What cloud providers do you work with?

AWS, GCP, and Azure — and Cloudflare for edge infrastructure. I am cloud-agnostic and will recommend the right provider for your existing stack and compliance requirements. Echonos, for example, runs its web app on Vercel and its AI pipeline services on Google Cloud with Firebase.

How much can serverless actually reduce cloud costs?

Typical savings range from 40% to 80% for workloads that are bursty or unevenly distributed across the day. The biggest wins come from eliminating idle EC2/ECS capacity and replacing always-on services with event-driven functions. The Echonos case study shows the same discipline applied to staged releases and rollback for an AI pipeline.

Will serverless architecture slow down local development?

Not if it is designed well. I use tools like SST (Serverless Stack), AWS SAM, and Cloudflare Miniflare to provide fast local development loops with production-identical behaviour. The goal is zero friction between local, staging, and production environments.

Service

Serverless Cloud Engineering

I architect the best serverless architecture for cloud-based AI — infrastructure that runs itself, auto-scales to any load, and keeps your AWS bill predictable. As Senior DevOps Engineer at Echonos AI I run release engineering for an AI music video platform on Vercel, Firebase and Google Cloud: branch-aware deploys, staged promotion, smoke checks and rollback.

What you walk away with

✓Infrastructure-as-code in Terraform or CDK — fully repeatable, environment-parity guaranteed
✓Serverless architecture design — function decomposition, event routing, and cold-start mitigation
✓Edge deployment with Cloudflare Workers or Lambda@Edge for global latency reduction
✓Observability stack — structured logging, distributed tracing, alerting, and dashboards
✓CI/CD pipeline with preview environments, canary deployments, and automated rollback
✓Cost optimisation report with before/after projections

Engagement model

Infrastructure Review

Audit

1–2 weeks. Audit current infrastructure for cost, reliability, and scaling bottlenecks. Delivered as a written report with prioritised recommendations.

Target Architecture

Design

1–2 weeks. Define the target serverless architecture — function boundaries, event flows, and IaC structure.

Build & Migrate

Migration

6–16 weeks. Implement new infrastructure alongside existing — zero-downtime migration with gradual traffic shifting.

Performance & Cost Tuning

Optimise

2–4 weeks. Cost baseline vs. target comparison, performance profiling, and documentation handoff.

From my own work

Where I have done this

Echonos AI
Senior DevOps Engineer
Branch-aware deploys, staged promotion, smoke checks, rollback and incident runbooks on Vercel, Firebase and Google Cloud.
Read the case study →
Sukaizen
Founder
Automated daily publishing, video rendering and indexing pipelines that run without a server to babysit.
Read the case study →

Recent case study

Echonos AI Music Video Platform

Release engineering for an AI platform that turns songs into beat-synced music videos.

Read the full case study →

Frequently asked questions

What cloud providers do you work with?: AWS, GCP, and Azure — and Cloudflare for edge infrastructure. I am cloud-agnostic and will recommend the right provider for your existing stack and compliance requirements. Echonos, for example, runs its web app on Vercel and its AI pipeline services on Google Cloud with Firebase.
How much can serverless actually reduce cloud costs?: Typical savings range from 40% to 80% for workloads that are bursty or unevenly distributed across the day. The biggest wins come from eliminating idle EC2/ECS capacity and replacing always-on services with event-driven functions. The Echonos case study shows the same discipline applied to staged releases and rollback for an AI pipeline.
Will serverless architecture slow down local development?: Not if it is designed well. I use tools like SST (Serverless Stack), AWS SAM, and Cloudflare Miniflare to provide fast local development loops with production-identical behaviour. The goal is zero friction between local, staging, and production environments.

Related writing

Production RAG: Why Retrieval Fails and How to Fix It

Read the post →

How to Evaluate LLM Agents in Production: Beyond Unit Tests

Read the post →

Related services

AI Systems Architecture →Next.js Development →

Ready to start a Serverless Cloud Engineering engagement?

Book a 30-minute strategy call. I will map out a scope and timeline specific to your situation.

Book a strategy call