Cloud
management
SDEN designs, deploys, and operates cloud infrastructure on AWS, GCP, and Azure across US, Canadian, and EU regions, with cost discipline and Infrastructure as Code by default.
What this domain covers
Multi-cloud by training, region-flexible by default. We run on AWS, GCP, or Azure where the workload calls for it, and host in your jurisdiction (US, Canada, or EU) when the threat model and data residency point that way.
Either way, infrastructure ships as code (Terraform, Pulumi, or provider-native IaC) and gets reviewed in the same pull-request flow as the app.
Cost is an engineering output we sign for, not a finance worry downstream. Every feature carries a published $/month figure before it deploys, reconciled against the bill each month. The usual culprits: oversized dev environments first, forgotten snapshots second.
Cloud management: the SDEN defaults
Defaults we ship
- Infrastructure as Code (Terraform): no click-ops in production
- Per-environment isolation with separate accounts / projects
- Per-feature $/month cost estimate published in the deployment PR
- Monitoring (Prometheus / Grafana) and alerting wired before launch
Deliverables
- Terraform modules covering the full stack, version-controlled in your repo
- Multi-environment topology (dev, staging, production) with parity
- Cost dashboard scoped to the project
- Runbooks for the operational tasks the on-call engineer will need
What we refuse to ship
We will not deploy to production with credentials in environment variables on a single VM. Secrets live in a managed store; deploys are reproducible from the repo.
More from
the SDEN blog.
Cornerstone writing from the SDEN team: what AI changes, what it doesn't, and how a senior team ships the difference.
Cloud management in the AI era: from cost-out to capability
Inference workloads, GPU spend, and data-residency rules are rewriting the cloud playbook. How to design infrastructure that holds up under AI-shaped load.
RAG for business: building knowledge assistants that actually work
Retrieval-augmented generation grounds AI answers in your data. What RAG is, when it beats fine-tuning or a plain prompt, and what separates a knowledge assistant you can trust from a demo.
AI agents for business: where they work, and where a workflow wins
Agents are powerful and easy to get wrong. When a task genuinely needs an agent, when a plain workflow is the better answer, and how to keep an agent safe and affordable in production.
Explore