The Hugging Face agency.Open models, in production.
Hugging Face is the hub for open-source AI, but open weights on a laptop aren't a product. We pick the right open model for your task, fine-tune it on your data, ship it via Inference Endpoints or self-hosted, and wire the MLOps that keeps it reliable.
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GeminiA Hugging Face agency ships open models, not just notebooks.
Anyone can clone a model off the Hub. Picking the right one, fine-tuning it on your data, deploying it reliably, and running the MLOps is a different job. Here are the four things we own.
- Model selection
The right open model, picked from the Hub for your task
The Hugging Face Hub has hundreds of thousands of open models, and the right one for your task is rarely the biggest or the one a vendor pushes. We read your task, your latency budget and your hardware, then pick from the Hub instead of defaulting to a costly frontier API. Sometimes a small open model fine-tuned on your data beats a giant generic one; sometimes the frontier API is genuinely the better call, and we'll tell you which.
See how we choose - Fine-tuning
An open model adapted to your domain and your data
A generic open model is a starting point, not the answer. We fine-tune it on your data so it speaks your domain, your formats and your edge cases, and beats a generic model on your actual task. We prep the dataset, run the training, evaluate against your benchmark, and keep your data yours throughout. The result is a model you own that does your job, not a prompt wrapped around someone else's API.
See the method - Deployment
Shipped via Inference Endpoints or self-hosted, run reliably
Open weights on your laptop aren't a product. We ship the model where it belongs: managed Inference Endpoints when you want it simple, self-hosted on your cloud when you want full control and data ownership. Either way we bring the MLOps that keeps it reliable, autoscaling, monitoring, versioning, fallback, so it stays up under real traffic instead of falling over the first busy afternoon.
See the integrations - RAG, apps & ops
Grounded on your data, with cost and quality under control
A deployed model is the start. We ground it on your data with RAG so answers are accurate, build the Spaces, demos and apps your team actually uses, and wire monitoring so you see drift, cost and latency before your users do. We're an automation and AI agency first, so the open model plugs into your stack and your workflows, not a science project off to the side.
See AI enablement
We run open models like production systems, not experiments.
Most open-model projects die the same way: a model cloned off the Hub, a notebook that works once, no fine-tuning on real data, no MLOps, and it never makes it to production. So we treat it like infrastructure: the right model selected on your benchmark, fine-tuned on your data, deployed reliably, and operated with monitoring and cost tracking from day one.
- Audit · map your task, your data, your latency budget and where an open model beats an API
- Select · shortlist from the Hub and benchmark the finalists on your data, not a leaderboard
- Fine-tune · adapt the model to your domain and prove it beats a generic baseline
- Deploy · Inference Endpoints or self-hosted, with the MLOps that keeps it reliable
We run open models in production with real ops.
We don't sell a partner tier. We come from automation and AI, so we treat an open model like a production system: selected on your benchmark, fine-tuned on your data, deployed reliably, and monitored for drift and cost. That's exactly what's missing when an open-model project ends at a notebook that ran once.
- We come from automation and AI, so we run open models in production with real MLOps, not just spin up a notebook and call it done.
- We're honest about the trade-off: open models give you control, cost and data ownership, but for some tasks a frontier API is still simpler, and we'll say so.
- You leave owning the model: fine-tuned on your data, deployed on your terms, with the ops documented so your team runs it without us.
- No partner badge to sell. We're judged on whether the model ships, stays reliable, and costs less than the API it replaced, not on a tier.
Hugging Face at the core, your stack and ops around it.
We configure the parts that turn an open model into reliable production throughput, then connect them to how your team already works. Here's what a real project covers.
- Setup
Model selection from the Hub
We shortlist open models from the Hugging Face Hub against your task, latency budget and hardware, benchmark the finalists on your data, and pick the one that wins, not the one with the biggest name.
- Setup
Fine-tuning on your data
We prep your dataset, fine-tune an open model so it beats a generic one on your task, evaluate against a real benchmark, and keep your data yours the whole way through training.
- Setup
Inference Endpoints / self-hosting
We deploy via managed Inference Endpoints when you want it simple, or self-hosted on your cloud when you want control and data ownership, with autoscaling and versioning either way.
- Setup
Spaces & demos
We build Hugging Face Spaces to host demos and internal apps so stakeholders can try the model in a browser, and your team can ship a working interface without standing up infra first.
- Setup
RAG & retrieval
We ground the model on your documents with retrieval so it answers from your data instead of guessing, with the embeddings, vector store and chunking tuned for your corpus.
- Setup
MLOps (monitoring, scaling, cost)
We wire the operations that keep an open model reliable: monitoring for drift and latency, autoscaling for traffic, cost tracking so self-hosting actually saves money, and fallbacks for when it doesn't.
We map your use case, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your task, your data, your volume and what you're paying an API today. You leave with an honest read on whether an open model beats your current setup, which one to pick, and what to fine-tune first. Zero pitch, just an engineer's take on your use case.
- An honest read on whether an open model beats your API
- Which open model from the Hub fits your task
- What to fine-tune and on what data
- A frank take on when to just use a frontier API
How we run a Hugging Face project.
Five steps, in order. We don't fine-tune before we've proven an open model is the right call, we don't deploy without the MLOps to run it, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Use-case audit
Map where an open model actually beats an API
We sit down with you and look at the real task: the data you have, the latency you need, the volume you run, and what you're paying a frontier API today. We check whether an open model from the Hub wins on cost, control or data ownership, or whether the API is honestly the better call. Half the value is telling you when not to self-host, so you don't take on MLOps for a problem an API solves cheaper.
- Step 2 · Model selection
Pick from the Hub and benchmark on your data
We shortlist open models from the Hugging Face Hub that fit your task, your hardware and your budget, then benchmark the finalists on your data instead of trusting a public leaderboard. The model that wins your benchmark is the one we move forward with. You see the numbers, so the choice is yours to sign off on, not a black box we hand you.
- Step 3 · Fine-tune
Adapt the model to your domain and prove it
We prep your dataset, fine-tune the open model so it speaks your domain, formats and edge cases, and evaluate it against a generic baseline so you can see it actually got better. Your data stays yours through training. The output is a model you own that does your task, not a generic model behind a prompt, and the evaluation tells you exactly where it's strong and where it still needs help.
- Step 4 · Deploy & integrate
Ship it via Endpoints or self-hosted, run reliably
We deploy the model where it fits: managed Inference Endpoints when you want simple, self-hosted on your cloud when you want control and data ownership. Then we wire the MLOps that keeps it up under real traffic: autoscaling, monitoring for drift and latency, versioning, RAG on your data, and Spaces for the apps your team uses. Everything ships with its monitoring and cost tracking from day one.
- Step 5 · Hand over & operate
Document the ops, then get out of the way
We document how the model is fine-tuned, deployed and monitored so your team can run it without us. The setup lives in your repo and your cloud, owned by you. If you want to go deeper, our AI training covers fine-tuning and deployment end to end. If you want us on call for the next model or the scale-up, we talk about that separately, never as a lock-in.
We're judged on the model that ships.
No partner badge to display, so we lead with what matters: feedback from the teams whose open model we put into production, and whether it kept running reliably and cheaper than the API it replaced after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The model lives in your cloud and repo, owned by your team
- Fine-tuned on your data, with your data staying yours
- Deployed with monitoring, scaling and cost tracking from day one
- Trustpilot reviews come from the teams we shipped it for
The questions we get asked on repeat.
What does a Hugging Face agency actually do?
A Hugging Face agency puts open-source models into production for you instead of leaving you with a notebook that never ships. We pick the right open model from the Hub for your task and budget, fine-tune it on your data so it beats a generic one, deploy it via Inference Endpoints or self-hosted, and wire the MLOps (monitoring, autoscaling, cost tracking) that keeps it reliable. The point is an open model running in production that you own, not a demo that impresses once and breaks under real traffic.How much does a Hugging Face project cost?
It depends on scope: picking and deploying an existing open model is nothing like fine-tuning on your data and self-hosting with full MLOps. We don't throw out a flat package. We start with a free 60-minute audit to find where an open model actually beats your current API, then quote a fixed scope. The compute, whether managed Inference Endpoints or your own cloud, you pay directly; we set it up so the bill stays predictable and, where it makes sense, lower than the API it replaces.Should we use an open model or just a frontier API?
It depends, and we'll tell you honestly. Open models from Hugging Face give you control, cost savings at volume, and data ownership, which matters a lot for sensitive data or high-throughput tasks. But they need real MLOps to run reliably, and for some tasks a frontier API is genuinely simpler and cheaper at low volume. We audit your task before recommending anything, and if the API is the better call, we'll say so rather than sell you a self-hosting project you don't need.Can you fine-tune an open model on our data?
Yes, that's often where the value is. A generic open model is a starting point; fine-tuned on your data it learns your domain, your formats and your edge cases, and beats a generic model on your actual task. We prep the dataset, run the training, evaluate against a real benchmark so you can see the gain, and keep your data yours throughout. You end up owning a model that does your job, not a prompt wrapped around someone else's API.How do you deploy an open model in production?
Two main paths. Managed Inference Endpoints when you want it simple: Hugging Face hosts the model, you call an API, and we wire the autoscaling, monitoring and versioning. Self-hosted on your own cloud when you want full control and data ownership: we set up the serving, scaling and monitoring on your infrastructure. Either way we bring the MLOps that keeps it reliable under real traffic, plus Spaces for demos and apps, and cost tracking so you know what it actually costs.What are Hugging Face Spaces and do we need them?
Spaces let you host a demo or app for a model in the browser, so stakeholders can try it without you standing up infrastructure first. Whether you need them depends on the work. For a proof of concept or an internal tool a few people use, a Space is the fastest way to ship something usable. For a high-traffic production endpoint, you'll want a proper deployment instead. We set up what fits, not what looks impressive in a demo.Is self-hosting an open model cheaper than an API?
Often at volume, not always at low volume, and we won't pretend otherwise. Self-hosting trades a per-call API bill for fixed compute plus the MLOps to run it, so it pays off when your throughput is high or your data can't leave your cloud. At low volume, a frontier API is usually cheaper once you count the engineering. We model both before recommending, and if the API wins on total cost for your usage, we'll tell you instead of selling a self-hosting project.Do you hand it over or keep us dependent?
We hand it over, and the documentation is part of the job. We document how the model is fine-tuned, deployed and monitored so your team can operate it without us, and the setup lives in your repo and your cloud. If you want to go deeper, we run AI training that covers fine-tuning and deployment end to end. If you want us on call for the next model or a scale-up, we talk about that separately, never as a lock-in baked into the build.
Stop leaving models in notebooks. Ship them right.
A 60-minute audit, your use case mapped, a plan to pick, fine-tune and deploy an open model with the MLOps baked in. If your team can run it in-house after setup, we'll hand you the playbook. If we're the right fit, we handle it.