Cloud · AI Infrastructure

Cloud Infrastructure
That Runs AI Workloads

Indian enterprise cloud adoption reached 78% in 2025, yet 61% of CIOs report their infrastructure isn't ready for AI workloads (Nasscom Cloud Report 2025). The issue isn't cloud itself — it's that most cloud setups were designed for traditional apps, not GPU compute, high-throughput data pipelines, and LLM inference.

AWS · Azure · GCP certified

99.9% uptime SLA

Cloud-native + hybrid

24/7 ops team

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What We Build

Cloud & AI Infrastructure Services

From initial migration to ongoing operations — every engagement is designed to make cloud infrastructure a reliable foundation, not a recurring problem.

Cloud Migration

Lift-and-shift or re-architect for cloud-native. Win Infosoft runs migrations with zero-downtime cutover plans and full rollback capability — so there's no moment where the business is at risk.

AI-Ready Infrastructure

GPU instances, vector databases, and high-bandwidth storage configured specifically for ML training, inference, and RAG workloads. Built for the models your team is actually running, not a generic blueprint.

Infrastructure as Code

Terraform, Bicep, and CloudFormation templates. Every environment is version-controlled, auditable, and reproducible — meaning any engineer on the team can rebuild a full environment from scratch in minutes.

Cost Optimisation

Average savings of 25–40% on cloud bills through right-sizing, reserved instances, spot usage strategies, and wastage audits. Most clients see measurable reduction within the first billing cycle after changes are applied.

Multi-Cloud Management

Single pane of glass monitoring across AWS, Azure, and GCP. No vendor lock-in by design — workloads can be moved or distributed across providers based on cost, compliance, or performance requirements.

Disaster Recovery & Business Continuity

RTO under 4 hours, RPO under 1 hour. Tested quarterly, documented, and ready for audit. Recovery procedures are verified against real failure scenarios — not just assumed to work because backups exist.

The Process

How Cloud Engagements Work

Step 01

Assessment

Current infrastructure audit, cost analysis, and AI readiness evaluation. No changes made at this stage.

Step 02

Architecture Design

Target architecture documented in full — cloud topology, IaC templates, security controls, and cost projections.

Step 03

Migration / Build

Phased execution with rollback checkpoints. Zero-downtime cutover for production workloads.

Step 04

Handover & Ongoing Ops

Full knowledge transfer, runbook documentation, and optional 24/7 managed operations.

Common Questions

Cloud Infrastructure FAQ

How long does cloud migration take?

Simple lift-and-shift migrations for 20–50 servers take 4–8 weeks. Re-architecting for cloud-native patterns — containers, serverless, managed databases — takes 3–6 months depending on application complexity. The audit phase determines which path is appropriate before any commitment is made.

What does AI-ready infrastructure actually mean?

It means the cloud setup is configured for GPU compute (needed for model training and inference), high-throughput data pipelines (needed to feed models at scale), and vector databases (needed for semantic search and RAG). Standard cloud setups are not pre-configured for these workloads — they require specific instance families, storage throughput configurations, and networking rules that most general-purpose environments lack.

Can you reduce our existing cloud costs without disrupting operations?

In most cases, yes. Win Infosoft starts with a no-commitment cloud cost audit — analysing current spend, identifying wastage, and providing a prioritised list of savings opportunities before any changes are made. Clients typically see 25–40% cost reduction after recommendations are applied, with most quick-win changes implementable without any service interruption.

Do you support hybrid cloud (on-premise + cloud)?

Win Infosoft designs and manages hybrid environments, typically using Azure Arc, AWS Outposts, or VPN/Direct Connect bridges. Many Indian enterprises maintain on-premise infrastructure for regulatory or latency reasons, and the team has direct experience designing hybrid architectures that balance compliance requirements against cloud flexibility.

Cloud Infrastructure That Runs AI Workloads