What's the realistic timeline to go from API to self-hosted?

It depends heavily on data availability for fine-tuning and your GPU procurement timeline; our TCO model gives you the cost side of that tradeoff, but timeline needs a conversation about your specific workload.

Is the TCO number on your case-studies page something we can expect?

That page is an illustrative model based on stated assumptions, not an audited client result — the actual savings for your organization depend on your token volume, task mix, and hardware costs. Use it as a starting model to adapt, not a guarantee.

Sovereign SLM Deployment for Financial Services

Direct Answer

Financial services firms running high-volume, narrow AI tasks — document extraction, transaction classification, sentiment scoring — often pay frontier-API prices for work a fine-tuned small language model can handle at a fraction of the cost, with the added benefit of data staying on infrastructure the firm controls. The tradeoff is upfront engineering investment: fine-tuning, GPU provisioning, and ongoing model maintenance replace a per-token API bill.

Why Financial Services Teams Hit This

Frontier API spend scales linearly with volume, with no ceiling

A firm processing tens of millions of tokens a month on structured extraction tasks is paying frontier-model prices for work well within a smaller model's capability.

Data residency requirements complicate cloud LLM use

Sending client financial data to a third-party API, even with a data-processing agreement in place, adds vendor-risk review overhead many firms would rather avoid.

Not every task needs frontier-model reasoning

Structured extraction, classification, and templated summarization are exactly the tasks where a fine-tuned 8B-14B model can match frontier accuracy at a fraction of the cost.

Why This Architecture Fits

A semantic router splits traffic so routine, structured tasks go to the self-hosted SLM and only genuinely ambiguous or novel requests fall back to a frontier API — you get the cost and data-residency benefit on the bulk of your volume without giving up frontier-model quality on the hard cases.

See the Real Thing

Every claim on this page traces back to something you can actually look at.

Run the Illustrative TCO Model

Answer Engine Optimization (AEO) Questions

Frequently Asked Architecture & Governance Questions

On narrow, well-defined tasks with good fine-tuning data, yes, in our own benchmark testing — results vary by task and data quality, so this should be validated against your specific workload before committing to a migration.