Capabilities
The technical ground we actually cover.
Technical depth across the modern cloud-native platform stack — infrastructure, delivery, reliability, security and operations.
Multi-cloud
Your cloud, not ours. Whether your platform runs on AWS, Azure, GCP, across several of them, or alongside infrastructure you still operate yourself, we work with the architecture you actually have — cloud-native and hybrid environments both.
What you get
- Cloud architecture chosen for the workload rather than for a vendor relationship
- One operating model across accounts, subscriptions and projects
- Hybrid and multi-environment estates operated without a separate process for each
AWS
- EC2 · ECS · EKS
- Lambda
- VPC and networking
- S3 · EBS · EFS
- RDS · Aurora · DynamoDB
- IAM · Organizations
- CloudWatch
- KMS · Secrets Manager
- Account structure
Microsoft Azure
- AKS
- Azure Functions
- VNet and networking
- Blob Storage
- Azure SQL · Cosmos DB
- Entra ID · RBAC
- Azure Monitor
- Key Vault
- Subscriptions and management groups
Google Cloud
- GKE
- Cloud Run · Cloud Functions
- VPC and networking
- Cloud Storage
- Cloud SQL
- IAM · workload identity
- Cloud Monitoring and Logging
- Secret Manager
- Projects and folders
Kubernetes & containers
Managed Kubernetes across the three clouds, and the surrounding components that decide whether a cluster is maintainable — ingress, certificates, scaling, upgrades, and the container supply chain feeding it.
What you get
- Clusters that can be upgraded on a schedule instead of postponed indefinitely
- Scaling behaviour based on measured usage rather than an initial guess
- Smaller, scanned images and a container supply chain you can account for
Clusters
- Kubernetes
- Amazon EKS
- Azure AKS
- Google GKE
- Cluster upgrades
- Multi-cluster environments
Workload delivery
- Helm
- Kustomize
- Argo CD
- Flux
- Kubernetes operators
Cluster services
- Ingress controllers
- cert-manager
- Service mesh
- Istio · Linkerd
- Autoscaling · HPA
- Karpenter
Containers
- Docker · BuildKit
- ECR · ACR · Artifact Registry
- Image optimization
- Vulnerability scanning
Infrastructure as Code
Infrastructure defined in code, reviewed like code, and applied through a pipeline — with state layout and module boundaries designed deliberately rather than inherited from whatever was fastest on day one.
What you get
- Environments that can be rebuilt from the repository, not from memory
- Infrastructure changes that are reviewable and reversible
- Drift found and fixed while it is still cheap
Provisioning
- Terraform
- CloudFormation
- Bicep · ARM
- Pulumi
- Reusable modules
- State design and remote state
Configuration and images
- Ansible
- Packer
- Environment automation
- Automated provisioning
Control
- Policy-controlled infrastructure
- Infrastructure standardization
- Drift detection and reconciliation
GitOps & continuous delivery
GitOps-first where it makes sense: Git as the source of truth, changes proposed by pull request, declarative environments, and automated reconciliation — with pipelines standardized enough that shipping is unremarkable.
What you get
- Repeatable deployments that do not depend on a particular person
- A single audit trail of what changed in production, and why
- Rollback treated as an ordinary, rehearsed operation
GitOps
- Argo CD
- Flux
- Declarative environments
- Automated reconciliation
- Controlled environment promotion
- Auditable change history
- Drift reduction
CI/CD systems
- GitHub Actions
- GitLab CI
- Jenkins
- Azure DevOps Pipelines
- CircleCI
- Bitbucket Pipelines
Pipeline engineering
- Reusable pipeline templates
- Self-hosted runners
- Release and deployment automation
- Rollback and recovery patterns
- Automated validation
- Security checks in-pipeline
- Pipeline troubleshooting
Platform engineering
The layer between your developers and the cloud. Application developers should not need to become cloud infrastructure experts just to ship software — so the common paths are paved, templated and safe by default.
What you get
- Developers ship without filing infrastructure tickets or reading Terraform
- New services start from a known-good template instead of a copied one
- Guardrails applied by default rather than reviewed after the fact
Developer platform
- Internal developer platforms
- Developer portals
- Backstage
- Crossplane
- Golden paths
Self-service
- Self-service environments
- Environment provisioning
- Reusable templates
- Standardized deployment workflows
Foundations
- Kubernetes platforms
- Platform automation
- Security guardrails
- Observability built in
- Developer experience
Observability
Logs, metrics and traces consolidated where engineers will actually look, instrumented around what indicates health, with alerting reduced to signals a human should act on. We favour OpenTelemetry so instrumentation is not locked to one backend.
What you get
- You find out about problems before your customers tell you
- Instrumentation that survives a change of monitoring vendor
- Alerts people still read, because the noise was removed
Instrumentation
- OpenTelemetry
- Metrics · logs · traces
- Distributed tracing
- APM
Open source stack
- Prometheus
- Grafana
- Loki
- ELK · Elasticsearch
- OpenSearch
Commercial and cloud-native
- Datadog
- New Relic
- Splunk
- CloudWatch
- Azure Monitor
- Google Cloud Monitoring
Operational use
- Dashboards
- Alerting design
- Production troubleshooting
SRE & reliability
The engineering that decides how production behaves under stress and how quickly it recovers — agreed targets, tested recovery, and incident practice that produces fixes rather than folklore.
What you get
- An agreed, measured definition of "reliable enough" per service
- Recovery that has been tested rather than assumed
- Incidents that end in a fix and a runbook, not a retelling
We help design the SLIs, SLOs and the SLAs your own service commitments depend on. Coverage for our engagement is agreed alongside them.
Targets and practice
- SLI / SLO / SLA design
- Error budgets
- Incident response and management
- Root-cause analysis
- Postmortems
- On-call design
- PagerDuty · Opsgenie
Resilience
- High availability
- Multi-region design
- Failover testing
- Disaster recovery
- RTO / RPO
- Backup and recovery strategy
Performance
- Capacity planning
- Performance engineering
- Load testing
Readiness
- Runbooks
- Operational readiness reviews
- Documentation
Cloud security & DevSecOps
Identity, secrets, hardening and policy as code — with scanning inside the pipeline so issues surface on the pull request instead of in production.
What you get
- Access that reflects what people and workloads actually need
- Secrets in a managed store rather than in CI settings
- Security findings raised on the pull request, when they are cheap to fix
We help engineering teams implement and operate controls aligned with frameworks such as SOC 2, PCI DSS, HIPAA, CIS benchmarks and NIST — building the controls and the evidence for them. Formal audit and certification stay with your auditor.
Identity and access
- IAM and least privilege
- Microsoft Entra ID
- Okta · SSO
- RBAC
- Workload identity
Secrets and keys
- HashiCorp Vault
- AWS Secrets Manager
- Azure Key Vault
- Google Secret Manager
- KMS and key management
Pipeline security
- SAST · SCA
- Container scanning
- Dependency scanning
- Secrets scanning
- Trivy · Snyk
- Checkov · tfsec
- SonarQube
- SBOM
- Image signing and supply-chain controls
Policy and hardening
- Policy as code
- OPA · Gatekeeper · Kyverno
- Infrastructure hardening
- Cloud security controls
Cloud networking
Network design and the connectivity between cloud, on-premises and third parties — usually where the hardest production problems turn out to live.
What you get
- A network design someone can explain, and a diagram that matches reality
- Private connectivity where traffic should not traverse the internet
- Cross-boundary latency and failure modes that are understood, not guessed at
Design
- VPC / VNet architecture
- Subnets and routing
- DNS · Route 53
- Load balancing
Edge
- CDN · CloudFront
- WAF
- TLS and certificates
Connectivity
- VPC / VNet peering
- PrivateLink · private endpoints
- VPN
- Direct Connect · ExpressRoute
- Hybrid connectivity
Security
- Network segmentation
- Security groups and policy
- Egress control
Data & platform infrastructure
We operate the infrastructure these systems run on — provisioning, high availability, upgrades, backup and recovery, monitoring and platform integration.
What you get
- Data services with tested restores and a known failover path
- Version upgrades that happen before end-of-support forces them
- Stateful infrastructure managed the same way as everything else
We support databases and streaming systems as part of the broader platform and infrastructure environment. Schema and query design stay with your engineers.
Relational
- PostgreSQL
- Aurora · RDS
- MySQL
- SQL Server
- Cloud SQL · Azure SQL
Non-relational and cache
- DynamoDB
- Cosmos DB
- Redis
- Elasticsearch · OpenSearch
Streaming and messaging
- Kafka
- Amazon MSK
- RabbitMQ
Operational work
- Database migrations
- Version upgrades
- Backup and restore testing
- Replication and failover
- Platform integration
Serverless & event-driven
Where a managed runtime is the right answer, with the same infrastructure and delivery discipline applied to it as to anything else.
What you get
- Serverless workloads under the same IaC and pipeline as the rest of the estate
- Event-driven designs with retries and failure handling thought through
Runtimes
- AWS Lambda
- Azure Functions
- Google Cloud Run
- Google Cloud Functions
Integration
- API Gateway
- Event-driven architecture
- Queues and event buses
- Scheduled workloads
Automation & engineering tooling
The glue work: scripting and API automation for the operational tasks that should never be manual, and the source control and artifact layer everything else depends on.
What you get
- Recurring manual work replaced by something that runs the same way every time
- Build output stored, versioned and retrievable when you need to roll back
- A branching and release model the whole team follows
Automation
- Python
- Bash
- PowerShell
- Cloud and platform APIs
- Operational automation
- Infrastructure automation
Source control
- GitHub
- GitLab
- Bitbucket
- PR workflows
Artifacts
- Artifactory
- Nexus
- Container registries
- Package repositories
- Artifact management
Release practice
- Branching strategies
- Release strategies
- Versioning and tagging
AI-native engineering
DeClouder embeds AI throughout infrastructure engineering, CI/CD, troubleshooting, observability, documentation and operations. AI accelerates the work; experienced engineers remain responsible for production outcomes.
This is a delivery advantage, not a product we sell. It is the reason a small senior team can cover the ground on this page.
What you get
- Senior engineers spending their hours on judgment, not on mechanical work
- Faster investigation when production is misbehaving
- Documentation and runbooks that stay current
Engineering
- AI coding agents · Claude Code
- Agentic engineering workflows
- Infrastructure code generation
- Terraform analysis
- Pull request generation
- PR review assistance
- Test generation
Operations
- Kubernetes troubleshooting
- CI/CD and pipeline analysis
- Log analysis
- Incident investigation
- Root-cause assistance
- Release validation and change analysis
- Runbook and documentation generation
Integration
- MCP
- Agent SDKs
- Controlled access to engineering systems
- Operational automation
- Security analysis
Controls
- Least-privilege AI workflows
- Human approval gates
- Policy and security checks in-pipeline
- Production access governed per environment
Controlled delivery
Where AI sits in the delivery lifecycle.
AI-assisted work travels the same path as everything else: validated, reviewed by a person, merged, then reconciled into the environment by GitOps.
- 01
Understand
- Request, change or incident
- Engineer investigates with AI assistance across code, config, metrics and logs
- 02
Change
- Infrastructure or application change authored, AI-assisted
- Automated validation: format, lint, plan, unit and policy tests
- 03
Review
- Pull request with the plan and diff attached
- CI/CD runs security, IaC and dependency scanning
- Engineer reviews and approves the change
- 04
Deliver
- GitOps reconciliation from the merged commit
- Deployment observed against metrics, logs and traces
- AI-assisted post-deployment review and documentation update
Want to go deeper on any of this?
Tell us what you are running and we will put you in front of the engineer who works in that part of the stack.