Cloud architecture that scales — and deployments that don’t wake you at 3am
I’ve migrated a live production system from monolith to microservices with near-zero downtime and 3× the throughput — while cutting infrastructure cost to roughly $3/month per customer site. Cloud bills and deployment pain are engineering problems; they have engineering solutions.
Teams whose monolith is slowing every release and every scale event
Startups with runaway cloud bills that nobody can explain
ML teams whose models work in notebooks but limp in production
What you get
Cloud architecture & migration
AWS, Azure, and GCP architecture design and workload migration — including the monolith-to-microservices path I’ve executed on a live system: 3× throughput, near-zero deployment downtime.
Containerisation & orchestration
Docker, Kubernetes, and the honest assessment of whether you need K8s at all — many products scale further on simpler infrastructure run well.
CI/CD & release engineering
GitHub Actions and Azure DevOps pipelines: automated tests, staged rollouts, and rollback paths — so deploys become boring.
MLOps & model deployment
Model versioning, ONNX-optimised serving (2–3× faster CPU inference), monitoring for silent degradation, and GPU-aware cost control.
Observability & cost control
Grafana, Prometheus, and Sentry wired to what matters. Automated anomaly detection on service health metrics cut critical incident response time by 70% in one deployment.
A growing real-time product on a monolith: every deploy risked downtime for all properties, and scaling one hot path meant scaling everything.
Built
Designed and executed a full migration to AWS microservices — service boundaries drawn around the real load profile, Dockerised deployment, staged rollout, and health-metric anomaly detection for incident response.
Results
3× throughput post-migration
Near-zero deployment downtime
~$3/month infrastructure cost per property
70% faster critical-incident response via automated anomaly detection
Maybe not — and I’ll tell you if so. Microservices trade operational complexity for independent scaling and deployment. If your team is small and your load is uniform, a well-structured monolith with good CI/CD often wins. I’ve done the migration when it was right; the audit tells you whether it is.
Can you cut our cloud bill?
Usually 30–60% on unoptimised accounts: right-sizing, reserved capacity, killing zombie resources, and moving hot paths off premium services. One of my production systems runs at ~$3/month per customer site — cost is designed, not discovered.
What does MLOps actually get us?
The difference between "the model worked last month" and knowing it works now: versioned models, reproducible training, monitored inference (latency, confidence drift), and one-command rollback. If AI touches revenue, this is not optional.
Do you do ongoing ops or just the setup?
Both. Most engagements end with your team owning boring, documented infrastructure. Some clients keep a monthly retainer for reviews, upgrades, and incident support — your call.
Have a project in mind?
A free 30-minute call — you describe the problem, I tell you honestly whether and how I'd solve it.