A custom solution instead of an expensive SaaS license.
Some SaaS tools can be built yourself with today's language models, cheaper, better fitted, and with full data ownership.
The situation
SaaS costs rise with every seat and every usage limit, and many tools only cover part of what you actually need. At the same time, your data leaves your own system and lands with a vendor whose roadmap you don't control.
Language models have shifted the cost-benefit math for many of these tools: classification, text generation, research, simple analysis can now be built in-house with manageable effort, tailored precisely to your process. We honestly assess where that pays off and where an existing SaaS solution is simply better.
Where a replacement pays off, we build a lean, maintainable solution your team can keep developing, instead of creating a new dependency on us.
Deliverables
Cost-benefit review
An honest assessment of whether building in-house or SaaS is more economical for the given use case.
Architecture decision
A decision on model choice, hosting, and data storage, documented and justified.
A working solution
A production-usable solution for the identified use case, not a demo.
Migration plan
A plan for retiring the existing SaaS tool, including data migration and a transition phase.
Handover to your team
Documentation and training so your team can keep developing the solution on its own.
Our approach
- 01
Review SaaS costs and use cases
We check what your tools cost and what share of their features you actually use.
- 02
Assess the economics
We compare in-house and SaaS costs over a realistic timeframe, including operations.
- 03
Build the solution
We develop the solution for the use cases where building in-house pays off.
- 04
Retire and hand over
We support the transition and hand over the solution, including documentation, to your team.
Typical starting points
| Starting point | Review |
|---|---|
| SaaS costs grow faster than the benefit | Whether a replacement is economical or a different plan is enough |
| A SaaS tool only covers part of the process | Whether a tailored in-house solution can cover the rest of the process |
| Data ownership is a concern | Whether sensitive data needs to leave your own system or not |
Review: Whether a replacement is economical or a different plan is enough
Review: Whether a tailored in-house solution can cover the rest of the process
Review: Whether sensitive data needs to leave your own system or not
Outside our scope
We don't recommend building in-house on principle. If a SaaS tool is the better choice for your case, we say so, even if that means no project for us.
Frequently asked questions
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
| EdTech company, customer service | Customer service answered recurring questions manually from scattered knowledge. | AI agent with access to the knowledge base, with clear escalation to humans. | Recurring inquiries run productively through the agent, complex cases stay with the team. |
| Own products: FoodCheck and FitWise | Own product idea in nutrition and fitness, with AI as the core function. | Flutter apps with OpenAI integration, own backend APIs, own AI services. | Two own AI products in production, fully built and operated in-house. |
EdTech company, content production
Ausgangslage
Content production ran step by step by hand and depended on a few people.
Umsetzung
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Ergebnis
The content team works on quality, not on mechanical intermediate steps.
EdTech company, customer service
Ausgangslage
Customer service answered recurring questions manually from scattered knowledge.
Umsetzung
AI agent with access to the knowledge base, with clear escalation to humans.
Ergebnis
Recurring inquiries run productively through the agent, complex cases stay with the team.
Own products: FoodCheck and FitWise
Ausgangslage
Own product idea in nutrition and fitness, with AI as the core function.
Umsetzung
Flutter apps with OpenAI integration, own backend APIs, own AI services.
Ergebnis
Two own AI products in production, fully built and operated in-house.
Initial call: 30 minutes, concrete.
We work out whether replacing your most expensive SaaS tool pays off.