Generative AI has stopped being an experiment and become a production tool in thousands of companies. Behind every internal assistant, every automatic summary or every semantic search there is an architectural decision that is rarely made explicit: do we consume AI as a service through a third-party API, or do we host our own open models on infrastructure we control?
It is not a trivial question. The right answer depends on usage volume, data sensitivity, budget and the level of control each organization needs. Choosing badly means overpaying for tokens that spike at the end of the month, or standing up GPU infrastructure that sits underused; or, worse, sending confidential data to a third party when regulation forbids it.
In this article we break down the five decision axes that separate one option from the other —privacy, cost, latency, capabilities and operational effort—, include a direct comparison table and calculate the break-even point at which owning the hardware starts to pay off against paying per token.
Two Ways to Put Generative AI to Work
The first option is to consume a commercial API: OpenAI (GPT), Anthropic (Claude), Google (Gemini) and other providers expose their most capable models through an endpoint. You pay per token consumed, you manage no server, and you access the state of the art without operating hardware. It is the lowest-friction way to start and the fastest way to prototype.
The second option is to host your own open models. The community has released highly capable model families —Llama (Meta), Mistral, Qwen (Alibaba), DeepSeek and gpt-oss, among many others available in repositories like Hugging Face— that you can download and run on your own GPUs. Here the model, the data and the infrastructure are yours: you decide the version, the availability and where the data physically resides.
They are not mutually exclusive, but they do represent different philosophies: one outsources complexity and variable cost; the other internalizes control in exchange for owning the operation. Let's look at the axes on which the balance tips.
Privacy and Data Sovereignty
For many companies, this is the deciding factor. When you send a prompt to a commercial API, your data —which may include medical records, contracts, proprietary code or customers' personal information— leaves your perimeter, travels to third-party servers and, often, crosses the Atlantic to infrastructure subject to jurisdictions outside the GDPR.
For regulated sectors —healthcare, banking, insurance, public administration, defense— that is not always acceptable. The GDPR, national security frameworks and data-sovereignty requirements demand knowing exactly where the information is processed and under what guarantees. Hosting the model on a GPU server inside your own datacenter, or in a certified one in Spain, removes that uncertainty at the root: the data never leaves the infrastructure you control.
With local inference, prompts and responses are not used to train third-party models, are not subject to external retention policies and do not depend on a provider changing its terms of use. For a company working with intellectual property or sensitive personal data, data sovereignty is reason enough for self-hosting, even when a purely economic analysis would not justify it.
The Real Cost: Pay Per Token vs Owning a GPU
An API's cost model is pay-as-you-go: you are billed for every million input and output tokens. For low volumes or occasional workloads it is unbeatable, because there is no fixed cost: if you don't use the AI one month, you pay nothing. The problem appears at scale: when consumption is high and sustained, the bill grows linearly and unpredictably, and a function that spikes in production can become a hard-to-budget expense.
Owning a GPU inverts that logic: it means a fixed cost —CAPEX if you buy the hardware, or a stable monthly fee if you rent it— that does not depend on the number of tokens processed. Once the node is amortized, each additional token has a marginal cost close to zero. It is an expensive model at low usage and very profitable at high, continuous usage, exactly the opposite of the API.
The key is utilization:
A GPU that processes requests continuously has a cost per token far below the API's. A GPU that sits idle most of the day is wasted money. Self-hosting pays off when you can keep the hardware busy with real, predictable load.
The Break-Even Point
The operational question is concrete: above how many tokens per month does owning the hardware pay off? There is no universal figure —it depends on the model, the GPU and the rates of the moment—, but there is a useful order of magnitude as a reference.
With current mid-range API rates and the rental cost of an inference GPU, the break-even usually sits when you process several hundred million tokens per month on a sustained basis. Below that threshold, the API comes out cheaper and saves you the operational complexity. Above it, the fixed fee of one or more owned GPUs starts to win, and the gap widens the larger and more constant the volume.
Two nuances shift that threshold. The first is privacy: if regulation prevents the data from leaving, self-hosting pays off even at low volume, because the criterion stops being economic. The second is the model used: a small, well-quantized open model squeezes a single GPU enormously, bringing the break-even forward; serving a huge model that demands several nodes pushes it back. In our article on GPU servers for AI and machine learning we go deeper into how to size the hardware to the workload.
Latency, Control and Fixed Versions
Beyond cost, hosting the model brings control the API cannot offer. The first is the absence of rate limits: you don't depend on per-minute quotas or on the provider prioritizing other customers at peak times. Your capacity is set by your hardware, and you can saturate it without anyone throttling you.
The second is version stability. API providers update, deprecate or retire models fairly often, and a version change can alter the behavior of a system you have spent months tuning. With your own model, the version is the one you fix: behavior is reproducible until you decide to change it, something critical in environments that require certification or auditing.
The third is latency. When the model runs close to your application, with no hop to the internet and no provider queues, the time to first token can be very consistent. For interactive applications or batch pipelines with strict timing requirements, that predictability matters as much as the average speed.
Capabilities: Are Open Models Up to the Task?
Here it pays to be honest: on the most demanding tasks —complex multi-step reasoning, hard code generation, autonomous agents with many tools— the best proprietary models are still ahead. If your use case lives on that frontier, the API gives you access to the state of the art without investing in your own research.
But the gap has narrowed a great deal, and most enterprise use cases do not live on that frontier. For RAG (retrieval-augmented search over your documentation), text classification, structured data extraction, summarization and internal assistants, today's open models deliver more than enough quality. A well-chosen Llama, Mistral or Qwen, tuned to your domain, solves 80-90% of the real needs of most organizations.
The added advantage of an open model is that you can specialize it: fine-tuning with your own data, adjusting the system prompt, custom quantization. A mid-size model fine-tuned on your vertical can beat a larger generalist on the specific task that matters to you, and do it for a fraction of the cost.
The Operational Effort of Serving an LLM
Self-hosting has a cost that never shows up on the invoice: you have to operate the inference. Serving an LLM in production means choosing a serving engine, managing GPU memory and maintaining the system. The most common tools are:
- check_circle vLLM: a high-performance inference engine with continuous batching and PagedAttention, designed to serve many concurrent requests while squeezing the GPU. It is the reference for production at scale.
- check_circle TGI (Text Generation Inference): Hugging Face's serving solution, with good integration into its ecosystem and simple container deployment.
- check_circle Ollama: the fastest way to get started. Ideal for prototypes, local development and small deployments, with a very simple user experience.
On top of that comes quantization —reducing the precision of the weights to INT8 or INT4 so the model fits in less VRAM and responds faster, with a usually acceptable quality loss—, GPU memory management, monitoring and updates. It is not trivial, but it is not cutting-edge research either: these are well-documented MLOps practices. The relevant question is whether your team can take on that operation, or whether you would rather start from managed infrastructure that arrives ready to infer.
Comparison Table: API vs Self-Hosted
The following table sums up the decision axes and in which scenario each option wins:
| Criterion | Third-party API | Self-hosted (own GPU) |
|---|---|---|
| Privacy and sovereignty | Data leaves the perimeter | Data never leaves |
| Cost at low volume | Very low (pay-as-you-go) | High (idle fixed cost) |
| Cost at sustained scale | High and variable | Low and predictable |
| Latency and control | Rate limits, provider queue | No limits, fixed version |
| Peak capability | State of the art | Enough for most cases |
| Maintenance | None (managed) | Requires MLOps |
| Customization | Limited | Full (own fine-tuning) |
| Typical usage profile | Prototypes, peaks, frontier tasks | High volume, sensitive data |
The Hybrid Approach: The Best of Both Worlds
In practice, the decision is rarely binary. The most mature organizations adopt a hybrid strategy that assigns each task to the option that solves it best, rather than committing to a single one.
The usual pattern is to use an open model of your own for everything sensitive and for the bulk of the volume —mass classification, RAG over internal documentation, extraction of personal data— keeping that data inside the perimeter and at a low marginal cost. And to reserve the commercial API for demand peaks that overflow the hardware and for frontier tasks that require the best available model, where quality justifies the cost per token.
This way you optimize every dimension at once: privacy and cost where volume matters, and cutting-edge capability where difficulty matters. It is the architecture we recommend evaluating before committing to a single provider.
EasyDataHost: Private Inference Hosted in Madrid
If your case points to self-hosting, at EasyDataHost we give you the infrastructure to do it without leaving Spain. We offer GPU servers sized for inference and fine-tuning of open models, and the NVIDIA DGX Spark hosted in our Madrid datacenter, designed precisely for private LLM inference.
- arrow_right Data that never leaves Spain: the model and the prompts are processed on our own infrastructure, with GDPR and ENS compliance, ideal for regulated sectors.
- arrow_right GPUs for every scale: from a single card to serve a quantized model to multi-node configurations with high-speed interconnect for large models.
- arrow_right Fixed, predictable cost: a stable monthly fee instead of a per-token bill that spikes with usage.
- arrow_right Local 24/7 technical support to help you serve your model with vLLM, TGI or Ollama and squeeze every GPU.
You can learn the details of our supercomputing platform in the article on the NVIDIA DGX Spark and AI supercomputing in Madrid. And if you need help deciding between API and self-hosting, contact our team for a no-obligation technical analysis.
Frequently Asked Questions
At what token volume does self-hosting an LLM pay off?
As a rule of thumb, when your monthly consumption sustainably exceeds the equivalent of several hundred million tokens, the fixed cost of an owned or rented GPU starts to be cheaper than paying per token. With privacy or data-sovereignty requirements, self-hosting can pay off even at lower volumes, because the deciding factor stops being price.
Are open models good enough for production?
For most enterprise use cases (RAG, classification, extraction, summarization, internal assistants) open models like Llama, Mistral, Qwen or DeepSeek already deliver more than enough quality. For complex reasoning or very demanding agent tasks, the best proprietary models are still ahead, which is why many companies opt for a hybrid approach.
What GPU do I need to serve an open model?
It depends on the model size and the quantization. A quantized 7-8B model runs on a single mid-range GPU with 16-24 GB of VRAM. 70B models require several GPUs or aggressive quantization, and the largest ones need a node with high-speed interconnect. Quantization (INT8, INT4) reduces memory and cost in exchange for a small accuracy loss.
Conclusion
There is no universal answer to whether you should consume an API or host your own AI: there is a set of axes that, applied to your case, tip the balance:
- arrow_right Privacy rules: if the data cannot leave the perimeter, self-hosting pays off even at low volume.
- arrow_right Cost crosses over at scale: the API wins at low or occasional volume; owning a GPU wins when usage is high and sustained and you keep the hardware busy.
- arrow_right Control belongs to whoever hosts: no rate limits, no surprise model changes, and fixed versions with predictable latency.
- arrow_right Open models already suffice for RAG, classification, extraction and summarization; reserve the API for frontier tasks and adopt a hybrid approach.
The good news is that you no longer have to choose blindly: you can start with an API to validate the use case and migrate to your own inference when volume or privacy justify it. At EasyDataHost we support you in that leap with GPU servers and the DGX Spark hosted in Madrid.