The Llama AI agency.Own your AI, on your infra.
Llama is Meta's family of open-weight models you can fine-tune and run anywhere, so you own your AI instead of renting it. We pick the right variant, fine-tune it on your data, self-host it in your VPC, and wire the RAG and MLOps that keep it reliable.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
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GeminiA Llama agency gets you ownership, not just a download.
Anyone can pull the weights off the hub. Picking the right variant, fine-tuning it on your data, serving it in production and keeping the cost honest is a different job. Here are the four things we own.
- Model selection
The right Llama variant for your task, not a bigger bill
Llama ships in a range of sizes and as text and multimodal variants, and the biggest one isn't always the answer. We pick the variant and parameter count that fits your task and your hardware, so you don't pay frontier-API prices for work a smaller open model handles fine. We benchmark candidates on your actual prompts before anything goes to production, instead of guessing from a leaderboard.
See how we pick - Fine-tuning
Llama adapted to your domain, data and tone
A generic model gives generic answers. We fine-tune Llama on your data so a smaller open model beats a bigger generic one on your specific task: your terminology, your formats, your edge cases. Done right, that's where open weights earn their keep, you get a model that knows your domain and runs on hardware you control, not a black box behind someone else's API.
See the method - Self-hosting & residency
On your infra, so sensitive data never leaves
Open weights mean you can run Llama on-prem or in your own VPC, so sensitive data never leaves your environment, which matters for residency and compliance. We set up the serving stack right (vLLM or Ollama, batching, quantization, GPU sizing) so it's fast and stable, not a notebook that falls over under load. You own the model, the weights and the infra it runs on.
See the integrations - RAG, agents & ops
Grounded on your data, monitored, with cost under control
A model alone isn't a product. We ground Llama on your data with RAG so it answers from your sources, build the agents that use it, and wire monitoring, scaling and cost control so it stays reliable in production. We're an automation and AI agency first, so this plugs into how your business already runs, and we'll route to a frontier API where that's honestly the better call.
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 that 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 a stack that holds under load, with monitoring and cost control wired before anyone calls it in anger.
- Audit · map your use cases, your data residency needs and where an API bill is hurting
- Select & fine-tune · the right Llama variant, adapted to 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, owned by you
We run open weights in production.
We don't sell a partner tier. We 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 run open weights in production with real MLOps, so we set Llama up the way it actually serves, not the way a demo notebook suggests.
- Ownership and residency by default: the model, the weights and the infra are yours, so sensitive data stays in your environment.
- We're honest about when a frontier API beats self-hosting, for the hardest tasks or low volume we'll tell you to route there instead of overselling open weights.
- No partner badge to sell. We're judged on whether you own a reliable model that's cheaper at your scale after we leave, not on a 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 your hardware budget, so you run the smallest model that clears your quality bar instead of overpaying for headroom you don't use.
- Setup
Fine-tuning on your data
We fine-tune Llama on your domain data (LoRA or full, depending on the case) so it learns your terminology, formats and edge cases, and a smaller open model beats a generic one on your task.
- Setup
Self-hosting (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 never leaves and the endpoint holds under load.
- Setup
RAG & retrieval
We ground Llama on your data with retrieval so it answers from your sources, not from its training set: chunking, embeddings, a vector store, and the evals to keep retrieval honest as your data 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 handles the bulk and the hardest or rarest 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 you didn't sign 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 hurting. We check your volume, your hardware and your compliance needs. Half the value is telling you which use cases justify self-hosting Llama and which are honestly better left on an API, so you don't build MLOps you don't need.
- Step 2 · Select & fine-tune
Pick the right Llama and adapt it to 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, formats and edge cases. We measure against a baseline so the gain is real, not a vibe. The output is a model sized for your hardware that beats a generic one on your task, with 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, running 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 you own it. If you want to go deeper, our AI training covers fine-tuning and serving end to end. If you want us on call for what scales next, or for the parts you'd rather route to a frontier API, we talk about that 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 it. 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 on your data with RAG, build the agents that use it, and wire the MLOps it needs: monitoring, scaling and cost control. The point is a reliable model you own, cheaper at your scale, not a science project that dies after the demo.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.Is self-hosting Llama actually cheaper than a frontier API?
It depends on volume, and we'll tell you the truth. At low volume, a frontier API is often simpler and cheaper because you pay per call and skip the infra. As usage scales, the per-token cost of an API keeps climbing while a self-hosted model amortizes the GPU you already pay for, so open weights win on cost and on data residency. We map your volume and use cases in the audit and only recommend self-hosting where it actually beats the API.Why fine-tune Llama instead of just prompting a bigger model?
Because a smaller fine-tuned model can beat a bigger generic one on your specific task, while running on hardware you control. Fine-tuning teaches Llama your terminology, your formats and your edge cases, so you get reliable output without paying for a frontier model's 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.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 widely supported across these stacks and cloud providers, so you're not locked into one vendor for the infra.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.