is why 65% of leaders cite agentic system complexity as their top barrier, and only about 9% of companies have AI agents running in production.

AI makes decisions based on fuzzy logic. Middleware excels at exact rule matching. The gap between “if field equals X” and “if the general sentiment suggests Y” is where these platforms fall apart.

Error handling assumes you can retry failed steps. With AI, you cannot just re-run the same prompt and expect identical results. Rate limits from third-party APIs add another layer of fragility that simple retry logic does not address.

Slack limits you to one request per second. OAuth tokens expire every 1-3 months. Your AI workflow that posts summaries to channels? It’ll hit limits and stop. The middleware has no intelligent way to handle this beyond “pause and retry.”

What to do instead

Here’s what the cost analysis actually looks like. Custom API integration costs vary significantly based on complexity, but the upfront investment pays for itself. Middleware platforms charge hundreds to thousands monthly at scale, and 65% of total software costs occur after original deployment anyway. You are going to pay either way. The question is whether you are building equity or paying rent.

For high-volume AI workflows, custom integration pays for itself within 1-2 years. More importantly, you own it. No middleware breaking when a vendor updates their API. No task limits when you need to scale. No support tickets to platforms that don’t respond. If you are weighing these trade-offs, a proper build vs buy framework helps clarify when custom development actually makes sense versus buying.

The Model Context Protocol has gone from experimental to industry standard remarkably fast. Introduced by Anthropic in late 2024, MCP was adopted by OpenAI, Google, and Microsoft within months. Microsoft integrated native MCP support into Windows 11 and Copilot Studio. Salesforce launched MCP servers for Slack and Agentforce. MCP acts as a universal interface for AI to interact with APIs, removing the need for platform-specific integrations. Unlike workflow automation that charges per task, MCP enables direct runtime connection with no per-operation fees.

For teams not ready for custom development, n8n offers an open-source alternative with execution-based pricing instead of per-task charges. Cloud plans start at $20 monthly for 2,500 executions, and every plan includes unlimited users and workflows. The self-hosted community edition is free. Most companies that switch from task-based platforms report 70-90% cost reductions.

Practical decision framework:

Small workflows with standard apps? Zapier works fine. You’re paying for convenience, which has value when automation is not your core business.

Complex workflows under 10,000 operations monthly? Make gives you more control at reasonable cost, assuming you have technical capacity for the complexity.

AI workflows at scale? Stop paying the middleware tax. Build direct API integration, adopt MCP for future-proofing, or use open-source platforms where you control the infrastructure. Run a proper TCO analysis before committing to any platform at volume.

The zapier ai vs make comparison assumes you need to pick one of these platforms. Most teams running serious AI automation discover they need neither.

They need systems that talk directly, with AI orchestrating the conversation, not middleware translating every word.