AI-Native Cloud & Platform Engineering
Cloud and DevOps without building a full internal platform team.
DeClouder designs, operates and improves cloud-native and hybrid platforms across AWS, Azure and GCP — Kubernetes, Infrastructure as Code, GitOps, CI/CD, observability, security and production reliability.
We also build and improve AI systems for customers — assessment, prototypes, production implementation and optimization.
Senior engineers, amplified by AI automation in our own delivery.
- Clouds
- AWS · Azure · GCP
- Cloud native
- Kubernetes · Terraform · GitOps
- Delivery
- Argo CD · CI/CD · Platform Engineering
- Operations
- Observability · SRE · Security
- AI engineering
- Assessment · Implementation · Evaluation
- Delivered with
- AI-assisted engineering
Your cloud, not ours — we work with the architecture you already have.
Full technical capabilitiesThe problem
Infrastructure eventually becomes too important to manage informally.
Most growing software companies reach the same point. The cloud account one engineer set up now runs the business. Terraform has drifted from reality. Kubernetes works until it does not, and the person who understands it is also shipping features.
The textbook answer is to hire a DevOps, SRE and platform team. That means senior salaries, a long search, ramp-up time and retention risk — for a workload that may not yet justify a full organization.
DeClouder provides that senior cloud and platform capability without requiring you to build the team internally.
Ongoing ownership
Someone is accountable for the agreed scope between incidents and projects — not only when a ticket is open.
Senior capability on tap
Experienced cloud, platform and reliability engineers, without the search, ramp-up and retention cost of building the roles internally.
Continuity and context
The same engineers stay with your environment, so knowledge of your architecture and its history accumulates instead of resetting.
Fewer unnecessary hires
Cover the platform work you genuinely need today without committing to a full DevOps, SRE and security organization before the workload justifies one.
What we do
Senior engineering across cloud, delivery, reliability and AI.
Ongoing operations, the developer platform, delivery pipelines, production reliability, security, scoped projects — and AI systems built for you.
Managed Cloud & DevOps
Ongoing ownership of an agreed cloud and platform scope across AWS, Azure or GCP — operations, infrastructure changes, troubleshooting and steady improvement.
Platform Engineering
The platform your developers build on: Kubernetes, golden paths, self-service environments and reusable infrastructure they can consume safely.
CI/CD & GitOps
Pipeline architecture and Git-driven delivery — reusable pipelines, declarative configuration, Argo CD or Flux reconciliation, real rollback.
SRE & Production Reliability
Operating production, not just building it: troubleshooting, incident response, SLOs, capacity, high availability and disaster recovery.
Cloud Security & DevSecOps
IAM and least privilege, secrets management, infrastructure hardening, policy as code, and scanning built into the pipeline rather than bolted on.
Cloud & Platform Projects
Scoped work with a defined end: migrations, Kubernetes adoption, Terraform modernization, GitOps rollout, observability, networking, DR.
AI Implementation & Engineering
AI systems we build for you: assessment and architecture, prototypes, production implementation, retrieval over your own information, evaluation and optimization.
AI-Assisted Engineering
How we deliverHow we deliver the work above, not something you buy separately. AI is embedded across our engineering and operations; engineers stay accountable.
How we deliver
AI embedded into the engineering workflow.
DeClouder embeds AI throughout infrastructure engineering, CI/CD, troubleshooting, observability, documentation and operations. It compresses the slow parts of the work — orientation across a large estate, drafting infrastructure code, keeping runbooks current, applying mechanical changes consistently — which is how a small senior team covers this much ground.
AI accelerates the work; experienced engineers remain responsible for production outcomes. Production workflows are built around validation, policy checks, approval gates and least-privilege access.
Where it helps: incident investigation and log analysis, Terraform and manifest authoring, pipeline and Kubernetes troubleshooting, pull request review, test generation, runbooks and documentation, repetitive change across many repositories.
Where engineers decide: architecture, security posture, change approval, incident command, and anything that touches production.
How we work
Two practical ways to engage.
Most customers start with one and add the other. What matters is that scope and responsibilities are explicit.
Managed / ongoing
Ongoing engagement
We take responsibility for a defined cloud and platform scope — infrastructure, Kubernetes, CI/CD, GitOps, observability, reliability and security work — under a monthly engagement.
- Agreed scope and agreed responsibilities
- Coverage defined for each engagement
- Scope reviewed as your environment changes
Projects
Defined project
A bounded piece of work with a defined outcome: a migration, a Kubernetes implementation, a Terraform or CI/CD modernization, a GitOps rollout, DR, or an assessment.
- Defined deliverables and success criteria
- Runs alongside or independently of a managed scope
- Specialized engineering consulting where that fits better
What changes
The outcomes worth measuring.
Practical outcomes your engineering team can see in production.
- Reliable production infrastructure and fewer avoidable surprises
- Safer releases with rollback that has actually been rehearsed
- Faster infrastructure changes through consistent Infrastructure as Code
- Less operational burden carried by application developers
- Kubernetes environments that are maintained rather than survived
- Observability engineers trust when something breaks
- Stronger cloud security posture and standardized guardrails
- Infrastructure knowledge in code and runbooks instead of in people
Why DeClouder
Small, senior, and accountable.
01
Senior engineering, not a ticket factory
A small experienced team does the work. No layers of account managers between you and the engineer touching your infrastructure.
02
Broad where it matters
We work across AWS, Azure and GCP, cloud-native and hybrid, and go deep on the parts of your stack that carry production.
03
GitOps-first where it makes sense
Declarative, auditable delivery is the default we argue for — Git as the source of truth, changes by pull request, reconciliation automated. Where it does not fit, we say so.
04
AI-native delivery
AI is embedded in how we investigate, author and review changes — inside the same pipeline and approval gates as everything else.
05
Practical ownership
The job is operating and improving real infrastructure — not producing strategy decks about it.
06
Engineering accountability
Automation assists engineers. Architecture, security, validation and responsibility for production stay with people.
Problems we solve
Developers absorbed by infrastructure work, Kubernetes that has become hard to maintain, fragile CI/CD, unmanageable Terraform, slow production troubleshooting, fragmented observability.
See how we helpAsk DeClouder
Have a question about scope?
Get a direct answer about how we engage, what a managed scope covers, or how we would approach your stack — then talk to an engineer.
Or start with:
Answers are generated and describe our services in general terms. For anything specific to your environment, talk to an engineer.
Common questions
Straight answers.
- Are you a staff augmentation company?
- We can provide engineering capacity, but the model we prefer is ownership of a defined cloud or platform scope, or a scoped project, rather than renting hours.
- Do you replace our internal DevOps team?
- No. We complement an internal team, or provide the platform capability for companies that do not yet need a full internal organization.
- Which clouds do you work across?
- AWS, Azure and GCP, including their managed Kubernetes services, plus hybrid estates. Many environments we work in span more than one. Your cloud, not ours.
- Do you build AI systems for customers, or just use AI internally?
- Both, and they are separate things. AI-assisted engineering is how we deliver cloud and platform work. AI implementation and engineering is a service: systems we design, build, evaluate and optimize for you.
Tell us about your environment
A short conversation is usually enough to tell whether we are a good fit. Describe what you are running today and what is getting in the way.