How to Choose Between a Commercial and an Open AI Model

“For data security, should we run our own open AI model instead of paying for ChatGPT or Claude?” The answer depends on what task the model will do, how many times a day, on whose data and who bears the consequences of a wrong answer. It rarely follows from beliefs about one kind of model being superior to the other.

Applies to GPT Claude Gemini Qwen3 Mistral Bielik gpt-oss

Three categories instead of two

The first category is a commercial model available only through the provider’s API, such as the GPT, Claude and Gemini families. The second is an open-weights model run by an external hosting provider: the weights are public, but the infrastructure still does not belong to the company. The third is an open-weights model run on your own hardware or in your own private cloud.

It is worth separating open source from open weights. Most popular open models publish their weights and architecture, but the full training set and training code stay closed. In business use the difference matters mainly when auditing data provenance.

Data confidentiality

This is the most common reason for talking about open models and also the argument made least precisely. Commercial providers today offer zero-retention agreements, exclusion of customer data from training and a choice of processing region in the European Union, also indirectly through cloud platforms. For most company data that is sufficient protection and a much shorter route than building your own infrastructure.

Cost at scale

Open models carry no license fees, but they do carry running costs. Renting a single H100-class compute card costs a few dollars an hour in specialized GPU clouds (graphics processing units), and noticeably more at the large public cloud providers. A card running non-stop costs several thousand zloty a month for the hardware alone, before adding team time, monitoring and upkeep.

The hardware scale is often smaller than intuition suggests. OpenAI states that gpt-oss-120b fits on a single 80 GB card and gpt-oss-20b in 16 GB of memory, which is a well-equipped workstation.¹ Both are published under the Apache 2.0 license.

At low and irregular volume the API wins almost always, because you pay only for usage. The break-even point appears with high, predictable volume of repeatable tasks: ticket classification, field extraction from documents, first-pass summarization. Before a migration plan, count the measured request volume from the last 3 months, not an estimate of it.

Quality in Polish

Commercial models keep a noticeable edge on tasks that need stylistic freedom: drafting letters, handling register, working with legal text. The gap has narrowed considerably, though.

For Polish comparisons there is the Open PL LLM Leaderboard run by SpeakLeash on Hugging Face, based on Polish NLP (natural language processing) tasks. It measures classification, reading comprehension and closed questions, so it says little about editorial quality. The decision needs your own test set: 50 to 100 cases from the company, judged by the people who do that work every day.

Control over the version

An API provider can retire a model version, change its behavior or tighten content filters, and a process that passed validation will then start returning different results. An open-weights model downloaded once works the same way a year later, as long as the company maintains the environment. For systems under validation, audit or compliance documentation, that repeatability has value in itself.

License and accountability

Read an open model’s license before building a product on it. The Llama 4 license contains the clause: “With respect to any multimodal models included in Llama 4, the rights granted under Section 1(a) of the Llama 4 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union”.⁵ The whole Llama 4 family is natively multimodal, so for a company based in the EU this is a legal barrier independent of model quality. The Apache 2.0 license, under which Qwen3, Mistral Large 3 and gpt-oss are published, has no such restriction.

Buying an API, a company also buys a contract, a service level, a support channel and an entity responsible for the service. Running an open model, it takes over that role entirely: availability, security updates and answer quality.

The EU AI Act provides general-purpose models released under free and open licenses with a limited exemption from some documentation obligations, provided the weights and architecture are publicly available and the model is not monetized. The exemption does not cover models with systemic risk, and the obligations on copyright policy and a training data summary remain. A deployer’s obligations exist regardless of whether the model is open.

A mixed architecture

Most organizations end up combining both approaches:

The boundary between the two is set once, written into policy and reviewed every quarter, because both sides of the market change faster than an annual budget cycle. We prepare model selection, data policy and the deployment plan as part of the AI Strategy package.

Frequently asked questions