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Local AI vs. cloud AI - which is better for your company?

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Lukas UrichDigital Marketing Manager

In this article, you will find out when local AI or cloud AI is the better choice for your company. While cloud AI impresses with its flexibility, local AI scores with maximum data security and real-time capability. We show you the advantages and disadvantages of both approaches and how to find the right strategy.

The choice between local AI and cloud AI is a challenge for many companies. Both approaches offer enormous advantages, but which solution suits your requirements? Cloud AI is characterised by flexibility and scalability, while local AI stands for data protection and offers you a high degree of independence. Discover which strategy will optimise your business processes, increase your efficiency and give you a competitive edge. sustainable competitive advantage can secure - customised for your company.

What is local AI? And what is the difference to cloud AI?

Local AI refers to the processing of data directly on the own infrastructure or hardware without transferring them to external cloud servers. AI models are executed directly on the local devices, to analyse data and make decisions. This technology makes it possible to keep data securely on site and analyse it in real time. This offers some advantages, even for smaller companies, but also poses some challenges.

Cloud AI on the other hand, processes data in the cloud, i.e. on servers of external providers, such as ChatGPT or Google Gemini. Companies use AI models via the internet, which lowers the barriers to entry, but is also associated with data protection and dependency risks.

Advantages of local AI:

  1. DSVGO compatibility : No transfer of sensitive data to external cloud servers, for example in the USA, means less risk of data leaks and simplified compliance with data protection regulations.

  2. Maximum control: You retain complete control over your data and AI models, as all processing takes place securely on your own infrastructure.

  3. Low latency: As processing takes place locally, there are no network latencies - perfect for real-time applications.

  4. IndependenceProcessing your data completely locally means you are independent of external cloud services and therefore less susceptible to their failures.
  5. Data storage in Germany: Everything remains securely on site for legal and regulatory requirements.

  6. Customised solutions: Individual adaptations allow you to specialise in your applications and thus achieve a high level of efficiency.

Challenges of local AI:

Expertise: Implementing a local AI solution requires in-depth knowledge of artificial intelligence and IT, as well as a deep understanding of individual business requirements in order to optimally adapt and utilise the technology.

Costs: While cloud AI primarily incurs running costs, a local AI solution usually requires higher initial investments in hardware, software and possibly personnel, for example. However, the ongoing operating costs may be lower under certain circumstances.

Scalability: In the event of strong growth in data volumes or model complexity, the capacities of a local infrastructure can be reached sooner than in a cloud environment, which can draw on massive computing capacities.

Local AI vs. cloud AI in comparison

AspectLocal AICloud AI
Data protectionGDPR-compliant, data remains on siteData in the cloud, potentially less secure
CostsHigher initial investment. stable in the long termLower entry costs, ongoing fees
LatencyLow, as processing takes place locallyHigher through data transmission
FlexibilityCustomised, hardware-boundDynamically scalable
IndependenceNo risk from external providersDependence on cloud services
Areas of applicationData protection and real-time applicationsData-intensive tasks

One Hybrid solution can be optimal for many companies in order to Advantages of both options to use. This way, you benefit from the security and data protection advantages of local AI and can fall back on cloud AI for data-intensive and more specialised cases, for example for tasks such as image processing or model updates.

Conclusion

The choice between local AI, cloud AI or a hybrid combination depends heavily on the specific requirements of your company. Local AI is particularly suitable for data-sensitive applications and real-time processing, while cloud AI offers a dynamic and more cost-effective solution for data-intensive applications.

With a well thought-out strategy you can not only drive innovation, but also increase your efficiency and hold your own against the competition in the long term. novalutions supports you in finding the right solution - customised, precise and innovative. Let's work together to develop the AI strategy that fits your company perfectly and will help you future-proof!

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