model · Meta
AI Infrastructure & MLOps with Llama
AI Infrastructure & MLOps built on Llama, chosen where it genuinely fits, and swapped where it does not.
- Category
- model
- Vendor
- Meta
- Alternatives we also use
- 7
Why Llama for this
Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.
Llama is strongest at full control, no per-token cost, and viable air-gapped deployment. For ai infrastructure & mlops that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- Open-weight models you can host yourself, the default when data cannot leave your building.
- Strongest at
- full control, no per-token cost, and viable air-gapped deployment
- Trade-off
- you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you
- Category
- model
We are not a reseller for Meta and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Workload sizing based on measured throughput, not guesses
- Model registry and versioned deployments
- Autoscaling and cost-per-inference monitoring
- Canary and rollback deployment paths
- On-premise or air-gapped options where required
- Runbooks and on-call documentation
Questions
Cloud or on-premise?
We model both against your real volume. On-premise typically wins at sustained high throughput or where data residency is non-negotiable; cloud wins on variable and early-stage workloads.
Can you deploy air-gapped?
Yes, with open-weight models and a fully offline inference stack, the usual pattern for defence, and for some healthcare and government work.
Do you support our existing Kubernetes setup?
Yes, and we would rather extend it than introduce a parallel platform your team has to learn.
Alternatives for ai infrastructure & mlops
Same capability, different stack. Each page states its own trade-off.
What else we build on Llama
Building with Llama?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878