Local LLM or OpenAI, Claude and Gemini?
When should you self-host a model or use OpenAI, Claude or Gemini? Compare quality, privacy, operating work and costs for a business deployment.
Syntalith
A business sending a dozen requests a day is buying something different from a team processing documents all night. The first mainly needs access to a model. For the second, utilization of owned hardware starts to matter. Both need useful answers and clear data rules.
We consider a private model when a client needs control over processing location, has a recurring workload or wants to adapt a model to a narrow task. OpenAI, Claude and Gemini APIs remain useful quality and cost baselines.
Compare the same work
| Question | Self-hosted model | Managed API |
|---|---|---|
| Who operates inference? | Your team or an implementation partner | The service provider |
| Where does the input go? | To the environment you design | To the environments and regions covered by service terms |
| How do you change models? | Replace weights and configuration, then test the server | Select an available model and check application compatibility |
| What do you pay for? | Hardware or rental, operations, power and spare capacity | API usage plus integration and quality control |
| What about fine-tuning? | The model, licence and tooling determine the options | Availability depends on the particular service |
OpenAI also publishes gpt-oss, and Google provides Gemma. The distinction between local inference and APIs therefore depends on the product and deployment method. A company name alone is insufficient.
Privacy requires checking the actual product
OpenAI states that API data is not used for model training by default unless the customer opts in. Retention is separate: default abuse monitoring logs may be kept for up to 30 days, and some features store application state.
Anthropic describes a default of not training on commercial product inputs and outputs. Permission to share data or submitted feedback can change the applicable treatment. Retention requirements need to be checked for the chosen service and features.
Gemini API terms distinguish paid and unpaid services and include regional provisions. These terms should not be transferred to the consumer Gemini app or Vertex AI. For a Polish business, we examine the applicable product, account, region and agreement.
These are technical and contractual review points, not a guarantee of GDPR compliance. Self-hosting also needs access and retention controls.
Where private inference can pay off
At a factory disconnected from the internet, local operation can be a functional requirement. At a law firm, it may follow from a document policy. In document operations, steady demand and a smaller model adapted to a recurring format can justify an evaluation.
An individual who already owns a suitable computer has a different starting point. Initial costs may mainly involve configuration and learning time. Performance still needs to be checked on the intended tasks.
Where an API makes the project easier
With irregular traffic, a server can spend most of its time idle. An API avoids operating GPUs, although integration, cost controls and output verification remain application responsibilities. If a local model misses the quality requirement, a stronger hosted baseline helps establish the price of better results.
A mixed setup is possible. A local model classifies documents; difficult cases go to a person or an agreed API. That split needs an explicit rule for the data permitted to leave the environment.
Syntalith evaluates deployment options on your task. Discuss a private LLM to compare quality and total cost. Our team includes AWS Machine Learning specialists, and we assess infrastructure outside AWS as well.
Evaluate private AI for your organization
We help businesses and individuals select hardware, deploy a model and test it on their own tasks. Start with a computer you already own or ask us before buying one.
Private LLMs and fine-tuning