framework · Meta
AI Infrastructure & MLOps with PyTorch
AI Infrastructure & MLOps built on PyTorch, chosen where it genuinely fits, and swapped where it does not.
- Category
- framework
- Vendor
- Meta
- Alternatives we also use
- 7
Why PyTorch for this
Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.
PyTorch is strongest at flexibility and the widest availability of pretrained models. For ai infrastructure & mlops that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: production serving needs deliberate optimisation work beyond the training code. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- The deep learning framework behind most current research and production model work.
- Strongest at
- flexibility and the widest availability of pretrained models
- Trade-off
- production serving needs deliberate optimisation work beyond the training code
- Category
- framework
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.
Building with PyTorch?
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