North India
AI Infrastructure & MLOps across Delhi
GPU infrastructure, model serving and MLOps pipelines, sized for your workload, not for a benchmark. Covering every district and PIN code in Delhi.
- Districts
- 8
- PIN codes
- 98
- Cities mapped
- 2
AI Infrastructure & MLOps in Delhi
Cost per inference is the operating metric. We instrument it from day one so capacity decisions are made on evidence.
Delhi runs on government and public administration, financial services, professional services, retail and e-commerce and media, policy, professional services and head-office functions, all of it document-heavy knowledge work, which is where copilots land first. Where ai infrastructure & mlops earns its budget here usually follows directly from that mix.
Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners. We hand over with runbooks, tests and a team that knows how it works, not a dependency.
नमस्ते , Namaste. We work in Hindi and English across Delhi.
Delhi coverage
- State / UT
- Delhi
- Region
- North India
- Districts covered
- 8
- PIN codes covered
- 98
- Cities mapped
- 2
- Working languages
- Hindi, English
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
Districts of Delhi
Every district has a coverage page listing its PIN codes.
Other capabilities across Delhi
Questions
Do you cover all of Delhi?
Yes, all 8 districts and 98 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.
Which Delhi sectors do you work with most?
Across Delhi the economy leans towards government and public administration, financial services, professional services, retail and e-commerce, media. Policy, professional services and head-office functions, all of it document-heavy knowledge work, which is where copilots land first.
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.
AI Infrastructure & MLOps in Delhi
Covering all 8 districts. Tell us what you are trying to change.
Or email bd@dtrasglobal.com · call +91 74118 77878