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dCDeClouder

Services

Cloud, DevOps and platform engineering — owned, not just advised on.

Cloud and platform engineering across the production lifecycle, on AWS, Azure and GCP, cloud-native or hybrid — ongoing operations, the developer platform, delivery, reliability, security and defined projects — plus AI systems built for you.

Ongoing engagement

Managed Cloud & DevOps

Operational ownership of an agreed cloud and platform scope. We handle the day-to-day work of keeping your cloud environment, clusters and delivery pipelines healthy across AWS, Azure or GCP — cloud-native or hybrid — and we make steady improvements while we are in there.

Typical problems

  • Infrastructure changes queue behind whoever happens to know Terraform
  • Production issues escalate to the same one or two people every time
  • Nobody owns the cloud accounts, subscriptions or projects between initiatives
  • Cloud spend grows and no one can explain which workload caused it

Capabilities

  • AWS · Azure · GCP operations
  • Kubernetes / EKS · AKS · GKE
  • Infrastructure as Code
  • CI/CD and GitOps
  • Routine infrastructure changes
  • Production troubleshooting
  • Observability
  • Security and reliability work
  • Cost awareness
  • Documentation and runbooks
  • Operational automation

When it makes sense

You are running meaningful production infrastructure and need someone accountable for the platform week to week, before the workload justifies a full internal DevOps team. Scope and coverage are defined for each engagement.

Ongoing or project

Platform Engineering

Build the platform your developers want to use. The goal is that application developers can consume infrastructure safely and consistently without every developer becoming a cloud expert — and without a ticket queue in the middle.

Typical problems

  • Every service is built, deployed and configured differently
  • Developers wait on infrastructure for routine, repeatable work
  • Environments drift apart, and nobody can say what is running where
  • There is no platform function — infrastructure is a side job for several people

Capabilities

  • Kubernetes platforms
  • EKS · AKS · GKE
  • Internal developer platforms
  • Developer portals
  • Golden paths and templates
  • Self-service environments
  • Environment provisioning
  • Reusable infrastructure modules
  • Backstage
  • Crossplane where it fits
  • CI/CD standardization
  • GitOps and Argo CD
  • Observability integration
  • Security guardrails
  • Developer experience

When it makes sense

Your product organization is growing and infrastructure friction is showing up as slower delivery, inconsistent environments, or work only one person can do. Best value when there are several teams sharing one platform.

Ongoing or project

CI/CD & GitOps

Delivery designed so that shipping is unremarkable. Pipelines are standardized and fast enough to run on every change, and production state is reconciled from Git rather than applied by hand.

Typical problems

  • Deploys need a specific person, a specific laptop, or a manual runbook
  • Pipelines are slow enough that engineers batch changes
  • Rollback means redeploying an older branch and hoping
  • Cluster and application configuration has drifted from what the repository says

Capabilities

  • CI/CD architecture
  • GitHub Actions · GitLab CI
  • Jenkins · CircleCI
  • Azure DevOps Pipelines
  • Bitbucket Pipelines
  • Reusable pipeline templates
  • Self-hosted runners
  • Release and deployment automation
  • Environment promotion
  • Rollback strategies
  • Automated validation
  • Security checks in-pipeline
  • Artifact management
  • Argo CD · Flux
  • Declarative delivery

When it makes sense

Delivery is where the friction is: releases need manual coordination, or you want Git as the source of truth — pull-request-driven changes, auditable history, automated reconciliation and controlled promotion between environments, with drift removed rather than managed.

Ongoing or project

SRE & Production Reliability

Operating production systems, not just building the infrastructure under them. That means being useful during an incident, and doing the unglamorous work that stops the next one.

Typical problems

  • Incidents are discovered by customers first
  • Difficult production issues stay open long enough to become normal
  • Nobody has agreed what "reliable enough" means for each service
  • Backups are configured but recovery has never actually been tested

Capabilities

  • Production troubleshooting
  • Incident response and management
  • Root-cause analysis and postmortems
  • SLI / SLO / SLA design
  • Error budgets
  • Capacity planning
  • Performance engineering
  • High availability
  • Multi-region architecture
  • Disaster recovery, RTO / RPO
  • Backup and recovery strategy
  • Failover testing
  • Runbooks and operational readiness
  • On-call design
  • PagerDuty · Opsgenie where used

When it makes sense

You need reliability treated as engineering work with someone accountable for it — whether that is helping design SLOs and on-call, or being in the room when production is broken. Coverage is agreed per engagement rather than promised as a blanket number.

Ongoing or project

Cloud Security & DevSecOps

Infrastructure security as engineering work: tightening what is too open, managing secrets properly, expressing policy as code, and moving scanning into the pipeline so problems surface before they ship.

Typical problems

  • IAM has accumulated permissions broader than anyone intended
  • Secrets live in environment variables, CI settings or worse
  • Security controls differ between accounts, clusters and teams
  • A vulnerability report arrived and there is no clear remediation path

Capabilities

  • IAM and least privilege
  • Entra ID · Okta · SSO · RBAC
  • Workload identity
  • Secrets management · Vault
  • Cloud-native secret stores
  • KMS and key management
  • Infrastructure hardening
  • Container and image scanning
  • SAST · SCA · dependency scanning
  • Secrets scanning
  • Trivy · Snyk · Checkov · tfsec
  • SonarQube
  • SBOM and image signing
  • Policy as code · OPA · Kyverno · Gatekeeper

When it makes sense

You want infrastructure-level security implemented by the engineers who will fix it, integrated into delivery. We help engineering teams implement and operate controls aligned with frameworks such as SOC 2, PCI DSS, HIPAA, CIS benchmarks and NIST.

Defined project

Cloud & Platform Projects

Infrastructure projects with a clear beginning and end, run outside or alongside managed operations. Scope, deliverables and success criteria are agreed before work starts.

Typical problems

  • A migration keeps getting deferred because nobody has the bandwidth to own it
  • Terraform has grown organically and now resists change
  • You are adopting Kubernetes or GitOps and want the first implementation done properly
  • Cluster upgrades have been postponed to the point of risk

Capabilities

  • Cloud migrations
  • Datacenter-to-cloud
  • Cloud-to-cloud migration
  • Replatforming
  • Kubernetes adoption and upgrades
  • EKS · AKS · GKE implementations
  • Terraform modernization
  • GitOps and Argo CD adoption
  • CI/CD redesign
  • Observability implementation
  • Cloud networking redesign
  • Disaster recovery
  • Account / subscription / project restructuring
  • Platform and infrastructure modernization

When it makes sense

A specific piece of infrastructure work needs to get done well, once, without pulling your product engineers off their roadmap for a quarter.

Assessment, project or ongoing

AI Implementation & Engineering

AI systems built for you, as the deliverable. Assessment and architecture, focused prototypes, production implementation, assistants that answer from your own approved information, workflow automation and integrations, and the evaluation and optimization that decide whether any of it holds up.

Full service detail

Typical problems

  • AI looks useful somewhere in the business and nobody has scoped where
  • A prototype works in a demo and not on real data
  • An assistant answers confidently and cites nothing
  • A workflow in production has no baseline for quality, cost or latency

Capabilities

  • Opportunity assessment
  • Technical architecture
  • Prototypes and pilots
  • Production implementation
  • Knowledge retrieval
  • Source attribution
  • Workflow automation
  • MCP and API integrations
  • Output quality evaluation
  • Model selection
  • Context handling and caching
  • Cost, latency and observability
  • Engineering support alongside your team

When it makes sense

You are exploring a first practical AI application, or you already run AI products and want specialist depth or extra delivery capacity for a defined scope. Capabilities are selected to fit the engagement rather than delivered as a bundle.

How we deliver, not what we sell

AI-Assisted Engineering

Our engineers use AI coding agents, agentic workflows and operations tooling as part of normal delivery. This is the delivery method behind the services above — distinct from AI Implementation & Engineering, which is AI we build for you as the deliverable.

Typical problems

  • Repetitive infrastructure work consuming senior engineering time
  • Slow investigation across logs, metrics, manifests and configuration
  • Documentation that goes stale the moment it is written
  • Mechanical changes that must land consistently across many repositories

Where we use it

  • AI coding agents · Claude Code
  • Agentic engineering workflows
  • Infrastructure code generation
  • Terraform and manifest analysis
  • Kubernetes and pipeline troubleshooting
  • Pull request generation and review
  • Test generation
  • Log and incident investigation
  • Runbook and documentation generation
  • MCP and agent SDKs
  • Least-privilege agent access
  • Human approval gates

What this means

This is why a small senior team can cover this much ground. AI accelerates the work, and experienced engineers remain responsible for architecture, judgment, security, validation and production outcomes.

Engagement

How these services are delivered.

We do not publish package pricing, because the right scope depends on what you are running and what you need us to own. Every engagement starts by agreeing that scope explicitly.

Technical capabilities in detail Problems we are brought in to solve

Ongoing engagement

We take responsibility for a defined cloud and platform scope under a monthly engagement — infrastructure, platform engineering, Kubernetes, CI/CD, GitOps, observability, reliability, security and operational support. Coverage is defined for each engagement, and it is agreed as a scope of responsibility rather than a block of hours.

Defined project

Larger transformations and one-time engineering work — migrations, Kubernetes implementations, platform redesign, Terraform modernization, GitOps, CI/CD modernization, observability, DR, security improvements — scoped separately with defined deliverables.

Specialized engineering consulting is available where that fits better than either model. Work outside an agreed managed scope is scoped separately rather than absorbed silently.

Not sure which of these you need?

Describe your environment and the problem you are trying to solve. We will tell you which of these fits, or whether you need something else entirely.

Discuss Your Platform info@declouder.ai