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Cloud & DevOps Consulting

Architect, deploy and scale infrastructure engineered for resilience — CI/CD pipelines, containers, infrastructure-as-code and autoscaling on AWS, GCP or Azure.

01

Overview

CODE TO PRODUCTION

Based in Pune, India, we treat infrastructure as code — reproducible, version-controlled and deployment-gated. We architect cloud environments on AWS, GCP or Azure, selected for workload fit and cost structure, and wire the entire path from git push to production: CI/CD pipelines, containerized builds and infrastructure defined in Terraform.

Autoscaling and scale-to-zero keep cost minimal under idle workloads and elastic under demand. Every environment ships with observability and cost attribution from the first deployment — pairs well with custom software or AI automation running on top.

MULTI-CLOUDArchitecture designed for workload fit, not vendor lock-in
SCALE-TO-ZEROPay nothing idle. Scale elastically under demand
OBSERVABLEMetrics, alerting and cost tracking from day one
02

The Process

5 STAGES · AUDIT → OPERATE
01Audit & assessMap current infrastructure, identify constraints and define target architecture.
02Cloud architectureDesign topology, networking, IAM and infrastructure-as-code templates.
03CI/CD pipelineAutomated build, test and deploy from every commit to production.
04Harden & observeMonitoring, alerting, security baselines and cost controls.
05Operate & optimiseOngoing scaling, cost optimisation and infrastructure evolution.
03

Stack

TOOLING
GitHub Actions Docker Cloud Run Terraform Kubernetes Vercel Railway
04

Example Outcome

SPIKY → AUTOSCALED
[ SCENARIO ]

A spiky workload migrated to autoscaling containers — infrastructure scales to zero when idle and handles 10x traffic surges without intervention, reducing monthly cloud spend by 70%.

ILLUSTRATIVE ENGAGEMENT
MEASURED IMPACT
−0%
MONTHLY CLOUD SPEND
$0IDLE INFRA COST
< 5 minDEPLOY CYCLE
99.9%UPTIME TARGET
05

FAQ

COMMON QUESTIONS
What does a CI/CD pipeline actually automate?

Every git push triggers a build, test and deploy — no manual SSH, no forgotten steps. Failed tests block the deploy automatically, and rollback is instant if something slips through.

How do you choose AWS, GCP or Azure?

By workload fit and cost structure, not brand preference — we weigh existing tooling, managed-service maturity for your stack, and team familiarity before recommending a provider.

Why containerize instead of deploying directly to a VM?

Containers make environments reproducible and portable across dev, staging and production, and they're what autoscaling and orchestration platforms are built around.

What monitoring do we get out of the box?

Metrics, structured logging, alerting and cost attribution ship with the first deployment — not bolted on later. You see resource usage and spend per service from day one.

How do you reduce cloud spend without hurting reliability?

Autoscaling and scale-to-zero for idle workloads, right-sized instances, and cost attribution that surfaces waste — engagements have cut monthly cloud spend by up to 70%.