The Hugging Face agency.Open models, in production.
Hugging Face is the largest library of open AI models: models you can download and run on your own servers instead of renting a paid API by the call. There are well over a million of them, plus Inference Endpoints to host one, Spaces to demo it, and a free tier to try it. But a model sitting on a laptop isn't a product. We pick, deploy and run the right open model for you, fine-tune it on your own data, and keep it reliable and cheaper than a paid API once real traffic hits it.
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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 holds well over a million open models, and the right one for your job is rarely the biggest or the one a vendor is pushing this week. We read your task, your speed needs and the hardware you can run, then pick from the Hub instead of defaulting to a pay-per-call API like ChatGPT or Claude. Often a small open model tuned on your data beats a giant generic one. Sometimes the paid API really is the smarter call, and we say so.
See how we choose - Fine-tuning
An open model adapted to your domain and your data
A generic open model is where you start, not where you finish. We fine-tune it on your data (we retrain it on your examples) so it learns your domain, your formats and your odd cases, and beats a generic model on the task you actually run. We prep the dataset, run the training, test it against a real benchmark, and your data stays yours the whole way. What you end up with is a model you own that does your job, not a prompt bolted onto someone else's API.
See the method - Deployment
Shipped via Inference Endpoints or self-hosted, run reliably
An open model on your laptop isn't a product. We put it where it belongs: managed Inference Endpoints (Hugging Face hosts and scales it for you) when you want it simple, or self-hosted on your own cloud when you want full control and your data never leaving your walls. Either way we handle the running of it, autoscaling, monitoring, versioning and fallbacks, so it stays up when traffic spikes 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 only the start. We connect it to your own documents so it answers from your data instead of guessing (the technique is called retrieval), build the Spaces and demos your team actually opens, and watch cost, speed and quality so you catch a problem before your users do. We're an automation and AI agency first, so the model plugs into the tools you already use, it isn't a side experiment nobody maintains.
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 · we map your task, your data, your speed needs and where an open model beats a paid API
- Select · we shortlist from the Hub and test the finalists on your data, not on a public ranking
- Fine-tune · we adapt the model to your domain and prove it beats a generic one before it ships
- Deploy · Inference Endpoints or self-hosted, run with the monitoring 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.
- Open Hugging Face model wins when you send a lot of calls: a paid API (ChatGPT, Claude) bills every single one, while a model you host has a fixed cost that gets cheaper the more you use it.
- Open model wins when your data is sensitive: it runs on your own servers, so nothing has to leave for a third party's API to read it. That alone decides it for health, legal and finance work.
- Paid API wins when volume is low and the task is general: at a few thousand calls a month, ChatGPT or Claude is simpler and cheaper than standing up and running your own model, and we'll tell you so.
- Open model wins when you need it to speak your domain: fine-tuned on your data, a small open model beats a bigger general one on your task, and you own the result instead of renting it.
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 speed you need, the volume you run, and what you're paying a service like ChatGPT or Claude today. We check whether an open model from the Hub wins on cost, control or keeping your data in-house, or whether the paid API is honestly the better call. You leave with a plan and a rough cost: projects start from $2,000, with a precise quote after the audit, and run anywhere from a week to six months depending on scope.
- 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 is Hugging Face, and what does a Hugging Face agency do?
Hugging Face is the largest library of open AI models: models you can download and run on your own servers instead of renting a paid API by the call. It also gives you Inference Endpoints to host a model, Spaces to demo one in a browser, and a free tier to try things out. A Hugging Face agency turns all that into something that actually runs. We pick the right open model for your task, fine-tune it on your data so it beats a generic one, deploy it on Inference Endpoints or your own cloud, and keep it monitored and reliable. The point is a model in production that you own, not a demo that works once and breaks the first busy afternoon. Projects start from $2,000, with a precise quote after a free 60-minute audit.How much do Hugging Face Inference Endpoints cost in 2026?
It comes down to the CPU or GPU you choose and how many hours the endpoint runs, and Hugging Face keeps the current per-hour Inference Endpoints rates on its official pricing page, so check there for the live figure. We won't quote a number here that moves with hardware and time, because that's how you end up citing a stale price. What we do is size the endpoint to your real traffic, switch on autoscaling and scale-to-zero (it powers down when idle so you stop paying), and track the bill so it stays predictable. In the free 60-minute audit we put Inference Endpoints side by side with what you pay a service like ChatGPT or Claude today, so you see the real number before committing.Does Hugging Face have a free tier for the Inference API?
Yes. Hugging Face offers a free tier on the serverless Inference API, and the exact limits (rate caps, monthly credits, which models are covered) live on its official pricing page and shift over time, so read the current numbers there. The free tier is great for prototyping and light, low-volume calls. It isn't built for production traffic. When your usage outgrows it, the honest move is a dedicated Inference Endpoint or self-hosting, not hammering the free tier until it rate-limits your own users. We draw that line with you in the audit and set up the tier that fits your real volume.Is Hugging Face Spaces free, and can it run in production?
Spaces has a free CPU tier that's ideal for demos and internal tools, plus paid GPU hardware for heavier apps, and Hugging Face lists the current Spaces tiers on its official pricing page. Free Spaces go to sleep when idle and share limited CPU, so they suit a proof of concept a handful of people poke at, not a high-traffic production app. For real load you want upgraded Space hardware or a proper Inference Endpoints deployment. We build the Space when a browser demo is the fastest way to put something usable in front of your team, and we flag the moment the workload has outgrown it.Can Hugging Face generate images on the free tier?
Yes. You can run open image models such as Stable Diffusion variants through the Inference API and Spaces, and the free tier lets you test image generation at low volume. The catch is speed and quota: free serverless calls queue up and rate-limit, so it's fine for trying a model out, not for serving your users at scale. For production image generation we deploy the model on an Inference Endpoint or self-host it on a GPU, with autoscaling so it stays fast under load. In the audit we check whether an open image model on your own endpoint beats paying a hosted image API for every picture.How many models are on the Hugging Face Hub in 2026?
Well over a million public models in 2026, across text, vision, audio and multimodal, plus hundreds of thousands of datasets. That scale is exactly the problem we solve: the right model for your task is almost never the biggest or the most downloaded, it's the one that wins on your data and your speed needs. We shortlist candidates from the Hub against your task, test the finalists on your data, and pick the one that actually performs, so you're not choosing off a public ranking or a download count.How do you keep up with the new open models coming out in 2026?
New open models land on the Hub every week, and part of the job is spotting which releases actually matter for your task instead of chasing every announcement. We watch the Hub, the release notes and the benchmarks, then re-run your own test when a new model looks like a genuine upgrade. Swapping in a newer, smaller or cheaper model is straightforward once the deployment is done properly, which is a big reason to ship it right the first time. If a mid-2026 release beats what you're running, you get a real comparison on your data, not the hype.Should we use an open model or a paid API like ChatGPT or Claude?
It depends, and we'll tell you straight. An open model from Hugging Face wins when you send a lot of calls (a fixed hosting cost beats paying per call), when your data is sensitive and has to stay on your own servers, or when you need it tuned to your specific domain. A paid API like ChatGPT or Claude wins when your volume is low and the task is general, where standing up and running your own model just isn't worth it. We audit your task before recommending anything, and if the paid API is the better call, we say so instead of selling you a self-hosting project you don't need.Can you fine-tune an open model on our own data?
Yes, and that's often where the real value is. A generic open model is a starting point; fine-tuned on your data (retrained on your own examples) it learns your domain, your formats and your odd cases, and beats a generic one on the task you actually run. We prep the dataset, run the training, test against a real benchmark so you can see the gain, and your data stays yours throughout. You end up owning a model that does your job, not a prompt wrapped around someone else's API.Do you hand it over, or keep us dependent on you?
We hand it over, and writing it up is part of the job. We document how the model is fine-tuned, deployed and monitored so your team can run it without us, and the whole setup lives in your repo and your cloud. If you want to go further, we run AI training that covers fine-tuning and deployment end to end. If you'd rather keep us on call for the next model or a scale-up, we sort that out 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.