We deploy, fine-tune and run Meta's Llama models inside your network or private cloud — so sensitive data stays in-house, costs stay predictable, and the model learns how your business works.
Illustrative demo.
Hosted APIs are quick to start. Self-hosted Llama wins when privacy, volume or customisation matter. Most mature AI products end up using both.
OpenAI, Claude, Gemini and others
On your servers or private cloud
Pick a model and precision, then set how many people use it at the same time. The estimate covers model weights, the memory each conversation needs (KV cache) and working overhead.
Model
Precision
Each active conversation (about 8,000 tokens of context) needs its own memory.A first estimate, not a guarantee. Mixture-of-experts models (Scout, Maverick) load all experts, so memory follows total size. We confirm with load tests on your traffic.
Hosted APIs charge per token, so the bill grows with every request. Your own servers cost roughly the same each month whatever the volume. Move the slider to see where the lines cross.
Illustrative costs. The real break-even depends on the API you compare with, your hardware and how busy it is — we model it with your numbers.
Fine-tuning turns a general model into a specialist that writes in your style, uses your terms and follows your rules. With LoRA adapters it is far cheaper than people expect.
Hundreds to a few thousand good input → output pairs from your tickets, documents or experts.
Remove personal data, fix labels, and hold back a test set the model never sees.
A small add-on layer is trained on top of Llama, on a single GPU for smaller models.
Compare against the base model and a hosted API on your own test set.
Load the adapter at serving time, or merge it, and roll out behind a feature flag.
Quantisation stores the model's numbers with fewer bits, so it needs less memory and runs faster. Each square below is a slice of memory.
Best quality, most memory. For training and accuracy-critical work.
Half the memory with almost no visible quality loss for most tasks.
A quarter of the memory. Runs bigger models on smaller GPUs; test quality on your data.
Downloading a model is the easy part. This is the stack we build so it stays fast, safe and observable under real traffic.
Open weights mean you choose the location — and can move later without rewriting your app.
Llama is free for most commercial use, with a few conditions worth knowing before you launch. This is a summary, not legal advice.
Where open weights give a real advantage: privacy, cost at scale, offline use or deep customisation.
Open-weight and hosted models each have a place. We help you pick, and often combine them behind one API.
| Model family | Strengths | When we choose it |
|---|---|---|
| Meta Llama | Open weights; run anywhere; fine-tune freely; widest tool support | Data must stay in-house, very high volume, deep customisation |
| Qwen | Open weights, strong multilingual and coding | Multilingual products, Asian languages |
| DeepSeek | Open reasoning models at low cost | Self-hosted reasoning and maths |
| OpenAI | Hosted APIs with voice, agents and images | Fastest launch, multimodal features |
| Anthropic Claude | Hosted, strong on long documents and coding | Contracts, research, coding agents |
| Google Gemini | Hosted multimodal with Google grounding | Video, voice and Google Cloud apps |
Straight answers to what clients ask us most.
Three reasons: your data never leaves infrastructure you control, costs become a fixed server bill that can be far cheaper at high volume, and you can fine-tune the model as deeply as you like. Hosted APIs are still faster to launch and often stronger on the hardest tasks, so we help you decide with real numbers.
Small models (1B to 8B) suit classification, extraction and on-device use. 70B-class models handle most business chat and reasoning. The Llama 4 mixture-of-experts models (Scout and Maverick) give stronger quality and long context, but need more GPU memory because all experts are loaded.
It depends on model size, precision and how many people use it at once. A 4-bit 8B model runs on a laptop or a single consumer GPU; a 70B model typically needs one or two 80 GB data-centre GPUs; the largest models need a multi-GPU server. Use the calculator above for a first estimate, then we load-test.
For most businesses, yes, under Meta's Llama community licence. Very large platforms with over 700 million monthly users need a separate licence, you must follow the acceptable use policy and show “Built with Llama”. It is not an OSI open-source licence, so we recommend legal review.
Yes. We usually train LoRA or QLoRA adapters on a curated set of your examples, evaluate against the base model and a hosted API, and only ship if it clearly wins. Your data and the resulting weights stay with you.
With an inference server such as vLLM or TGI behind an OpenAI-compatible gateway, running on Kubernetes with autoscaling, monitoring, logging and safety filters. For small or offline use we use llama.cpp or Ollama.
Yes. Many clients route sensitive or high-volume traffic to self-hosted Llama and send the rare hardest questions to a hosted model such as Claude, GPT or Gemini, all behind one API.
Tell us about your data, your users and where the model must run. We reply within 4 business hours with honest advice.