The Llama AI agency.Fine-tuned on-prem, owned by you.
Llama is Meta's family of open-weight models, and the whole ecosystem already runs on it: the runtimes, the tooling, the quantized community builds. A Llama agency puts that to work for you. We pick the right variant, fine-tune it on your data, and self-host it on your own infra, so you own the weights instead of renting an endpoint.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
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GeminiA Llama agency gets you a tuned model you own, not just a download.
Anyone can pull the weights off the hub. Picking the right variant, fine-tuning it on your data, serving it on the stack the ecosystem already expects, and keeping the cost honest is a different job. Here are the four things we own.
- Model selection
The right Llama variant for the job, not the biggest number
Llama isn't one model, it's a family: several sizes, text and multimodal, each with community fine-tunes already floating around the hub. The biggest one is rarely what you need. We match the variant and parameter count to your task and the GPUs you actually have, then benchmark the shortlist on your own prompts before anything ships. You skip frontier-API pricing for work a smaller open model closes just as well.
See how we pick - Fine-tuning
Llama trained on your domain, on hardware you control
A base model answers like a base model. We fine-tune Llama on your data (LoRA or a full run, depending) so it picks up your vocabulary, your formats, the edge cases that trip a generic model. That's the part Meta's open-weight ecosystem makes doable and most teams still skip. Done properly, a smaller tuned Llama beats a bigger generic one on your task, and it runs on a box you own instead of behind someone else's API.
See the method - Self-host & ownership
On your infra, you own the weights outright
Open weights mean the model lives wherever you put it: on-prem, in your VPC, air-gapped if the compliance team asks. Sensitive data never leaves, so residency stops being a negotiation. We stand up the serving stack properly (vLLM or Ollama, batching, quantization, GPU sizing) so it holds under load, not a notebook that dies at the first spike. The weights, the model and the box it runs on are yours to keep.
See the integrations - RAG, agents & ops
Wired into your stack, monitored and cost-aware
A model on its own isn't a product. We ground Llama on your data with RAG so it answers from your sources, build the agents around it, and wire the monitoring, scaling and cost tracking that keep it steady in production. We run as an automation and AI agency first, so it plugs into how your business already works, and we'll route to a frontier API for the calls where that's honestly the better answer.
See AI enablement
We deploy Llama like production infra, not a science project.
Most open-weight efforts stall the same way: a model downloaded, a fine-tune nobody measured, a notebook that falls over the first time real traffic hits it. So we treat it like infrastructure: the right variant, fine-tuned and benchmarked, served on the runtimes the Llama ecosystem is built around, with monitoring and cost control wired before anyone calls it in anger.
- Audit · map your use cases, your residency needs and where a frontier-API bill is hurting
- Select & fine-tune · the right Llama variant, trained on your data, benchmarked on your prompts
- Self-host · vLLM or Ollama in your VPC or on-prem, sized and stable under load
- Operate · RAG, routing, monitoring and cost control, so it stays reliable and owned by you
We fine-tune open weights in production.
We don't sell a partner tier. We fine-tune and run open-weight models in production with real MLOps, so we set Llama up the way it actually serves: a variant sized to the task, a fine-tune we measured, a serving stack that holds, and cost tracking on every endpoint. And we'll tell you when a frontier API beats self-hosting, instead of overselling open weights to win the project.
- We fine-tune and serve open weights in production, not slideware. Llama gets set up the way it actually runs, the way the ecosystem's own tooling expects, not the way a demo notebook suggests.
- Ownership by default: the weights, the model and the infra are yours, so sensitive data stays in your environment and nobody can deprecate your endpoint out from under you.
- We're honest about when a frontier API beats self-hosting. For the hardest reasoning or thin volume we'll tell you to route there instead of overselling open weights to win the project.
- No partner badge to wave around. We're judged on whether you're left owning a tuned, reliable model that's cheaper at your scale, not on a vendor tier.
Llama at the core, your serving stack around it.
We configure the parts that turn open weights into a reliable, owned endpoint, then connect them to how your business already runs. Here's what a real deployment covers.
- Setup
Model & size selection
We benchmark Llama variants and parameter counts on your real prompts and the GPUs you already own, so you run the smallest model that clears your quality bar instead of paying for headroom you never touch.
- Setup
Fine-tuning on your data
We fine-tune Llama on your domain data (LoRA or a full run) so it learns your terminology, your formats and your edge cases. This is where the open-weight ecosystem earns its keep and where a tuned small model beats a generic large one.
- Setup
Self-host (vLLM / Ollama / VPC)
We deploy Llama on your infra with the right serving stack: vLLM or Ollama, quantization, batching and GPU sizing, in your VPC or on-prem so sensitive data stays put and the endpoint holds under load.
- Setup
RAG & retrieval
We ground Llama on your data with retrieval so it answers from your sources, not its training set: chunking, embeddings, a vector store, and the evals that keep retrieval honest as your corpus grows.
- Setup
Model routing (Llama + frontier)
We route requests between your self-hosted Llama and a frontier API by task and cost, so the open model carries the bulk and the rarest or hardest calls go where they're cheaper to get right.
- Setup
MLOps (monitoring, scaling, cost)
We wire the production layer open weights need: monitoring, autoscaling, request logging, eval harnesses and cost tracking, so self-hosting stays an asset and not a 3am pager nobody signed up for.
We map your use cases and cost, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your use cases, your data residency needs and where an API bill is hurting. You leave with an honest read on what self-hosting Llama fixes, which model to start with, and what to keep on an API. Zero pitch, just an engineer's take on your AI stack.
- An honest read on where Llama beats an API for you
- The variant and fine-tune to start with
- The serving stack and residency setup to wire
- A frank take on what to keep on a frontier API
How we run a Llama deployment.
Five steps, in order. We don't fine-tune before we know self-hosting pays off, we don't ship a model without an eval set, and you own it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · AI audit
Map the use cases, the data and the real cost
We sit down with your team and look at what you're actually trying to run on an LLM, what data it touches, and where a frontier-API bill or a residency constraint is biting. We check your volume, your GPUs and your compliance needs. Half the value here is telling you which use cases justify a self-hosted Llama and which are honestly better left on an API, so you don't stand up MLOps you'll never need.
- Step 2 · Select & fine-tune
Pick the right Llama and train it on your data
We benchmark Llama variants and sizes on your real prompts, then fine-tune the one that fits on your domain data so it learns your terminology, your formats and your edge cases. We measure against a baseline so the gain is real, not a vibe. You come out with a model sized for your hardware that beats a generic one on your task, plus the eval set to prove it before it ships.
- Step 3 · Self-host on your infra
Deploy it where your data stays put
We deploy Llama on your infra, on-prem or in your VPC, with the serving stack set up right: vLLM or Ollama, quantization, batching and GPU sizing so the endpoint is fast and holds under load. Sensitive data never leaves your environment, which is the whole point of open weights. You get an OpenAI-compatible endpoint your apps can hit, owned by you, on hardware you control.
- Step 4 · Ground, route & operate
RAG, routing and the production layer
We ground Llama on your data with RAG so it answers from your sources, build the agents that use it, and set up routing between your open model and a frontier API by task and cost. Then we wire the MLOps open weights need: monitoring, autoscaling, logging, eval harnesses and cost tracking. Everything ships with its observability from day one, not bolted on after the first incident.
- Step 5 · Hand over
Leave you owning the model and the stack
We hand you a model, weights and a serving stack your team can run without us, with the runbooks and evals to keep it healthy. The setup lives in your infra and your repo, so it's yours. Want to go deeper? Our AI training covers fine-tuning and serving end to end. Want us on call for what scales next, or for the parts you'd rather route to a frontier API? We sort that out separately.
We're judged on the model that ships.
No partner badge to display, so we lead with what matters: feedback from the teams whose Llama deployment we ran, and whether they kept owning a reliable, cheaper model after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The model, weights and stack live on your infra, owned by you
- Fine-tunes measured against a baseline before they ship
- Self-hosted in your VPC or on-prem, data residency intact
- Trustpilot reviews come from the teams we deployed for
The questions we get asked on repeat.
What does a Llama agency actually do?
A Llama agency deploys Meta's open-weight models so you own your AI instead of renting an endpoint. We pick the right Llama variant and size for your task, fine-tune it on your data, and self-host it on your infra (on-prem or VPC) so sensitive data never leaves. Then we ground it with RAG, build the agents that use it, and wire the MLOps it needs: monitoring, scaling and cost control. The point is a tuned, reliable model you keep, cheaper at your scale, not a science project that dies after the demo.Why choose Llama over other open models like Mistral or DeepSeek?
Because Llama sits at the centre of the widest open-weight ecosystem. Runtimes, quantized community builds, fine-tuning tooling and cloud support all target it first, so there's less bespoke plumbing to get it into production. Mistral is a strong European option and DeepSeek is aggressive on raw cost, and we'll say so plainly. But for most teams the deciding factor is how much already works out of the box with Llama, and how far you can fine-tune it on your own hardware. We benchmark the real candidates on your task before we commit.How much does a Llama deployment cost?
It depends on scope: a single fine-tuned model on a modest GPU is nothing like a multi-model, RAG-backed, autoscaled deployment with routing. We don't throw out a flat package. We start with a free 60-minute audit to find where self-hosting Llama actually pays off versus an API, then quote a fixed scope. Llama itself is free to download under its community licence; what you pay for is the GPU infra and the engineering to run it well, and we size both so the bill is predictable.Why fine-tune Llama instead of just prompting a bigger model?
Because a smaller fine-tuned Llama can beat a bigger generic model on your specific task, while running on hardware you control. Fine-tuning teaches it your terminology, your formats and your edge cases, so you get reliable output without paying frontier-model size on every call. It's not always the answer: for broad, open-ended work a larger general model can still win. We benchmark both on your prompts and recommend whichever clears your quality bar cheaper. The open-weight ecosystem makes the tuning path far less painful than it used to be.Can we keep our data on our own infrastructure?
Yes, that's the main reason to choose Llama. Because the weights are open, we can run it on-prem or in your own VPC, so your data never leaves your environment, which matters for residency and compliance. We set up the serving stack (vLLM or Ollama), the network boundaries and the access controls so the model is private by default. Nothing is sent to a third-party API unless you explicitly route a request there, and even then you decide what leaves.What serving stack do you use to run Llama in production?
It depends on scale. For high-throughput production we use vLLM, which batches requests and serves an OpenAI-compatible endpoint your apps can hit directly. For smaller or local setups, Ollama is simpler to run. We add quantization to fit your GPU budget, size the hardware to your traffic, and load-test before go-live. Llama is one of the best-supported models across these runtimes and cloud providers, so you're not locked into a single infra vendor.Will a self-hosted Llama replace a frontier API entirely?
Not always, and we won't pretend it does. A fine-tuned Llama covers the bulk of most workloads at a lower cost on your own infra, but for the hardest reasoning or very low volume, a frontier API can still be simpler and better. That's why we set up routing: the open model handles the volume, and the rare or hardest calls go where they're cheaper to get right. We optimise for your outcome and cost, not for self-hosting everything as a point of pride.How long does a Llama deployment take?
For a scoped deployment (one fine-tuned model, self-hosted with basic monitoring), count a few weeks: audit and model selection first, then fine-tuning and the serving stack. Adding RAG, routing, agents and full MLOps runs longer. We split into batches so you get a working, owned endpoint fast, rather than waiting on a big platform before anyone can call the model. Each batch ships with its evals and observability so you can trust what's in production.
Stop renting your AI. Own it.
A 60-minute audit, your use cases and cost mapped, a deployment plan with residency 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.