A European fintech company cut its LLM costs 55% without sending a single prompt outside its perimeter.
An anonymized production case for Private LLM Deployment & Operations: the workload moved from a public API to a private, OpenAI-compatible deployment operated by DigitalCare.
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The client is a European fintech company in the mid-market scale bracket, processing sensitive customer documents and support conversations with LLMs at production volume.
The client’s name is withheld under NDA; the workload, stack, and results below are from the production engagement.
Two pains, stated bluntly.
Public API spend scaled linearly with usage: at high tens of millions of tokens per day the monthly bill was on a trajectory toward the mid five figures.
Prompts containing customer PII were routed through a third-party API outside the client’s perimeter, blocking a GDPR data-residency requirement the business needed to close.
Methodology over adjectives.
Evaluation first. Open models were tested against the incumbent API on the client’s real prompts: 500 test generations, blind-scored by the client’s own reviewers.
Model. Qwen3-32B was selected: quality parity on the client’s task mix at a hardware footprint of a single GPU node. Model names don’t identify the client.
The one-line migration. Client code unchanged except base_url: the endpoint is OpenAI-compatible, so SDKs, tooling, and agents kept working on day one.