It’s 3:07 p.m. and you’ve got that dangerous kind of confidence-the kind that only shows up when an AI assistant just spat out a working prototype.
A chat UI. A few tool calls. A database table you didn’t even design, but somehow it works. Your “AI app builder” moment has arrived: you can describe the feature in English and watch the app appear.
Then the first real user signs up.
The second one triggers a prompt that produces a runaway token bill. A third hits a permission edge case you didn’t think existed. And suddenly you’re not vibe coding anymore-you’re running a product.
The debate floating around “vibe coding” (a term popularized by Andrej Karpathy) isn’t really about whether SaaS dies. It’s about whether the unit of software changes: from big, monolithic suites to fast-built, founder-specific apps that behave like bespoke SaaS for a niche.
So let’s answer the founder question that actually matters:
How do you keep the speed of vibe coding while building a backend that can survive reality-security, scaling, cost predictability, and future optionality?
Along the way, we’ll connect the dots between no-code/low-code, modern AI workloads, and why an open-source foundation is the easiest way to avoid getting trapped when your MVP becomes a company.
Vibe coding doesn’t kill SaaS-it changes what “SaaS” looks like
SaaS isn’t dominant because people love subscription dashboards. SaaS is dominant because it packages the boring, expensive parts of software so businesses can buy outcomes instead of assembling infrastructure.
Even now, the market signals “SaaS is adapting,” not collapsing. Gartner continues to forecast strong growth in SaaS spend, driven in part by modernization and AI features inside products. See Gartner’s public cloud spending forecast and SaaS numbers for context: https://www.gartner.com/en/newsroom/press-releases/2024-05-20-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-surpass-675-billion-in-2024
What vibe coding changes is who can build software that feels like SaaS:
- A 2-person team can build workflow automation that used to require an entire “application development companies” budget.
- An internal ops lead can ship a micro-tool that replaces three subscriptions.
- A founder can validate a vertical AI product before fundraising.
But the shift has a boundary. Vibe coding accelerates the front of the funnel-prototyping, wiring features together, scaffolding. It doesn’t erase the realities that made SaaS valuable:
- Reliability and uptime
- Compliance and security
- Cost control and billing clarity
- Data integrity
- Operational visibility
- The ability to evolve architectures without rewriting the company
In other words: vibe coding can help you create software. It doesn’t automatically create a service.
The founder story: from “it works” to “we can’t ship this”
If you’re an AI-first startup founder, you’ve probably lived some version of this:
You start with a prompt.
You use one of the new AI app builder experiences-maybe an IDE copilot, a chat-based scaffold tool, or an agent that edits a repo. Within hours, you can demo a product that would’ve taken weeks.
This isn’t imagination; even traditional copilots show measurable speedups. Microsoft Research found developers completed a coding task significantly faster with GitHub Copilot in a controlled study: https://www.microsoft.com/en-us/research/publication/the-impact-of-ai-on-developer-productivity-evidence-from-github-copilot/
But then production shows up with a checklist you didn’t ask for:
- “Where are we storing user data?”
- “What happens when a tool call retries?”
- “Can we see prompt vs completion cost per feature?”
- “How do we rate-limit so one customer doesn’t burn the token budget?”
- “If we outgrow this platform, can we leave without rewriting everything?”
That last question-migration optionality-is where many AI-native teams quietly lose years.
Because the backend you choose at MVP is the one you’ll fight with at Series A.
AI app builder speed needs an AI-ready backend (or you just built a demo)
The core mistake isn’t using vibe coding. The mistake is assuming the code is the product.
For AI-driven apps, the backend is doing more than CRUD:
- Real-time orchestration: updates, events, agent status, job queues
- Identity & access control: object-level permissions, roles, team spaces
- Data modeling for AI: embeddings, conversation memory, tool outputs
- Cost and usage governance: rate limits, quotas, per-tenant policy
- Observability: traces across model calls, retries, fallbacks
- Prompt/version management: not just code versions-behavior versions
This is why “app builder no code” and “AI app builder” experiences feel magical at first and painful later. They hide the backend-until your requirements become non-negotiable.
A useful rule of thumb:
If your AI feature can cause unbounded resource consumption (tokens, GPU time, background jobs), you need a backend that treats cost controls as a first-class concern.
And if your app has users, you need API security and authorization patterns that survive growth.
For a grounded view of what goes wrong in API-driven backends, OWASP’s API Security Top 10 is a sober reference point: https://owasp.org/API-Security/editions/2023/en/0x11-t10/
Best backend frameworks 2025: what matters for AI-native products
Founders often ask for “the best backend frameworks 2025” as if there’s a single winner.
In practice, the decision is less about syntax and more about operational posture:
Option A: Framework-first (build your own backend)
You pick a stack-Node/Express, FastAPI, Django, Rails, Spring, or a serverless approach-and you own everything.
This can be great when:
- You have a backend engineer who likes infrastructure
- You need unusual control over data and execution
- Your compliance needs are complex from day one
The trade-off is time and DevOps overhead. AI products already have enough moving parts (models, evals, retrieval, agents). Many small teams end up spending runway on things that don’t differentiate.
Option B: BaaS-first (backend as a service)
You get authentication, database APIs, functions, file storage, and often real-time features out of the box.
This can be perfect for AI MVPs-until you hit one of these walls:
- Vendor lock-in (proprietary APIs, hard-to-export auth, closed-source runtimes)
- Opaque limits (requests, bandwidth, function timeouts)
- Unpredictable pricing (especially when AI features spike usage)
Option C: Open-source-based platform (speed without the trap)
This is the category that’s quietly becoming the “default” for teams that want velocity and an exit strategy.
The idea is simple: build on an open-source backend foundation so your architecture stays portable, but let a managed platform handle scaling and operations.
A widely used example is Parse Server-an open-source backend that runs on Node.js and can be deployed on your own infrastructure: https://github.com/parse-community/parse-server
This is where an AI-first founder can have both:
- MVP speed (ship now)
- Migration safety (leave later)
- Cost transparency (understand the bill)
The backend problems vibe coding won’t solve for you (until it’s too late)
Vibe coding is great at “make it work.” Production is “make it durable.” Here are the failure modes that show up fastest in AI apps.
1) Authorization gets subtle fast
AI features tend to blur boundaries:
- “Summarize this document” becomes “summarize any document” if object-level authorization isn’t airtight.
- Tool calls become de facto admin actions if roles aren’t explicit.
This is why the most common API vulnerabilities are authorization failures-not cryptography mistakes.
Practical move: define access control rules early, before the schema sprawls. Treat every AI tool call as an API surface.
2) Your cost model becomes your product model
In traditional SaaS, infra cost usually scales with users and storage.
In AI products, infra cost scales with behavior:
- prompt length
- retrieval depth
- tool-call retries
- background jobs
- streaming vs batch patterns
If you can’t attribute cost drivers per feature or tenant, you can’t price confidently.
Practical move: add usage metering and budgets early, even if you don’t charge yet.
3) “Works on my laptop” doesn’t mean “works at 2 a.m.”
The day you ship, the app becomes a 24/7 system.
If your backend can’t auto-scale, you’ll either overprovision (burn runway) or underprovision (lose users).
Practical move: pick infrastructure that auto-scales without you becoming the on-call DevOps team.
4) Data integrity is harder than it looks
AI apps often store:
- user profiles and auth
- chat history
- tool outputs
- structured records produced by models
- feedback labels and eval results
If you don’t enforce data constraints and migration discipline, you’ll end up with a messy knowledge base that makes every new feature slower.
Practical move: decide early what belongs in a relational store vs a document store.
When founders ask for “database management system examples,” the practical answer is: you’ll likely use multiple.
- Relational DBMS (PostgreSQL, MySQL) for transactions and integrity
- Document-style storage for flexible objects
- Vector storage for retrieval (depending on your approach)
Where Google Cloud Cloud SQL fits (and where it doesn’t)
For many startups, the fastest path to reliable relational data is a managed service.
Google Cloud Cloud SQL is a fully managed relational database service (MySQL, PostgreSQL, SQL Server) designed to offload patching, backups, and operational work: https://docs.cloud.google.com/sql/docs/introduction
It’s a strong choice when:
- You need PostgreSQL/MySQL reliability without DBA overhead
- You want managed backups and high availability options
- Your schema needs constraints, joins, and transactions
It’s not a full “AI backend.” Cloud SQL doesn’t give you auth, permissions, file storage, real-time APIs, or deployment workflow. It’s a critical building block-but only one piece.
For an AI-first founder, the winning architecture is often:
- a managed relational database for durable transactional data
- an API layer with strong auth and rate limiting
- a job/worker pattern for AI tasks
- observability to trace costs and failures
Vibe coding can scaffold this, but it can’t operate it.
The real question: are we building software-or a service?
The NDTV-style debate around “SaaS dominance” often gets framed like a boxing match:
- SaaS companies vs vibe coding platforms
- enterprise suites vs custom tools
But the market reality is closer to a supply chain:
- Vibe coding makes it cheaper to create apps.
- Low-code platforms and “app builder no code” tools make internal apps more common.
- SaaS companies respond by embedding AI, offering extensibility, and becoming platforms.
- Startups ship faster, but win only if their backends don’t collapse under real users.
This is why the future likely looks like more software, not less:
- more niche apps
- more AI copilots inside existing SaaS
- more vertical tools that replace generic suites
Gartner’s continued SaaS spend growth is consistent with that story: SaaS isn’t disappearing; it’s absorbing AI and expanding its surface area.
A production readiness checklist for AI-native MVPs
You don’t need enterprise ceremony. You need the few things that prevent “MVP” from becoming “incident generator.”
Here’s a founder-friendly checklist you can run in an afternoon.
Security & access control
Start here because it’s cheaper now than later.
- Define roles (admin, member, customer) and object-level permissions
- Separate public vs private data explicitly
- Add rate limiting and abuse protection for model-triggering endpoints
- Keep secrets out of the client; treat tool calls as privileged operations
If you want a structured threat lens, map your API endpoints against OWASP API Top 10 categories and fix the obvious gaps.
Reliability & scaling
- Auto-scale where possible (especially API + workers)
- Make AI workloads async when latency isn’t essential
- Add retries with backoff, but cap them to avoid cost spirals
- Plan for queue spikes (launches, virality, batch imports)
Data & model governance
- Decide what you store long-term (chat logs, tool outputs) vs ephemeral
- Add data retention controls early if you handle sensitive content
- Version prompts and tool schemas like you version code
Cost controls
- Set per-tenant budgets and hard limits for AI-heavy actions
- Measure prompt vs completion behavior (you’ll learn what users actually do)
- Separate “nice to have” AI flows from “must work” flows
Developer velocity
Your “mobile app development tools” may change as you iterate-React Native, Flutter, native iOS/Android-but the backend should remain stable.
- Use CI-connected deploys so backend changes are not a ritual
- Keep environments consistent (dev/staging/prod)
- Prefer platforms that reduce DevOps work without restricting architecture
The lock-in trap: why AI founders should care earlier than most
Lock-in isn’t only about pricing. It’s about architecture gravity.
The moment you rely on proprietary auth models, closed-source runtimes, or non-exportable data structures, your product roadmap starts getting negotiated by a vendor.
That’s fine if you’re buying an outcome you’ll never outgrow.
But AI-first products rarely stay still:
- you might move from one model provider to another
- you might bring inference in-house for cost reasons
- you might need region-specific data handling
- you might add real-time agent collaboration
Each of those changes becomes harder if your backend is a sealed box.
If you’re evaluating common backends, it helps to compare portability and limits explicitly. For example, if you’re considering Firebase or Supabase as your starting point, it’s worth reading clear, side-by-side comparisons that emphasize migration paths and constraints:
- Firebase alternative context: https://www.sashido.io/en/sashido-vs-firebase
- Supabase alternative context: https://www.sashido.io/en/sashido-vs-supabase
(These comparisons matter less as “who wins” and more as a forcing function: they reveal where future friction will come from.)
So… will vibe coding end SaaS dominance?
Not likely. But it will force SaaS to compete on a new axis.
In the old world, shipping software was hard, so distribution and bundling won.
In the new world, shipping a first version is easier, so the winners will be teams who can:
- iterate quickly without generating unpayable tech debt
- keep costs understandable as AI usage spikes
- maintain security and compliance without slowing down
- avoid dead-end platforms that require rewrites to grow
Vibe coding turns more founders into builders. It doesn’t turn every builder into an operator.
Which brings us back to the practical takeaway:
Your AI app builder prototype is only the beginning. The backend you choose decides whether you can ship version two without panic.
A helpful next step if you’re turning a vibe-coded MVP into a service
If you want to keep the speed of vibe coding but move onto a backend that auto-scales, stays migration-friendly, and is designed for modern AI workloads, you can explore SashiDo’s platform here: https://www.sashido.io/
Conclusion: build your AI app builder like you plan to survive success
Vibe coding is an acceleration layer-and it’s real. It’s also not a substitute for the backend fundamentals that keep products stable.
If you’re an AI-first founder, treat your AI app builder phase as a gift: it buys you speed. Then use that speed to make the durable choices early-open foundations, predictable costs, and a backend architecture you can evolve without vendor lock-in.
SaaS won’t end. But the teams that combine vibe-coded velocity with production-grade backends will redefine what SaaS feels like in 2025 and beyond.
