Meta Llama specialists for your dedicated team.

Where Meta Llama fits in a long-term product team.

Self-hosting answers questions an API cannot. Data never leaves your infrastructure, which resolves most regulatory objections directly; the model version is fixed until you change it, so behaviour does not shift underneath you; and the weights can be fine-tuned on proprietary data.

The cost comparison is more nuanced than it appears. GPU instances are expensive and billed whether or not they are serving, so self-hosting is cheaper than an API only above a volume threshold that many workloads never reach. Below it, an API is both cheaper and considerably less work.

What your dedicated team does with Meta Llama.

Serving models is real infrastructure with real operational load: GPU capacity, batching, autoscaling that responds slowly because instances take minutes to start, and monitoring for a workload whose failure modes differ from a web service.

That is platform work as much as AI work, which is why it is scoped alongside a dedicated cloud and DevOps team.

Common Meta Llama use cases.

• Data that cannot leave the estate Inference inside your own infrastructure, which resolves most residency objections.
• Behaviour that does not change underneath you A fixed model version, so a provider update cannot alter production output.
• Fine-tuning on proprietary data Adaptation to a domain where a general model performs poorly.

Adding Meta Llama skills to your team.

Self-hosted model work sits with your dedicated AI team, with the cost crossover against an API established before infrastructure is committed.

Every Meta Llama specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.