TL;DR
For AI projects, there are three paths: SaaS (ready-made, affordable, limited control), custom development (expensive, full control), or productized services (the middle ground — 70% blueprint, 30% customization). The decision depends on data sovereignty, process individuality, and budget. In B2B scenarios with specialized processes, the path usually leads through productized services.
Contents
- What’s the difference between SaaS and Custom Development?
- When does SaaS make sense?
- What speaks for custom development?
- What are Productized Services?
- How do I make the right decision?
- FAQ
What’s the difference between SaaS and Custom Development?
In short: SaaS is rented software — quickly available but not customizable. Custom development is tailor-made — but expensive and complex.
SaaS (Software as a Service):
Tools like Salesforce, Miro, or Figma. Someone developed a solution hoping to find enough customers who pay monthly. The advantage: Low entry costs, quick start. The disadvantage: No influence on further development, limited customization.
Custom Development:
I specify exactly what I need, and a team builds it from scratch. Full control, perfect fit. But: High initial costs, long development time, I have to make all the mistakes myself.
When does SaaS make sense?
In short: When a solution exists that fits exactly — and the conditions are right.
The advantages of SaaS:
- No large initial setup effort
- Productive in a few days
- Someone else handles updates
- Low monthly costs instead of high initial investment
The pitfalls:
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Data sovereignty: In AI projects, we work with internal data, often even customer data. Where is it hosted? Who has access? Is it used for training?
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Terms change: Today the conditions fit, tomorrow the provider changes them — and you either agree or cancel.
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Feature requests go nowhere: Your individual requirement ends up at the bottom of a roadmap. You have to live with what’s there.
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Vendor lock-in: The deeper the integration, the harder it is to switch.
What speaks for custom development?
In short: Full control, perfect fit — but high effort and all mistakes on your own account.
The advantages:
- Exactly what you need
- Data stays under your control
- No dependency on external roadmaps
- Can be adapted to existing systems
The reality:
The problem: You often don’t know your own requirements that precisely. The process of figuring out how you actually work is complex. And all the mistakes a SaaS vendor has already made and learned from — you make them again.
An example from practice: In the first sales agent projects, we underestimated how different collaboration with an agent is compared to working with people. The user experience had to be completely rethought. You only learn that through experience.
What are Productized Services?
In short: The middle ground — a proven blueprint with individual customization. 70% is fixed, 30% gets adapted.
Productized services mean: Someone has already thought it through. There’s a proven approach, a data model, an architecture. But the code isn’t set in stone.
What stays fixed:
- The core of the application
- Proven patterns and learnings
- Basic functionalities
What gets adapted:
- Integration into existing systems
- Mapping of specific process steps
- User interface and experience
- Data connectors
- New modules for special requirements
The advantage: You benefit from the provider’s experience without developing everything from zero. If a new module is needed — voice agent for field sales, for example — it gets developed and can be reused for future projects.
How do I make the right decision?
In short: Check SaaS first, then productized service, then custom — in that order.
The decision logic:
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Is there a SaaS tool that meets your requirements? If yes, and the conditions (data, costs, control) are right — use it.
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Is there a productized service from an experienced provider? If there’s no suitable SaaS, this is the way. You get experience and flexibility.
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Do you really need something nobody has done before? Only then does full custom development make sense. Often it’s enough to wait until someone else had the idea.
The reality in B2B:
In B2B scenarios — especially with agentic AI for business processes — it usually ends up being productized services. The processes are too specialized, the data too individual, the integration too complex for standard SaaS.
An important point about adoption:
With SaaS, there’s a temptation to start quickly and then give up when it doesn’t immediately work. With productized services, commitment is higher — you do requirements workshops, service design sessions, bring people along. This leads to better adoption.
FAQ
Isn’t Productized Service just expensive SaaS?
No. With SaaS, you get a finished product that’s the same for everyone. With productized services, it gets adapted — to your processes, your systems, your data.
What costs more: SaaS or Productized Service?
Short-term, SaaS costs less (OpEx vs. CapEx). Long-term, it depends on whether the SaaS tool really fits. If you can’t use it properly, even cheap SaaS is wasted money.
Who runs the software with Productized Services?
Flexible. Can be operated by the provider in the cloud, can be handed over to your IT, could theoretically run on-premise too.
Do I need an IT team for Productized Services?
Not necessarily for operations — the provider can handle that. For requirements workshops, you need people who know the processes.
How long does a Productized Service project take?
Significantly shorter than custom development, but longer than SaaS setup. Weeks not days, but also not months.
Conclusion
The question isn’t “SaaS or Custom?” — the question is: Which path fits my situation?
If a SaaS tool fits exactly and the conditions are right: Use it.
If your processes are too specialized, your data too sensitive, your requirements too individual: Productized services are the pragmatic middle ground.
And only if really nobody offers something like this — and it pays off: Then develop yourself.
In most B2B AI projects, the path leads through productized services. Things are too specialized for standard SaaS, but too similar to other projects for complete new development. And for smaller internal processes, there’s a third option: mini-apps built with AI coding agents in days instead of months.
This article is based on a conversation between Manuel Zorzi and Michael Kirchberger about choosing between SaaS and custom development for AI projects. Watch the full podcast episode on YouTube →