Capability
AI Infrastructure & MLOps across India
GPU infrastructure, model serving and MLOps pipelines, sized for your workload, not for a benchmark.
- Industries
- 12
- Stack options
- 8
- Typical first release
- 6 weeks
What ai infrastructure & mlops means when we build it
On-premise inference makes sense more often than the cloud narrative suggests, at steady high volume, or where data simply cannot leave. We model both honestly.
We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.
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
Who this is for
We usually work with infrastructure leads, ML platform teams, CTOs and security architects, the people who own the outcome rather than the tooling decision.
AI Infrastructure & MLOps by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- AI Infrastructure & MLOps for SaaS & TechnologySOC 2
- AI Infrastructure & MLOps for Financial ServicesRBI guidelines
- AI Infrastructure & MLOps for BankingRBI master directions
- AI Infrastructure & MLOps for Healthcare & HospitalsDPDP Act 2023
- AI Infrastructure & MLOps for Pharmaceuticals & Life SciencesCDSCO
- AI Infrastructure & MLOps for Defence & Aerospacesecurity clearance requirements
- AI Infrastructure & MLOps for Government & Public SectorDPDP Act 2023
- AI Infrastructure & MLOps for TelecommunicationsTRAI regulations
- AI Infrastructure & MLOps for ManufacturingISO 9001
- AI Infrastructure & MLOps for Energy & UtilitiesCEA regulations
- AI Infrastructure & MLOps for Media & Entertainmentcopyright law
- AI Infrastructure & MLOps for Education & EdTechDPDP Act 2023
AI Infrastructure & MLOps, stack options
We pick per workload. Each page states the honest trade-off.
- AI Infrastructure & MLOps with Kubernetesinfra
- AI Infrastructure & MLOps with Pythonframework
- AI Infrastructure & MLOps with PyTorchframework
- AI Infrastructure & MLOps with AWS Bedrockplatform
- AI Infrastructure & MLOps with Azure OpenAIplatform
- AI Infrastructure & MLOps with NVIDIA Jetsoninfra
- AI Infrastructure & MLOps with Llamamodel
- AI Infrastructure & MLOps with PostgreSQLdata
Questions we get asked
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.
Considering ai infrastructure & mlops?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878