AWS Certified Solutions Architect and FinOps Certified Practitioner helping organisations understand, govern, and reduce what they spend on AI — before the bill arrives.
Context windows, embeddings, vector DBs, logging, retries — the hidden cost drivers most AI teams discover too late.
The Inform → Optimise → Operate framework applied to LLM workloads — and why AI spend is different from EC2.
I post weekly on AI cost management, FinOps, and cloud engineering. Connect and join the conversation.
I'm a Manchester-based FinOps Certified Practitioner and AWS Solutions Architect who has spent 5+ years at the intersection of cloud infrastructure and financial management. My background in Quality Assurance gives me an unusual perspective — I approach cost governance the same way I approach testing: systematically, with an eye for where things go wrong before they do.
My current focus is AI cloud cost management — helping organisations move beyond reactive monthly bills and toward proactive governance of their LLM and AI infrastructure spend. That means tagging strategies, anomaly alerting, context window cost modelling, and Power BI dashboards that translate fragmented billing data into decisions.
I've worked with AWS, Azure, and Huawei Cloud across financial services, technology, and the third sector. I'm comfortable presenting to technical and non-technical audiences alike, and I care about making data accessible — hence the WCAG 2.1 dashboards.