When a business decides to implement artificial intelligence in its processes, sooner or later the question arises: do we use the OpenAI API, Anthropic‘s, Google’s, or do we build something with open-source models? Most articles that answer this question do so from a technical perspective — performance metrics, benchmark scores — which is not what matters when what you’re deciding is your company’s AI architecture.
This is the answer from the perspective of whoever has to make that business decision.
The lay of the land
There are two types of large language model available for businesses today.
Proprietary models —GPT-4 from OpenAI, Claude from Anthropic, Gemini from Google— are systems that run on the provider’s infrastructure. You send a request via their API, they process it on their servers and return the response. You pay per use: for every text you process, every response you generate, every token that passes through the system.
Open-source models — LLaMA from Meta, Mistral, Qwen, and a list that grows every month — are models you can download and run on your own infrastructure. They are free to use, modify and adapt. You can run them on a server you control, with the data you choose, without any third party seeing what you process.
Meta’s LLaMA family is the most relevant for business use in this second group. LLaMA 3,released in 2024,is a model that in many benchmarks approaches the performance of GPT-3.5and in some specific contexts competes with more advanced models, depending on how it is configured and for what task it is used.
The cost argument that is not just about cost
When someone at BAI explains to a client why we use local LLaMA instead of the OpenAI API for the products we build, the conversation always starts with cost. And cost matters: the difference between paying for each API call and having the model running on your own server can be one to two orders of magnitude for high usage volumes.
But the most important argument is not money. It is predictability and sovereignty.
When you use a proprietary API, you are betting on that provider’s continuity, on the stability of their prices, on them not changing their data use policies, on the model’s performance remaining consistent over time, and on them not suffering a service outage precisely when you need it most. OpenAI has had outages. It has changed its prices. It has modified the behaviour of its models in updates. It has changed its data use policies. All of that affects the applications that depend on its infrastructure.
With an open source model running on your infrastructure, the model that works today will keep working the same way in twelve months. The cost per inference does not change because OpenAI decided to readjust its prices. The data you process does not leave your servers.
When GPT-4o Claude are the right choice
Proprietary models have real advantages that shouldn’t be ignored.
The first is quality in complex tasks. For sophisticated reasoning, for analysing very long documents, for generating complex code, for synthesising information with many nuances, the most advanced models from OpenAI and Anthropic remain superior to what you can set up with a local LLaMA on standard hardware. The difference can be substantial in tasks where the quality of reasoning matters greatly.
The second is ease of implementation. Integrating the OpenAI API into an application takes hours. Setting up the infrastructure to run LLaMA locally — a server with sufficient GPU, Ollama or another runtime, model management, monitoring — takes days and requires specific technical knowledge.
The third is maintenance. A local model to update, an infrastructure to monitor and maintain, potential hardware problems: all of that has an operational cost that does not exist when you use an API.
The hybrid architecture that makes most sense
For most business applications, the optimal architecture is not choosing one or the other: it is using each type of model where it makes most sense.
High-volume, low-complexity processes — text classification, structured data extraction from documents, FAQ response generation, quick summaries — benefit from local models for reasons of cost, speed, and privacy.
Low-volume, high-complexity processes — contract analysis, reasoning over strategic scenarios, generation of highly elaborate content, tasks where quality has a high direct economic impact — justify the cost of using high-end proprietary models.
At BAI we build our products on this logic. High-volume, repetitive processes run on local LLaMA on VPS Hetzner. For cases where reasoning quality is critical, the architecture allows external APIs to be used on a targeted basis.
The factor that will change everything in the next twenty-four months
LLaMA 3 is notably better than LLaMA 2.LLaMA 4 will be notably better than LLaMA 3.The quality gap between the best open source models and the best proprietary models is narrowing at a faster rate than most of the industry anticipated.
Over a one-to-two-year horizon, it is reasonable to expect that high-end open-source models — running on specialised hardware — will reach the performance that today only the most advanced proprietary models offer. For businesses building their AI architecture now, it makes sense to design it in a way that can take advantage of that evolution without having to rebuild everything from scratch.