Local LLM vs ChatGPT: How to Decide Under KVKK

In short: There is no single right answer to local LLM vs ChatGPT for every company. Cloud LLMs such as ChatGPT run on the provider's infrastructure, while a local (on-premise) LLM runs the model inside the company's own network. The decision depends on how sensitive the data is, whether it may leave the company, and whether there is a team to run the system. Most companies use both through a single entry point.

For companies, local LLM vs ChatGPT is rarely the choice of one product. It is a setup that decides which work goes to which model, which is why the answer to which one a company should choose depends on the task: in the same company, marketing copy can be processed in the cloud and a customer file in-house.

The same holds under KVKK, Turkey's personal data protection law. A local setup alone does not mean KVKK compliance, and using ChatGPT alone does not mean a KVKK breach. What decides it is which data you process, for what purpose, on what legal ground and under which contract.

1. If the work uses confidential data that must not leave the company, run the model in-house as a local LLM.
2. For work that is not sensitive, if you need a strong general-purpose model and a quick start, ChatGPT Business, ChatGPT Enterprise or the OpenAI API can fit. If personal data is involved, read the provider's current data processing terms first.
3. Most companies need both. Classify the data first, then route the work to the right model. A single company entry point is better than employees using personal ChatGPT accounts without control.
4. A local setup makes the cross-border transfer question smaller, but it does not end the KVKK obligations. Purpose, legal ground, retention, access, processors and security are still the data controller's job.
5. If you have no team to run a GPU server and the service, do not start with a bare local setup. Get a preinstalled or managed option, or stay with the cloud for suitable work.

ChatGPT is OpenAI's chat product. What matters for a company is which plan it uses: besides individual plans, OpenAI offers business plans such as ChatGPT Business and ChatGPT Enterprise and an API for your own applications. We cover the plan limits in ChatGPT usage limits.

OpenAI's Enterprise privacy page lists its commitments on business data for ChatGPT Business, ChatGPT Enterprise, ChatGPT for Healthcare, ChatGPT Edu, ChatGPT for Teachers and the API Platform, and says the data in these products is not used to train models by default. According to the same page, OpenAI can sign a Data Processing Addendum (DPA) with customers for ChatGPT Business, ChatGPT Enterprise and the API. Individual plans are not on that list.

How long data is kept also depends on the plan. According to OpenAI's Enterprise privacy page, on ChatGPT Business the workspace admins set the retention period. Deleted conversations are removed from the systems within 30 days, unless longer retention is required by law or is reasonably necessary to protect the services or a third party from harm. On the API, inputs and outputs can be kept for up to 30 days to detect abuse, and zero data retention can be requested for eligible endpoints.

A cloud LLM API means your own application sends requests to the model through the provider's servers. The data is still processed on the provider's infrastructure. For credits and billing, see our ChatGPT Business API credits guide.

A local LLM (also on-premise LLM or in-house model) means the model and the data run on the company's own infrastructure. Questions, documents and answers do not have to go to a cloud provider. We explain what the model is in What is an LLM? and the hardware and setup in our guide to running an LLM locally.

A local setup uses open-weight models, and there are models that run locally for Turkish work too. RuneLab's Hati Glint SQL is one of them: a 9-billion-parameter open model that turns Turkish questions into SQL and can call tools. Its bf16 weights need about 16.8 GB of GPU memory. A system that answers Turkish questions about a company database can be built with it without going to the cloud. We explain how it works in Ask your database in Turkish.

The first step is to choose the data, not the model. The work examples in the table are representative.

The generative AI guide of Turkey's Personal Data Protection Authority gives a similar example. A company's HR unit summarizes candidates' CVs with an LLM-based system that also gives pre-assessment scores against the job criteria. According to the guide, the company that decides which data goes into the system and how the outputs are used may be considered the data controller. So whether the model runs in the cloud or on your own server, the responsibility usually stays with you.

Four points affect the choice of model: who the data controller is, the legal ground, whether data leaves the country, and security.

Article 3 of Law No. 6698 defines the data controller as the person who determines the purposes and means of processing personal data, and the data processor as the person who processes personal data on the controller's behalf under the authority it gives. OpenAI's Data Processing Addendum says OpenAI acts as a Data Processor on the customer's behalf when it provides the services. The Authority's guide stresses that the parties' actual control, more than the contract wording, decides the roles.

Article 4 of the law requires data to be processed for specific, explicit and legitimate purposes, limited to the purpose and kept only as long as needed. Articles 5 and 6 list the conditions for processing. These rules do not depend on where the model runs. A local model does not make processing without a legal ground lawful.

That is the main question on the cloud side. According to the Authority's guide, when data controllers in Turkey use generative AI through providers abroad and personal data is transferred abroad, the transfer must follow Article 9 of the law and the cross-border transfer regulation published in the Official Gazette on 10 July 2024. Article 9 ties a transfer first to an adequacy decision and, without one, to appropriate safeguards. One safeguard is the standard contract announced by the Board, which must be notified to the Authority within five business days of signing. Without these, transfers are possible only on an incidental basis.

On OpenAI's side, as of 6 November 2026:

• According to the data residency article, the storage regions for ChatGPT Enterprise and Edu include Europe (EEA and Switzerland), the UK and the US, among others, but not Turkey. Inference residency, where the model also runs in the region, is offered only for Europe, the US and the United Arab Emirates.
• According to the ChatGPT Business article, the selected region covers storage only and does not decide where requests are processed. Even with a non-US region, a copy of every prompt and response is kept in the US for a limited time. Region selection for Business is rolling out gradually and is not yet available to all customers.
• The addendum's international transfer section covers data from the EEA, Switzerland and the UK. It has no clause specific to KVKK or the Board's standard contract.

This does not mean ChatGPT cannot be used. For work with personal data, you need to make the Article 9 assessment yourself and settle how the transfer is set up with the provider.

Article 12 of the law requires the data controller to take every technical and administrative measure needed to prevent unlawful processing and access. If someone else processes the data on your behalf, you are jointly responsible with them for these measures. The Authority's guide on generative AI and personal data protection (in 15 questions), published on 24 November 2025, lists example measures: a data protection impact assessment, controls against prompt injection and jailbreak attempts, multi-factor authentication, secure storage of system logs and employee training. A model on your own server is not secure without these measures either.

In practice most companies use both routes. The setup works like this:

1. Applications and employees connect to the models through a single company entry point (an LLM gateway).
2. Each request is assessed by its data class and how hard the task is.
3. Work with sensitive data goes to the in-house local model, suitable work goes to the cloud model.
4. Every request is logged and team budgets are tracked.

You can build this with an open-source gateway, a cloud provider's tools or a ready-made product. RuneLab's RuneGate is a ready-made example of the setup: it offers a single OpenAI-compatible endpoint, classifies how hard each request is and routes the work to the most cost-effective model that can do it, stops requests before a team budget is exceeded and logs every request. The optional RuneGuard module replaces sensitive information in a request with placeholders before it reaches the model provider and puts the right value back for each placeholder in the answer. If the data must never leave the company, RuneGate runs with local models on RuneBox, and it is also offered as a managed cloud service.

The setup works when employees know which data they may enter into which tool. RuneLab's AI Awareness Training is a one-day hands-on course on using generative AI tools in the right place and safely.

On the cloud side the cost is a bill per user or per use. If the data has to be kept in a particular region, check the extra terms too: according to OpenAI's developer documentation, data residency endpoints on the API are priced with a 10% uplift for eligible models released on or after 5 March 2026.

On the local side the cost is hardware and operations: the GPU server, model updates, security patches, monitoring and the team that runs them. Because capacity is fixed there are no surprise bills, but demand above capacity has to wait. We explain how much VRAM each model needs in detail in our post on running an LLM locally. The break-even point depends on usage volume, the chosen model and staff costs, and differs from company to company.

A preinstalled setup reduces the operating load. RuneLab's RuneBox is a ready on-premise LLM setup installed in the company network: models, RAG and agent flows run on an NVIDIA GPU box in a closed network with no outside connection. In the product page's words, because data is processed within the organization's boundaries it makes your BDDK and KVKK assessments easier and no data is transferred abroad or to third parties, while compliance remains the organization's own assessment: RuneBox provides the architecture that makes that assessment easier and gives no legal undertaking. If your own team will run the setup, Local LLM Training teaches how to run open models on your own servers, from model choice and GPU planning to setup with vLLM and Ollama. To weigh the cost and risk of both routes together, there is our Cloud LLM APIs vs. On-Premise Models: Cost, Risk and Regulation course.

No. What decides it is which data you process, for what purpose, on what legal ground and under which contract. If personal data is transferred abroad, you need a transfer route that follows Article 9 of KVKK.

Not automatically. A local setup makes the cross-border transfer question smaller, but purpose, legal ground, retention, access rights and the security measures of Article 12 remain the data controller's obligations.

Yes. You can set up a single entry point (an LLM gateway) that classifies the data and routes sensitive work to the in-house local model and suitable work to the cloud model.

The model, the documents and the logs must run in the company network. That takes a local LLM running in-house and outside integrations such as web search turned off. Access rights and logs must also be kept in-house.

It depends on usage volume. In the cloud you pay per user or per use. With a local setup you pay for hardware and operations. Because local capacity is fixed there are no surprise bills, but demand above capacity waits.

It depends on the size of the model and how many people use it at the same time. We cover VRAM needs and the setup steps in our post on running an LLM locally.

No. Local or cloud is about where the model runs. RAG or fine-tuning is about how you give the model your company's knowledge. For the second, see our post on the difference between RAG and fine-tuning.

• Turkey's Personal Data Protection Authority (KVKK), guide on generative AI and personal data protection, announcement of 24 November 2025; full text of the guide, publication no. 113: questions 7 (example 4), 10 and 13.
• Law No. 6698 on the Protection of Personal Data, current text: Articles 3, 4, 5, 6, 9 and 12.
• OpenAI, Data Processing Addendum: roles and the international transfer section.
• OpenAI Help Center, Data residency and inference residency for ChatGPT and Where your ChatGPT Business content is stored.
• OpenAI, Enterprise privacy at OpenAI: retention on ChatGPT Business and the API, zero data retention.
• OpenAI developer documentation, Data controls in the OpenAI platform: the uplift on data residency endpoints.
• RuneLab, Hati Glint SQL model card: model size and bf16 memory needs.

• How to run an LLM locally
• What is an LLM? Large language models explained
• RAG vs fine-tuning
• ChatGPT usage limits explained
• Courses: Local LLM Training, Cloud LLM APIs vs. On-Premise Models