Meta Llama.
Llama is Meta's family of open-weight language models, downloadable and self-hostable. Weights can be fine-tuned and the models run on your own infrastructure, which changes the data, cost and control position entirely.
Where Meta Llama fits on a long engagement.
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 an assigned 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 devops as a service rather than treated as a model decision.
What we use Meta Llama for.
- 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.
How Meta Llama capacity is assigned.
Self-hosted model work is assigned under managed ai services, with the cost crossover against an API established before infrastructure is committed.
Tell us what your roadmap needs Meta Llama for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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