What counts as a private LLM?
Private LLM is used loosely, so define it before comparing costs. At one end is a self-hosted open-weight model running on infrastructure you control, in your data center or your own cloud account. In the middle are dedicated or single-tenant deployments operated by a provider. At the other end is a shared model API used under enterprise terms that govern retention and training on your data.
These options differ in who operates the model, where data is processed and what contractual commitments apply. Many organizations assume they need full self-hosting when their real requirement is a specific data-processing commitment that an enterprise API agreement can meet. Start from the requirement, not the architecture.
Start with the data question
Classify the data each use will send to the model. Public and internal material usually fits a managed API with appropriate terms. Regulated data such as protected health information, cardholder data or confidential supervisory information may carry contractual, residency or examiner expectations that narrow the options. Write those constraints down with your privacy and security teams before evaluating vendors.
Remember that self-hosting alone does not guarantee data stays put. Retrieval pipelines, logging, monitoring tools and fallbacks to external models can all move data outside the boundary. Map the full path from user question to answer and back, including every system that stores a copy.
Ask any managed provider the same concrete questions: where prompts and outputs are processed, how long they are retained, whether they are used for training, who at the provider can access them and how you would demonstrate those answers to an auditor or examiner. Clear written answers often resolve the requirement without new infrastructure. Vague answers are a signal to narrow the data you send or to keep that use on a private deployment.
Related: Review Nexatron deployment and model-provider options
Capability is a measurement, not an assumption
Frontier models offered through APIs generally lead on complex reasoning, long documents and tool use. Open-weight models have closed much of the gap for narrower tasks such as classification, extraction and structured query generation. The only reliable way to know which is sufficient is to evaluate candidates on your own representative tasks with reference answers.
Score the failures, not just the averages. A model that is slightly less accurate but fails predictably may be easier to operate than one with a higher score and occasional confident errors on critical questions.
Total cost includes the work around the model
API pricing is visible per token, which makes it easy to compare and easy to underestimate at scale. Self-hosting shifts cost to GPUs, serving software, capacity planning and staff. Serving frameworks such as vLLM make high-throughput inference practical, but someone still has to patch, monitor, scale and upgrade the stack.
Model the cost of a realistic workload: expected users, questions per day, context length and peak concurrency. Include the people needed to run the platform and the cost of idle capacity. Self-hosting tends to pay off at steady high volume with tight data requirements; APIs tend to win for variable demand and fast access to new model releases.
Operations and accountability
With an API, the provider handles model updates, availability and much of the security of the serving layer, and you manage contracts, access and usage. With self-hosting, your team owns uptime, incident response, model version control and abuse prevention. The OWASP Top 10 for LLM applications applies in both cases, but more of the controls fall to you when you run the model.
Plan model portability either way. Keep prompts, evaluation sets and tool definitions independent of a single provider so you can switch when quality, price or policy changes.
The common answer is a hybrid
Most enterprises end up routing by data class and task. Sensitive, high-volume, well-defined work runs on a private model; complex reasoning on less sensitive data uses a managed API. A routing layer with clear rules and logging makes the split auditable.
Decide with a short scorecard: data constraints, required capability measured on your tasks, three-year cost at expected volume and the operating capacity you actually have. Revisit it every six months, because model quality and pricing move quickly in both directions.