For the first wave of generative AI adoption, the infrastructure decision was relatively simple: connect to an API.
Companies could tap powerful models from OpenAI, Anthropic and other providers without buying GPUs, deploying inference servers or maintaining the increasingly complicated software stack required to run a large language model.
That equation is beginning to change.
A growing ecosystem of downloadable (or more accurately, open-weight) models is giving companies another option: run the AI themselves. Models from developers including Meta and Mistral can be deployed on infrastructure controlled by the organization, while tools such as vLLM have made serving those models considerably easier. Mistral, for example, explicitly documents how its models can be self-deployed on private infrastructure using inference engines such as vLLM, TensorRT-LLM and TGI.
The result is an emerging technology debate that sounds familiar: rent the platform or own the stack?
Why Proprietary Models Still Have an Advantage
For most organizations, proprietary AI remains the path of least resistance. An API eliminates much of the infrastructure work. The provider handles the model, GPU clusters, scaling, optimization, security updates and new model releases. Developers primarily worry about integrating the service into their applications.
The strongest commercial models also tend to arrive with sophisticated tool use, multimodal capabilities, reasoning features and managed enterprise controls.
Data privacy concerns have also become more nuanced than the early argument that sending information to an outside AI provider automatically means surrendering control of it. OpenAI, for example, states that data submitted through its business products and API is not used to train its models by default. It also offers enterprise security controls and zero-data-retention options for eligible API use cases.
Anthropic similarly maintains enterprise security and compliance programs covering Claude and its API, including certifications and configurations for regulated environments.
For a company experimenting with AI or running workloads that fluctuate dramatically, paying for model access can therefore make far more sense than owning the infrastructure underneath it.
The Case for Hosting Your Own LLM
Self-hosting becomes more interesting as AI moves from experimentation into permanent infrastructure.
The obvious attraction is control. A company running an open-weight model can determine exactly where inference occurs, how data moves through the system and which security controls surround it. Sensitive information may never need to leave the organization’s environment.
There is also considerably more freedom to customize the stack. Companies can select the model, inference engine, quantization method, retrieval architecture and hardware rather than accepting whatever configuration an API provider exposes.
Meta has positioned its Llama family around precisely this kind of developer control, describing the model as one component of a broader system that organizations can design around their own requirements.
The infrastructure tooling is improving as well. vLLM can expose self-hosted models through an OpenAI-compatible API, potentially allowing developers to change the underlying model without completely rebuilding applications.
Meanwhile, techniques such as quantization can dramatically reduce memory requirements. Hugging Face notes that converting model weights from 32-bit to 16-bit precision can cut model size roughly in half, with even lower-precision approaches making large models practical on less expensive hardware.
But… Running It Yourself Isn’t Free
This is where the economics get complicated. An open-weight model may not charge a per-token API fee, but somebody still has to pay for GPUs, electricity, storage, networking and engineering. And inference is only the beginning.
Production environments need monitoring, load balancing, security, model updates, evaluation systems and enough redundancy to keep AI applications available when hardware fails.
NVIDIA’s own documentation for self-hosted NIM deployments illustrates the infrastructure involved: supported GPUs, CUDA drivers, container software, memory requirements and multi-GPU configurations can all become part of the equation.
In other words, companies can eliminate the AI provider’s margin while simultaneously becoming their own AI provider.
That tradeoff may be attractive at high utilization. It may be terrible economics for a lightly used corporate chatbot.
The Answer May Be Hybrid
The decision increasingly does not have to be binary.
Organizations can reserve proprietary frontier models for difficult reasoning, advanced agents or infrequent high-value tasks while routing routine workloads to smaller models they control.
Customer support classification, document extraction, internal search and structured content generation may not require the most powerful model available.
That creates a new architectural question. Instead of asking, “Which LLM should we use?”, companies may increasingly ask, “Which model should handle this particular workload?”
For organizations with large AI volumes, strict data-control requirements or substantial technical infrastructure, hosting an LLM is becoming a credible alternative rather than an experimental project.
For everyone else, proprietary APIs still offer something remarkably valuable: somebody else has to keep the GPUs running.