Per-token API bills add up fast once a product moves past the demo stage. Hugging Face gives you a way out — open-source models you can fine-tune, host yourself, and control end to end. We help teams pick the right model, adapt it to their data, and stand up infrastructure that runs it reliably in production.
Closed, per-token APIs are the fastest way to prototype — and often the most expensive way to run something at scale. A dedicated team makes the switch without breaking what already works.
Self-hosted open-source models replace a variable, usage-driven bill with fixed compute cost — often far cheaper at real production volume.
Nothing leaves your infrastructure. For regulated data or proprietary content, self-hosting removes a whole category of vendor risk.
We evaluate open-source models on your actual workload — not benchmark leaderboards — and pick the smallest model that meets your quality bar.
A well-chosen open-source model fine-tuned on your data frequently outperforms a generic model prompted with more context and higher cost.
Open weights mean you can move between hosting providers, cloud GPUs, or on-prem hardware as your cost and scale needs change.
Scoped to where you are today — a quick model evaluation, a full self-hosted deployment, or both.
Benchmarking candidate open-source models from the Hugging Face Hub against your actual prompts, data, and latency requirements.
LoRA and full fine-tuning pipelines that adapt a base model to your domain without needing a research team on staff.
Deployment on your own cloud GPUs or on-prem hardware, with the serving layer, batching, and autoscaling tuned for your traffic.
Moving an existing product off a per-token provider onto a self-hosted open-source model, with a fallback plan while quality is validated.
Connecting fine-tuned or off-the-shelf models to a retrieval layer so answers are grounded in your own content.
Yes. Most open-source models on the Hugging Face Hub can be deployed on your own GPUs or a dedicated inference provider, replacing per-token billing with fixed infrastructure cost. We size the hardware to your actual traffic before you commit.
It depends on the task, latency budget, and data sensitivity — there's no single best model. We run a short evaluation against your real prompts and pick the smallest model that clears your quality bar, rather than defaulting to the largest one available.
Yes. We typically start with parameter-efficient fine-tuning (LoRA) since it's faster and cheaper to iterate on, and move to full fine-tuning only if the task genuinely needs it.
It ranges from a single GPU instance for a smaller fine-tuned model to a multi-node autoscaled cluster for high-traffic production use. We scope the infrastructure to your expected load and budget rather than over-provisioning by default.
Tell us what you're running today and what it costs — we'll scope a model, a fine-tuning plan, and a self-hosted deployment path.