infra · open source
AI Infrastructure & MLOps with Kubernetes
AI Infrastructure & MLOps built on Kubernetes, chosen where it genuinely fits, and swapped where it does not.
- Category
- infra
- Vendor
- Open source
- Alternatives we also use
- 7
Why Kubernetes for this
Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.
Kubernetes is strongest at portability and fine-grained control over scaling and scheduling. For ai infrastructure & mlops that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: an operational burden most teams below a certain size should not take on. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- Container orchestration for workloads that genuinely need it.
- Strongest at
- portability and fine-grained control over scaling and scheduling
- Trade-off
- an operational burden most teams below a certain size should not take on
- Category
- infra
We are not a reseller for Kubernetes 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 Kubernetes?
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