Private Llama deployments for teams that need open-weight models: self-hosted inference, fine-tuning on your data, and full control over where data lives.
When API models are the wrong fit
Some workloads cannot ship data to a third-party API: regulated records, unreleased product data, client contracts. Others simply run at a volume where hosted pricing dwarfs the cost of your own GPUs. Open-weight models solve both, but running them well requires real infrastructure work that most teams have not done before.
What we build with Llama
- Self-hosted Llama inference sized for your latency and volume
- Fine-tunes and adapters trained on your domain data
- Quantization and serving choices that balance quality against GPU cost
- The same RAG, evaluation, and monitoring layer we build around hosted models
How we work
Confirm open weights actually beat an API for your constraint
Benchmark model sizes on your tasks before buying GPU capacity
Deploy with serving infrastructure built for production, not notebooks
Keep an upgrade path as new Llama releases land
Typical stack
Frequently asked questions
Only above a certain sustained volume, and only if operations are efficient. We model your workload cost both ways before recommending either. Data control, not cost, is usually the stronger reason to self-host.
For many scoped, domain-specific tasks a fine-tuned Llama performs comparably. For open-ended frontier reasoning, hosted models still lead. We benchmark on your tasks and tell you honestly which side your workload falls on.