Open AI models in production: costs and infrastructure

The APIs of the big AI providers are convenient, but at some point someone in the company asks the question: why don't we host an open model ourselves and stop paying per use? The honest answer is that open AI models in production can pay off, but only if you're clear about the infrastructure costs and the operational responsibilities you're taking in-house. Let's look at what to evaluate before starting, with the experience of people who design and maintain infrastructure for a living.

When an open model makes sense

There are three solid reasons to bring an open model in-house, and it pays to be honest about which one is yours.

• Data control. If you work with health, legal or otherwise sensitive data, keeping inference on your own machines or in a cloud under your control simplifies compliance and the conversations with your privacy officer.
• High, predictable volumes. Pay-per-use APIs are cheap when volumes are low. When you process millions of requests per month with steady loads, dedicated hardware can become competitive.
• Deep customization. With an open model you can work on aspects the APIs don't expose, from fine-tuning to full control of the inference pipeline.

If instead the reason is only the appeal of open source, stop: the model's free license is the smallest line on the total bill.

On-premise or cloud: the first decision

The infrastructure choice comes before the model choice. There are three routes.

Cloud GPUs. You rent the compute and pay by the hour. It's the right way to start: it lets you test different models, understand the real sizing and scale without purchases. The risk is keeping it running for years while paying as if it were a test.

On-premise servers. You buy the machines and put them in-house or in a datacenter. It makes sense when data requirements demand it or when the loads are so steady that the amortization works out. But you take on power, cooling, spare parts and the person who looks after it all.

Providers hosting open models. A middle road: you use an open model through the API of a vendor that serves it for you. You give up part of the control, but you avoid managing the hardware.

In the projects we manage we almost always start from the cloud for the validation phase, and consider on-premise only with real load data in hand. Sizing servers and infrastructure on eyeball estimates is the fastest way to buy the wrong iron.

Quantization and sizing

An open model is almost never used in its full form: you pick a variant or a quantization that fits in the memory you have, with the quality you need. The principles to hold onto:

• quantization reduces memory and costs, but it can degrade quality in ways you won't see until you test on your specific use case;
• a smaller model that answers your task well beats a huge generic model, both in latency and in costs;
• sizing must be done on the peak of simultaneous requests, not on the daily average: the peaks are where the user is waiting.

Build an internal test set, with cases taken from your domain, and compare the variants on that. It's the only benchmark that matters for you.

Monitoring: the part nobody prepares for

A model in production is a service like any other, and it should be monitored like any other, plus a few specific items:

• latency and throughput, with alerts on the high percentiles, where user frustration hides;
• output quality over time: sample the responses and have them reviewed periodically, because silent degradation is real;
• GPU saturation, to understand when to scale and when instead you're paying for unused capacity;
• fallback: what happens when the model doesn't respond? You need a graceful reply, a queue, or routing to a backup external API.

Add request logging from day one, with privacy in mind: without historical data, every future decision on models and sizing goes back to being a bet.

The real costs: not just the GPU

When we prepare a quote for this kind of project, the hardware line is just the beginning. The full bill includes:

• power and cooling, if you're on-premise;
• updates to the model and the pipeline, because the open ecosystem moves fast and standing still means accumulating debt;
• people's time: someone has to apply the security patches, handle the peaks, answer the alerts at night;
• the staging environment, because testing updates directly in production is a bad habit here too.

The fair comparison with pay-per-use APIs is done on this total, projected over two or three years, with your real volumes. Sometimes open wins, sometimes it doesn't: it depends on your numbers, not on the ones read in an enthusiastic post.

Need a hand with the infrastructure?

We design and manage servers and infrastructure for companies that want to bring AI workloads into production, from initial sizing to monitoring, on-premise or in the cloud. If you're evaluating an open model and want to understand what it would cost in your specific case, book a free call: we'll go over volumes, data requirements and alternatives together, before you sign hardware orders.