I made this with ChatGPT currently as an example, this is mostly what is seen in enterprise. It’s a big thing so, it’s fenced to make it easier for the reader, but it shows where a vibe coder might lack knowledge.
Three Integration Models: Monolithic, Best of Breed, and SaaS
Three Integration Models: Monolithic, Best of Breed, and SaaS
The three models can look deceptively similar from the UI.
The real difference is where responsibility lives, where data crosses boundaries, and where failures can occur.
1. 
Monolithic Integration
Example: An organisation runs one large application containing authentication, customer management, billing, reporting, and persistence.
flowchart LR
U[User] --> APP
subgraph APP["Monolithic Application"]
AUTH[Authentication]
CRM[Customer Management]
BILL[Billing]
REPORT[Reporting]
AUTH --> CRM
CRM --> BILL
BILL --> REPORT
end
APP --> DB[(Shared Database)]
Core idea
Most functionality belongs to one deployable system.
Modules may be logically separated, but they commonly share:
- runtime
- database
- authentication
- deployment lifecycle
- internal domain model
Advantages
- Simple initial deployment
- Fewer network boundaries
- Transactions can remain local
- Easier initial debugging
- One primary technology stack
Trade-offs
The convenience creates tight coupling.
A change to the customer model may unexpectedly affect billing, reporting, authentication, or database migrations.
Vibe-coder hurdle
A vibe coder can easily ask:
“Add subscriptions to the application.”
…and receive something that appears to work.
The difficulty starts when they lack fundamentals around:
coupling → database schemas → migrations → transactions → dependency direction → regression testing
The generated feature may be locally correct while quietly violating assumptions elsewhere in the monolith.
The dangerous question becomes:
“It works — but what else did it change?”
2. 
Best-of-Breed Integration
Example: An organisation deliberately chooses specialised systems for CRM, payments, messaging, analytics, and identity.
flowchart LR
U[User] --> FRONT[Application]
FRONT --> IDP[Identity Provider]
FRONT --> CRM[CRM]
CRM --> INT[Integration Layer]
FRONT --> INT
INT --> PAY[Payment Platform]
INT --> MSG[Messaging Service]
INT --> DATA[Analytics Platform]
PAY -. events .-> INT
MSG -. status .-> INT
CRM -. updates .-> INT
Core idea
Instead of asking one platform to do everything, the organisation selects the best specialised tool for each capability.
For example:
- Identity → Auth0 / Entra ID
- CRM → Salesforce
- Payments → Stripe
- Messaging → Twilio
- Analytics → Snowflake
The architecture therefore becomes an architecture of contracts between systems.
Advantages
- Strong specialised capabilities
- Components can evolve independently
- Vendors can sometimes be replaced independently
- Teams can select technologies appropriate to each problem
Trade-offs
Complexity moves away from the individual applications and into the connections between them.
You now have to understand:
- APIs
- authentication
- schemas
- retries
- queues
- rate limits
- event ordering
- idempotency
- eventual consistency
- observability
Vibe-coder hurdle
This is where “just connect the APIs” becomes a tiny architectural horror movie.
A generated integration might correctly do:
Create Customer → Charge Card → Send Email
but what happens when:
Create Customer ✅
Charge Card ✅
Send Email ❌
Or:
Webhook arrives twice.
Or:
Payment succeeds but the HTTP request times out.
Or:
CRM says ACTIVE while Billing says CANCELLED.
Without fundamentals, the temptation is to keep adding conditionals until the happy path works again.
The missing concepts are often:
contracts → state → ownership → idempotency → retries → consistency → failure recovery → observability
The system stopped being merely code.
It became a distributed system.
3. 
SaaS-Centric Integration
Example: A custom application is relatively small because most major capabilities are consumed from external SaaS platforms.
flowchart TB
USER[User] --> APP[Custom Application]
APP --> AUTH[Authentication SaaS]
APP --> CRM[CRM SaaS]
APP --> PAY[Payment SaaS]
APP --> STORAGE[Storage SaaS]
PAY -. webhook .-> WH[Webhook Handler]
CRM -. webhook .-> WH
AUTH -. events .-> WH
WH --> APP
APP --> LOCAL[(Local Application Data)]
Core idea
The organisation does not build every capability itself.
Instead, the application acts partly as an orchestrator of externally managed services.
This can massively reduce the amount of infrastructure a small team needs to operate.
Advantages
- Very rapid development
- Little infrastructure to maintain
- Managed scaling
- Managed security features
- Powerful capabilities available through APIs
- Excellent fit for prototypes and smaller teams
Trade-offs
The simplicity is partially an abstraction.
Your system still depends on:
Your application
↓
Vendor APIs
↓
Vendor authentication
↓
Vendor availability
↓
Vendor rate limits
↓
Vendor data models
↓
Vendor pricing
↓
Vendor API lifecycle
Your architecture therefore inherits somebody else’s architecture.
Vibe-coder hurdle
SaaS is perhaps the easiest model to vibe-code initially because the first version can be spectacularly fast:
Prompt
↓
Generate UI
↓
Add Supabase
↓
Add Stripe
↓
Add OAuth
↓
🎉 SaaS!
Until reality introduces:
duplicate webhook
expired token
API version change
rate limit
tenant boundary
partial failure
permission escalation
deleted remote object
stale local cache
The missing fundamentals tend to be:
OAuth → authorization → tenancy → API contracts → webhook semantics → data ownership → security boundaries → failure handling
The crucial distinction is:
Calling an API is easy. Owning the consequences of calling that API is architecture.
The Architectural Difference
flowchart TB
NEED[Business Capability]
NEED --> MONO[Monolithic]
NEED --> BOB[Best of Breed]
NEED --> SAAS[SaaS-Centric]
MONO --> M1["Complexity lives<br/>inside the application"]
BOB --> B1["Complexity lives<br/>between systems"]
SAAS --> S1["Complexity is partly<br/>delegated to vendors"]
M1 --> FUND[Engineering Fundamentals]
B1 --> FUND
S1 --> FUND
| Model |
Primary Strength |
Where Complexity Hides |
Fundamental Skill |
| Monolithic |
Simplicity |
Internal coupling |
Software architecture |
| Best of Breed |
Specialisation |
Integration boundaries |
Distributed systems |
| SaaS |
Speed |
External dependencies |
Contracts & operational ownership |
The Vibe-Coding Trap
Vibe coding is extremely good at collapsing:
Idea
↓
Implementation
Traditional software engineering spends a lot of effort thinking about everything in between:
flowchart LR
IDEA[Idea]
--> REQ[Requirements]
--> DOMAIN[Domain Model]
--> ARCH[Architecture]
--> CONTRACT[Contracts]
--> STATE[State]
--> FAILURE[Failure Modes]
--> SECURITY[Security]
--> TEST[Test Strategy]
--> OBS[Observability]
--> CODE[Implementation]
AI can generate the implementation remarkably well.
But generation does not make the intermediate concepts disappear.
That is why a vibe-coded prototype can feel magical at:
“Can we make this work?”
…and suddenly become painful at:
“Can we explain why this works, predict how it fails, modify it safely, and operate it for three years?”
The fundamental gap is therefore not really coding ability.
It is the ability to reason about:
boundaries, contracts, state, ownership, coupling, failure, security, testing, and change.
Those are the parts of software engineering that only become visible once the happy path stops being happy.
After all this, the important steps are currently taking place in education around the world. In Switzerland for example, at the higher education institutions AI is already being integrated as a form to enable and help advanced through studies with the initiative Bildung 6.0 for example. So, for your case, approach institutions near your area. Seminars, exchanges, speakers, all that is important to help shape what is coming.
And most important, bring innovation, AI can do so much, but the human needs remain human.
