Different Tools for
Different Jobs
How much assembly is required, and what do you maintain long-term?
Strengths & Trade-offs
Each approach has its place. Here's what they offer — and where you'll hit limits deploying AI workers at scale.
Zapier / Make
Large connector libraries and simple trigger-action models. AI features are being added, but bolted onto an integration-first architecture.
What They Offer
- Extensive app connector libraries
- Simple trigger-action setup for basic automations
- Templates and community resources
Where You'll Hit Limits
- AI agents are additive features, not the core architecture
- Cross-workflow memory and learning requires workarounds
- No built-in SLA tracking for human approval steps
- LLM cost visibility is limited within AI-powered steps
- Complex multi-step AI reasoning needs manual prompt chaining
Best for: Simple SaaS-to-SaaS integrations with occasional AI-powered steps
n8n
Open-source with AI agent nodes and self-hosting. The trade-off is operational overhead — you own the infrastructure, scaling, and security.
What They Offer
- AI Agent nodes with ReAct and tool use
- Self-hosted option for data control
- LangChain integration for AI patterns
Where You'll Hit Limits
- Self-hosting means you manage servers, scaling, backups, and security
- AI worker personas and memory are per-workflow, not centralized
- Multi-tenant isolation requires Enterprise license
- Per-step cost tracking and SLA enforcement need custom implementation
- The 20th complex AI workflow is as much work to maintain as the 1st
Best for: Technical teams comfortable managing their own infrastructure
AI Coding Tools
Claude Code, Cursor, and Copilot make it fast to build a solution. The question is who maintains it after day one — and who runs it in production.
What They Offer
- Build a working prototype in hours, not weeks
- Full control — no platform constraints
- Any LLM, any framework, any architecture
Where You'll Hit Limits
- Fast to build, but every automation becomes its own codebase to maintain
- No built-in governance, cost tracking, or audit trails — you add those yourself
- Credential management, secret rotation, and security are still on you
- Non-developers can't modify or monitor what was built
- Day 1 is fast. Day 100 — with 20 automations in production — is the real cost
Best for: One-off solutions or prototyping, when your team can own the ongoing maintenance
What Others Are Saying
Real feedback from users who've hit the limits.
Zapier / Make
"A 'simple' order workflow consumed 9 tasks per trigger. At 200 orders a day, we were burning 54,000 tasks per month."
— ThatAPICompany
"I worked 6 days on a scenario to create automated Pinterest pins from 70 blogs, and I just have to give up."
— Trustpilot review
"It will crash, lag, and freeze — losing all your progress."
— Trustpilot review
"Support has never responded. It's already been 2 months."
— Trustpilot review
n8n
"For a simple write-to-text-file action you have to use at least 3 nodes."
— n8n Community
"CSV processing is a pure nightmare."
— n8n Community
"AI Agent documentation is particularly poor. Pages are based on outdated versions."
— n8n Community
"RAM usage is hell at times. AI Agent nodes fail silently or time out on long-running operations."
— n8n Community
AI Coding Tools
"16 of 18 CTOs reported production disasters directly caused by AI-generated code."
— Final Round AI survey, 2025
"AI-generated code has 2.7× more security vulnerabilities than human-written code."
— CodeRabbit analysis
"Experienced developers were 19% slower with AI tools — despite predicting they'd be 24% faster."
— METR study, July 2025
"The codebase ends up looking like it was written by 50 developers who never talked to each other."
— Dev.to
Frequently Asked Questions
What's the difference between Doozer and Zapier for AI automation?
Can n8n handle AI agent workflows?
What is an AI worker platform?
See the difference firsthand.
Bring your most complex automation challenge. We'll show you how Doozer handles it — live.