Advantages of local generative AI for enterprises

The rapid spread of generative AI has led many companies to adopt cloud-based AI services. However, from the standpoint of handling confidential data and managing costs, running generative AI models in a local environment (local LLMs) is drawing attention. This article examines the advantages of local generative AI models and their impact on enterprise AI strategy.

The decisive advantage in data security

The greatest benefit of local generative AI models is their absolute advantage in data security. Because a company’s confidential data is never sent outside the on-premises environment, the risk of data leakage drops significantly. This is especially important in industries such as the following:

• Financial services: Protecting customers’ financial information and transaction data
• Medical and healthcare: Protecting patients’ personal information and medical records
• Legal services: Protecting confidential communications with clients and legal documents
• Government and public sector: Protecting information related to national security

With cloud-based AI services, data is sent to and processed on external servers, which makes it difficult to ensure this level of security.

More predictable costs

Another major advantage of local generative AI models is cost predictability. Cloud-based AI services typically use pay-as-you-go pricing based on the number of API calls or tokens. This means costs can rise sharply as usage grows.

In contrast, local generative AI models require an upfront investment (hardware and software setup), but operating costs after that are relatively stable. For companies with heavy AI usage in particular, local generative AI models may prove more cost-effective in the long run.

Lower latency

Lower network latency is another important advantage of local generative AI models. With cloud-based services, time is spent sending data, processing it, and receiving the results. Local generative AI models eliminate this network delay and enable faster responses.

In applications that require real-time performance, for example:

• Customer support chatbots
• Real-time document analysis
• Interactive decision support systems

this reduction in latency becomes an important competitive advantage.

Flexibility in customization and control

Local generative AI models greatly increase flexibility in customizing and controlling the model. Companies can fine-tune models to fit their specific needs and industry. By controlling model behavior and parameters in detail, they can also obtain more accurate and consistent results.

With cloud-based services, this level of customization is usually limited, and companies depend on the features the service provider offers.

Implementation challenges and solutions

Adopting local generative AI models also involves several challenges:

1. Compute resource requirements: Large language models require high-performance GPUs and memory.
2. Technical expertise: Deploying and managing models requires specialized knowledge.
3. Scalability: Expanding the system as demand grows is a challenge.

The following solutions can address these challenges:

• Optimized models: Using smaller models optimized for the company’s needs
• Specialized platforms: Using platforms that simplify the deployment and management of local generative AI models
• Hybrid approach: A strategy of using cloud services for non-confidential data and local generative AI models for confidential data

Conclusion: rethinking enterprise AI strategy

Given the advantages of local generative AI models, companies need to rethink their AI strategies. Local generative AI models are an attractive option, especially for companies where data security is a top priority and for companies with heavy AI usage.

Descarty’s Kitewell lets customers choose which AI models to use, and it also supports models that run in-house or locally. For details, see Security.

In the future, a hybrid approach combining cloud and local models will likely become mainstream, with each company choosing the best combination based on its own needs and priorities. What matters is to adopt a strategy tailored to each company’s specific requirements, rather than a “one size fits all” approach to AI adoption.