Cloud DevOps
Cloud & DevOps

Infrastructure that ships fast and scales without surprises.

We design and implement cloud infrastructure, CI/CD pipelines, and MLOps platforms for AI-first companies - cutting deployment cycles and improving infrastructure efficiency while improving reliability.

AWS / GCP / AzureKubernetesTerraformMLOps
Talk to a DevOps engineer

10×

Faster deployment cycles

60%

Average infrastructure efficiency improvement

99.9%

Uptime SLA achieved

< 5min

Mean time to deploy

What we deliver

Cloud Architecture

Infrastructure design for AWS, GCP, and Azure - including multi-region, multi-account setups, resource optimisation, and cloud-native architecture for AI workloads.

CI/CD Pipelines

Automated build, test, and deployment pipelines using GitHub Actions, GitLab CI, and ArgoCD - enabling multiple production deployments per day with zero-downtime deploys.

Kubernetes & Container Orchestration

Production Kubernetes clusters on EKS, GKE, and AKS - with autoscaling, resource quotas, pod disruption budgets, and GitOps-driven deployments.

Infrastructure as Code

Terraform and Pulumi for reproducible, version-controlled infrastructure. Modular designs that scale from single-region startups to multi-cloud enterprise setups.

MLOps Infrastructure

Model serving infrastructure (Triton, Ray Serve, KServe), model registries, experiment tracking (MLflow, W&B), and automated retraining pipelines.

Observability & Security

Full-stack observability with Datadog, Grafana, and OpenTelemetry. Security scanning in CI, secrets management (Vault / AWS Secrets Manager), and SOC 2 readiness.

Engagement model

From audit to production infrastructure

01
Infrastructure audit

Review your current cloud usage, architecture, CI/CD maturity, and security posture. Identify gaps and quick wins.

02
Architecture design

Design target-state infrastructure - IaC modules, cluster topology, network layout, and IAM model.

03
Pipeline setup

Build CI/CD pipelines with automated testing, container builds, and GitOps-driven deployment to staging and prod.

04
Hardening

Security scanning, secrets management, RBAC, usage alerting, and SLO-based autoscaling configuration.

05
Handoff & runbooks

Full documentation, on-call runbooks, dashboard walkthroughs, and optional ongoing retainer support.

A better way to ship

Manual ops vs. what we build

Without modern DevOps

Deployments require SSH access and manual steps

Environment drift - staging and prod behave differently

Infrastructure defined in wikis, not code (and out of date)

No observability - you learn about outages from users

Cloud usage grows unpredictably with no visibility

Database migrations done manually under pressure

With StartxLabs

Push to main → automated test → deploy to prod in < 5 min

IaC with Terraform: environment parity guaranteed

Version-controlled infrastructure with PR review and audit trail

Full observability: traces, metrics, logs, and alerting before users notice

Usage dashboards with anomaly alerts and rightsizing recommendations

Migration tooling with rollback procedures and zero-downtime patterns

Technology

Tools and platforms we work with

Cloud providers
AWSGCPAzureCloudflare WorkersFly.ioRender
Container & orchestration
DockerKubernetesEKSGKEAKSHelmArgoCDKustomize
Infrastructure as code
TerraformPulumiAWS CDKTerragruntOpenTofu
CI/CD
GitHub ActionsGitLab CICircleCIBuildkiteTektonArgo Workflows
Observability
DatadogGrafanaPrometheusOpenTelemetryJaegerPagerDutySentry
MLOps
MLflowWeights & BiasesRay ServeTriton InferenceKServeKubeflowBentoML
Common questions

FAQ

We're on AWS but considering migrating to GCP - can you help?

Yes. We've run multi-cloud migrations and cloud-to-cloud moves. We start with a capability analysis, map your workloads, then execute the migration in phases with zero-downtime cutovers. We handle DNS, data replication, IAM parity, and monitoring switchover.

Do you work with teams that already have some infrastructure in place?

Most of our engagements are brownfield - existing infra, some CI/CD, inconsistent IaC coverage. We run an audit first, identify the highest-leverage improvements, and evolve your infrastructure without requiring a full rewrite or downtime.

What's the difference between your MLOps offering and standard DevOps?

MLOps adds model-specific concerns on top of standard DevOps: model versioning, experiment tracking, training pipeline orchestration, model serving infrastructure (GPU autoscaling, batching, latency SLOs), and automated retraining triggers when data drift is detected.

How do you handle secrets and access credentials?

We never store secrets in code or environment variables. We implement HashiCorp Vault or AWS Secrets Manager / GCP Secret Manager depending on your cloud, with dynamic credentials where possible, short-lived tokens, and RBAC policies that follow least-privilege principles.

What does an ongoing retainer look like?

Typically 2–4 days per month: on-call escalation support, monthly infrastructure reviews, resource optimisation recommendations, Kubernetes version upgrades, and Terraform module maintenance. We become your on-demand platform engineering team without the full-time hire.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

What happens next

1

Send us a message

Tell us what you're building or what's broken.

2

Discovery call (30 min)

We ask hard questions. You get honest answers.

3

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Contact Us

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

  • Agentic AI development and multi-agent systems
  • Generative AI consulting and LLM integration
  • RAG development and custom model deployment
  • Data engineering, MLOps and custom software
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