Our previous insight in this series asked where your AI model runs. This one asks what that choice costs, because the two questions turn out to be the same question seen from different sides.
The spend is no longer a rounding error. Gartner forecasts worldwide spending on foundation AI models to roughly double in 2026, from $11.4 billion to $23.4 billion, and expects this to be the first year in which global cloud spending on running models exceeds spending on training them. In a pilot, the per-request bill is noise. At production scale, it becomes a budget line.
Two different shapes of cost
Hosted AI, accessed through a provider’s API, is billed by use: you pay for every request, every time, measured in tokens – the units, roughly three-quarters of a word each, in which models process text. That is ideal when volume is low or unpredictable – you pay only for what you use. But it is a cost that never stops, and it climbs fastest exactly when a use case succeeds and gets busier.
Running an open model yourself inverts the shape. The cost is mostly fixed: you pay for the infrastructure whether it handles one request or a million, so the cost per request falls as volume rises. Crucially, “yourself” no longer means buying and racking hardware. An open model can run on your own servers, in a private cloud, or on dedicated GPU capacity in a sovereign European cloud such – keeping the fixed, predictable cost shape, and the guarantee that data stays in-jurisdiction, without the capital outlay.
Where the line crosses
The crossover depends on what you would otherwise pay. Published estimates put it in the hundreds of millions of tokens a month against premium hosted models, and in the billions against low-cost hosted open models – volumes many individual workloads do not reach. And the target keeps moving: the price of any given level of capability is falling steeply, which tends to push the crossover higher still.
What decides it is whether the infrastructure stays busy and who keeps it running. A sovereign-cloud deployment and a managed platform take the second off your plate; the first depends on your workloads. For pure cost, that still means the answer for most organizations is selective: own the highest-volume, most predictable work, and rent the rest.
When cost isn’t the deciding factor
The calculation changes entirely when the data cannot leave. For patient records, financial transactions, or anything under residency rules, running the model in-house – or on a sovereign cloud is not chosen because it is cheaper; it is chosen because it meets the requirement, and cost stops being the deciding variable. This is where the economic case and the sovereignty case meet: the same architecture answers both.
What this looks like by industry
In high-volume operations – logistics, retail, hospitality, customer service – the case is primarily economic. A workflow handling millions of documents, shipments, or interactions a month is the steady volume at which owned inference pays back, turning a per-request bill that grows with success into a fixed cost you already carry.
In financial services and healthcare, the two arguments point the same way. Volume is high and the data is sensitive, so the cost logic and the compliance logic reinforce each other – and where the data cannot leave, the “is it cheaper” question is settled before it is asked. The public sector reaches the same place by a third route, where the deciding factor is jurisdiction rather than cost or volume.
Make it a calculation, not a default
Most organizations will run a mix, and the value is in choosing deliberately rather than sending everything to an external service by habit. This is how Sirma.AI Enterprise is built: through its integration with Hugging Face, open models can be brought in and run alongside hosted ones – on-premise, in a private cloud, or on a sovereign cloud, with each task assigned to the most appropriate model by cost, performance, and sensitivity. The operational work of running open models is handled by the platform rather than added to your team’s workload. Adopting an open model becomes a setup decision, not a new hire.
Where this leaves you
The question is not whether hosted AI is too expensive; for plenty of work it is exactly right. It is whether you are paying per request by default, across everything, or choosing where that makes sense – and where owning the model would cost less and expose less. One is a habit. The other is a decision.
Sirma has worked in AI since its founding in 1992 and builds systems for regulated industries; it is incorporated in the EU and listed on European exchanges. Sirma.AI Enterprise applies that experience as an architecture: your choice of models, run where you need them, at a cost you can see and control.
If you want to know where your own crossover sits, we can map your highest-volume workloads and show where owning the model would pay back.
Learn more about our platform: Sirma.AI
Sources
- Gartner – Worldwide AI Platforms and Models Market to Grow 63% in 2026 (20 Jul 2026)
- Gartner – Worldwide AI-Optimized IaaS Spending to Grow 96% Through 2026 (10 Aug 2026)
- KKRF – Self-Hosted LLM vs API: Enterprise Cost & Security (2026)
- Cohorte – Self-Hosted LLM Cost vs API: The Second Break-Even
- TechSpot – Epoch AI on the falling cost of AI performance (Oct 2026)
