What Meta Llama is.
Llama is Meta's family of open-weight large language models. 'Open-weight' is the key word: unlike GPT, Claude or Gemini , which run only on their makers' servers , Llama's models can be downloaded and run on hardware you control.
That changes the privacy equation entirely. With Llama, the model comes to your data instead of your data going to a third party. For a business handling information that must not leave its walls , client records, health data, anything under strict confidentiality , this is often the deciding factor.
Aerosoft deploys Llama when data residency and privacy matter most, or when a business wants to own its AI stack outright with no per-request fees to an outside provider.
What makes Llama different.
Open weights
The model itself can be downloaded and run on your own servers or a private cloud , no sending data to an outside API.
Runs in your environment
We deploy Llama inside your boundary, so sensitive data never leaves it , a clean answer to data-residency and confidentiality requirements.
No per-request fees
You pay for the hardware that runs it, not for every request. For steady, high-volume workloads this can be far cheaper at scale.
Tunable
Because you hold the weights, Llama can be fine-tuned on your own material to fit your domain and language more closely.
Capable and current
Modern Llama models are strong general-purpose performers and support structured output and tool use, so they fit into real systems , not just experiments.
When the data simply cannot leave.
We choose Llama when privacy, control and cost-at-scale outweigh the convenience of a hosted API.
- 01Your data stays put. The model runs where your data lives , nothing is sent to an outside provider, which resolves most confidentiality and residency concerns.
- 02You own the stack. No dependency on an external service's pricing, availability or policy changes , the model is yours to run.
- 03It is cost-stable. You pay for hardware, not per request , predictable economics for steady, high-volume work.
- 04It is not lock-in. We build behind our own layer, so a private Llama can power the sensitive jobs while a hosted model handles the rest , whatever fits each task.
What we build with Llama.
We deploy Llama for workloads where data must stay in-house , internal document search and Q&A, processing confidential records, drafting against private knowledge , running entirely inside your environment.
As with every build, it works from your own data, returns sources where relevant, and operates with the guardrails and human approval that sensitive work demands , just without anything leaving your walls.
What Cayman businesses ask about Llama.
Why choose Llama over GPT, Claude or Gemini?
One reason above all: privacy. Llama runs on hardware you control, so sensitive data never leaves your environment. It is also cost-stable at high volume. For workloads without those constraints a hosted model is often simpler , we pick per job.
Where does the model run?
Inside your boundary , your own servers or a private cloud instance we manage for you. Your data is processed there and nowhere else.
Is it as capable as the hosted models?
Modern Llama models are strong and close the gap for most business tasks. For the very hardest reasoning a frontier hosted model may still edge ahead , we advise honestly on the trade-off per use case.
Does running it privately cost more?
There is a hardware cost instead of per-request fees. For steady, high-volume workloads private hosting is often cheaper overall; for light or occasional use a hosted API can be more economical. We model both for your case.
Can we fine-tune it on our own data?
Yes. Because you hold the weights, Llama can be tuned on your material to fit your domain, terminology and language more closely.
Is it really private if it's based on Meta's model?
Yes. You download the open weights once; after that the running model has no connection back to Meta. Your data and prompts stay entirely within your environment.
Can it integrate with our systems?
Yes. Llama supports structured output and tool use, so we connect it to your internal systems exactly as we would a hosted model , just inside your walls.
How do we start?
We identify the workload where data cannot leave, size the hardware, deploy a private instance, and build the first use case on it. Tell us what data must stay in-house.
Keep sensitive data
inside your walls.
Tell us what cannot leave your environment. We'll recommend whether a private Llama deployment is the right answer , and explain why.
Request a quote