AI only works with architecture: watch the masterclass now 
Digital innovation in the building materials trade: AI image search for STARK Germany 
The right system for every application: Medienwerft Demo Days – See for yourself 
End2End solutions from UX to hosting: Benefit from the expertise of the FIS Group! 
Do you fit into our team? Discover all vacancies now and apply now! 
Ready for the shipyard? Find out what you can look forward to as an apprentice at Medienwerft! 
AI expertise in three episodes: Listen to our podcast series now! 
Composable commerce reimagined: Discover Emporix's new strategy now! 
Three use cases: Watch the AI Booster in Retail webinar now! 
Use AI Models efficiently – to prevent the agent from becoming a cost trap
AI agents automate processes and save time – but large models quickly result in high operating costs. Even simple inquiries can become a cost factor in continuous operation. Only the right strategy makes artificial intelligence truly efficient.
The trend towards using AI models for optimizing business processes continues steadily – and for good reason: It saves time, reduces costs and increases efficiency.
From the automated processing of support requests to the evaluation of order incidents and after-sales support through personalized product recommendations – AI agents are taking on more and more tasks in everyday operations.
But despite all the enthusiasm for the possibilities of intelligent automation, one thing must not be overlooked:
At the end of the day, the value must outweigh the cost. This is because the use of generic Large Language Models (LLMs) such as GPT-4, Claude or Gemini is anything but free – especially if they are operated via API connections in real time with high usage volumes.
Even in self-hosting operation, the demands on hardware and energy consumption increase rapidly.
Efficiency yes – but not at any price
What many companies underestimate: The biggest cost traps do not arise from the initial setup of an AI system, but from its ongoing operation.
Every prompt, every user request, every query in the multi-turn dialog generates token costs or GPU time – and these add up dramatically with high scaling.
Therefore, if you rely on AI, you must also rely on efficient AI.
Optimized AI agents – for performance AND efficiency
The good news is that there are technological approaches for operating AI applications in a targeted, resource-efficient and cost-conscious manner. The FIS Group deliberately relies on its own infrastructure and development expertise – in its own data center, GDPR-compliant and powerful.
An overview of the most efficient levers
LEVER #1
Use specially trained and quantized AI models
Instead of using a universal LLM for every task, it is worth looking at task-specific trained and quantized models. These models are smaller, faster and more economical to operate – with consistently good performance in the respective field of application.
Example:
A quantized model such as DistilBERT or TinyLLaMA, which has been specially trained for support ticket categorization, requires only a fraction of the resources of a GPT-based model – with almost identical precision for this specific use case.
LEVER #2
Using lightweight transformer models without vector space overhead
Not every use case requires the semantic depth of a vector space model. For many tasks – such as the evaluation of simple ordering processes or the matching of standard answers – classic transformer models with a reduced architecture are absolutely sufficient.
Example:
Models such as FastFormer or Linformer deliver solid results with low latency and are particularly resource-efficient – ideal for edge deployments or on-premise solutions.
LEVER #3
Using LoRAs for control and efficiency
Low-rank adaptation (LoRA) is a clever way of adjusting large language models in a targeted manner without having to completely retrain them. Only relevant parameters are changed through targeted weight adjustments – this creates an “input and output funnel” that focuses the AI model on a clearly defined task.
Benefit:
Spending for processing power is reduced because the “thinking space” of the model is smaller – at the same time, natural speech input is retained, which increases user-friendliness.
LEVER #4
Human-in-the-loop as a quality anchor
As efficient as AI can be: Complex, risky tasks should never be fully automated. This is because AI does not make decisions – it generates probabilities. For processes that are complex to calculate, as well as for safety-relevant decisions or legal assessments, humans are irreplaceable as the final reviewer.
Result:
An efficient agent is not only fast and inexpensive – it also knows when to ask people.
CONCLUSION
Thinking efficiently about AI means thinking about profitability
The use of AI agents is a game changer – but only if it is done strategically. Those who blindly rely on large models will quickly be surprised by operating costs. The future lies in modular, specialized and well-calibrated AI systems that not only perform, but can also be operated economically.
Because intelligence without efficiency is waste.
Blog posts about artificial intelligence
Using AI efficiently: Answers to frequently asked questions
Smaller models such as DistilBERT or TinyLLaMA are trained for specific tasks and require significantly fewer computing resources. For a clearly defined use case, they deliver comparable results to large, generic LLMs at a lower cost.
LoRA (Low-Rank Adaptation) only adapts selected parts of a model instead of completely retraining it. This allows an AI model to focus on a specific task, which reduces both computing power and energy consumption.
Many companies rely on large models in general, without a clear efficiency strategy. This results in typical challenges:
1. Unnecessarily complex models for simple tasks – Instead of lean solutions, oversized models are used, which wastes resources.
2. Lack of cost control during operation – without monitoring or limits, models run inefficiently or cause unnecessarily high cloud costs.
3. No strategic model selection or modularization – there is a lack of planning as to which model is used for what and how flexibly components can be replaced.
Our impulse:
A targeted AI strategy begins with the selection of efficient models for the respective use case. Small, specialized models are often faster, cheaper and easier to maintain. Modularization creates scalability and adaptability, while cost controlling ensures that the benefits are in proportion to the costs.
AI automates recurring processes, reduces manual effort and speeds up response times – e.g. in support, order processing or customer service. The precondition is targeted and scalable use with clear benefits.
