← Back to Services
Cloud Infrastructure That Works
Cloud & DevOps
Cloud Infrastructure That Works
When You Need It Most.
We design, migrate, and operate cloud environments that are secure by default, fully observable, and built to scale — without surprise outages or runaway bills.
What We Deliver
- Cloud Architecture Design: environment topology, VPC design, networking, IAM strategy, and reference architectures across AWS, Azure, and GCP — right-sized for your actual workload and budget, not a generic template that over-provisions in some areas and leaves gaps in others.
- Migrations & Modernization: lift-and-shift and re-architect migrations planned workload by workload to minimize downtime and risk to production — not a copy-paste to the cloud that preserves all the same problems in a different location and adds a monthly bill on top.
- Security & Compliance: IAM with least-privilege from the start, secrets management that keeps credentials out of environment variables and repositories, network segmentation, encryption in transit and at rest, and audit logging that actually captures what matters — built in, not retrofitted after the first audit finding.
- CI/CD & DevOps: automated pipelines from commit to production — build, test, lint, security scan, deploy — with rollback capability, deployment gates, and environment promotion controls that make releases routine rather than events your team dreads.
- Observability & SRE: centralized logging, distributed tracing, uptime monitoring, alerting with meaningful thresholds, and on-call runbooks that reflect how your system actually behaves — so when something goes wrong at 2am, whoever responds can find the problem and fix it, not spend an hour trying to understand what they're looking at.
- Cost Optimization: right-sizing, reserved capacity planning, unused resource identification, and cost allocation tagging — cloud spend made visible, attributed to the workloads generating it, and actively reduced rather than accepted as an inevitably growing line item nobody questions.
Common Scenarios
- "Our app keeps going down under load": performance investigation to find the actual bottleneck — which is rarely where everyone assumes it is — followed by auto-scaling configuration, load balancer tuning, caching layers, connection pool sizing, and resilience patterns that make the system handle real traffic spikes without your on-call rotation getting paged at midnight. The difference between an outage and a non-event is usually architecture decisions that could have been made before the traffic arrived.
- "We're moving off bare metal or on-prem": a full cloud migration that treats each workload individually — assessing what to lift-and-shift, what to re-architect, what to replace with a managed service, and what to retire — rather than a bulk move that recreates your existing problems in a cloud wrapper. Phased plan, validated at each stage, with zero-downtime cutover for the services your business can't afford to take offline.
- "We don't know where our cloud money is going": a cost audit that traces spend to the workloads generating it, a tagging taxonomy your teams will actually maintain, rightsizing recommendations based on real utilization data, and reserved instance and savings plan analysis that captures commitments only where the numbers clearly justify them. Cloud bills grow because nobody owns them — we make ownership and accountability part of the solution, not just a report.
- "Our deployments are manual and scary": when deploying means SSH-ing into servers, running scripts in the right order, and hoping nothing breaks — and when it does break, there's no clean rollback path — the solution isn't more careful manual process. It's CI/CD pipelines, infrastructure-as-code, environment promotion gates, and automated rollback so that the act of deploying becomes something any engineer can trigger with confidence, not a ceremony reserved for Fridays when the senior person is available.
How We Work
01
Assessment
Audit your current architecture, costs, security posture, and operational gaps to establish a clear baseline.
02
Design
Future-state architecture design with trade-offs documented — no black-box recommendations.
03
Migrate / Build
Execute changes with proper staging environments, testing gates, and rollback plans at every step.
04
Operate & Optimize
Ongoing monitoring, runbook maintenance, cost reviews, and continuous hardening after go-live.
Platforms & Tooling
- AWS: EC2, ECS/EKS, RDS/Aurora, Lambda, S3, CloudFront, Route 53, IAM, Secrets Manager, CloudWatch, GuardDuty — and the broader AWS ecosystem for production workloads of any scale. AWS is our most common deployment target, and the depth of the ecosystem means most architecture decisions have a purpose-built service answer rather than requiring custom infrastructure that adds operational overhead.
- Azure: AKS, App Service, Azure SQL, Cosmos DB, Azure DevOps, Entra ID, Key Vault, Monitor, Defender for Cloud, and enterprise-grade Azure networking and hybrid connectivity. Azure is typically the right choice for organizations with existing Microsoft infrastructure, Active Directory, or enterprise licensing agreements — and for regulated industries where the Azure compliance posture and regional data residency options are material decision factors.
- GCP: GKE, Cloud Run, BigQuery, Cloud SQL, Pub/Sub, Cloud Armor, Secret Manager, and GCP-native observability and security tooling for data-intensive and container-first workloads. GCP's strength is analytics and data infrastructure — if BigQuery is a core part of your stack, or if you're building around ML/AI pipelines, the native integration with the Google data toolchain typically outweighs the cost of deviating from the more widely adopted alternatives.
- Infrastructure as Code: Terraform and Pulumi for cloud-agnostic, version-controlled infrastructure — no manual console changes, full auditability, and reproducible environments. Infrastructure managed through the console accumulates drift that nobody tracks, creates environments that can't be recreated consistently, and makes every audit or compliance review harder because the actual state of the infrastructure exists only in someone's memory or a screenshot.
- Containers & orchestration: Docker, Kubernetes (EKS / AKS / GKE), Helm, and service mesh patterns for reliable, scalable container workloads in production. Running Kubernetes in production is a different discipline from getting it running in a development environment — resource requests and limits, pod disruption budgets, network policies, cluster autoscaling, and graceful shutdown behavior are the details that determine whether a cluster is actually production-grade or just technically running.
- Observability stack: Datadog, Grafana, Prometheus, OpenTelemetry, PagerDuty, and cloud-native logging — structured, correlated, and actionable from day one. The difference between an observability setup that helps and one that doesn't isn't the tooling — it's whether logs are structured and correlated, whether alerts are tuned to signal rather than noise, and whether the runbooks attached to alerts actually reflect how the system behaves under the conditions that trigger them.
Who We Work With
- Engineering teams inheriting cloud sprawl: infrastructure accumulated without a plan — paying for resources nobody uses, with no visibility into what's running or why. The compounding problem is that the longer sprawl goes unaddressed, the more dependent systems build up on top of it — making cleanup harder each month and the bill larger every quarter while the underlying architecture debt accumulates.
- Companies planning a cloud migration: moving workloads from data centers, colocation, or managed hosting and needing a strategy that minimizes risk and downtime. A migration done without per-workload assessment typically recreates on-premises problems in cloud form — the same performance bottlenecks, the same monolithic structures, the same operational fragility — just with a cloud bill added and a false sense of modernization.
- SaaS companies scaling fast: growing beyond what their current cloud setup was designed for — hitting reliability walls, rising costs, and deployment friction at scale. The setup that worked at 50 concurrent users starts showing cracks at 500 and breaks visibly at 5,000 — not because the original decisions were bad, but because infrastructure designed for early-stage product validation wasn't built to carry production traffic from a growing customer base.
- Teams needing DevOps expertise on demand: cloud and DevOps capability without the cost and delay of hiring a full-time platform engineer or building an internal team from scratch. The right engagement model when you have a specific project, a capability gap that needs filling for a defined period, or an existing team that needs specialist support embedded alongside them rather than a permanent hire they don't yet have the volume to justify.
Why Kubrik for Cloud
- We build for what breaks in production, not what works in a demo: anyone can spin up a Kubernetes cluster. Designing one that handles irregular traffic, survives node failures, drains gracefully, and can be debugged at 2am by someone who didn't build it — that's the work. We've seen what breaks under real load, and we design around it upfront.
- Security is in the architecture, not bolted on afterward: IAM policies written with least privilege from the start, secrets never touching environment variables, network boundaries defined before the first service goes live, and audit trails that actually capture what matters. Retrofitting security onto an existing cloud environment is expensive — we avoid that by doing it right the first time.
- Full-stack perspective across application and infrastructure: our cloud engineers work closely with our software engineers. That means infrastructure decisions are informed by how the application actually behaves — query patterns, connection pooling, cache invalidation, deployment frequency — not abstracted from it. The gaps between app teams and platform teams are where most incidents originate.
- We leave documentation your team can actually use: runbooks, architecture decision records, cost dashboards, and on-call playbooks that reflect your actual system — not generic templates. When we hand over, your team can operate what we built without us in the room.
Results You Can Expect
Stable production systems that handle traffic spikes without incidents — and when issues do occur, faster mean-time-to-recovery because your team has the observability and runbooks to find and fix problems rather than guess at them. Automated deployments that turn releases from stressful events into routine operations any engineer can trigger without senior oversight. Cloud spend that's visible, attributed, and actively managed — teams consistently find 20–40% in recoverable waste once spend is properly tagged and reviewed. Security posture that holds up to scrutiny from enterprise customers, compliance auditors, and your own engineers. And infrastructure documented well enough that the next person who joins can understand what exists, why decisions were made, and how to operate it without a two-week handover from the person who built it.
