Cut cloud costs by up to 70% — without cutting capabilities.
Cost-Optimized Cloud & AI
Reduce your cloud spend by up to 70% through right-sizing, reserved capacity, and FinOps practices. I turn bloated infrastructure into lean, cost-efficient systems.
What is Cost-Optimized Cloud & AI?
Cloud cost optimization is the practice of cutting your AWS bill without cutting capability. I review where the money actually goes, then right-size compute, move idle or over-provisioned resources to pay-per-use or reserved models, and apply FinOps practices so cost stays visible and under control as you grow. For AI and serverless workloads I also tune model tiers, inference regions, and data-access patterns that quietly drive spend. The work starts with a concrete assessment of your current architecture and usage, so every change is measured against real numbers rather than guesswork. It fits teams whose cloud bill is growing faster than their usage.
Most companies overspend on cloud by 30-50%. I perform a deep cost analysis of your AWS infrastructure, identify waste, and implement FinOps practices that deliver measurable savings — without sacrificing performance or reliability.
My approach combines right-sizing, reserved capacity planning, spot instance strategies, and serverless migration to eliminate idle compute costs. For AI workloads, I optimize model selection, batching strategies, and caching to keep inference costs under control.
Every engagement starts with a detailed cost analysis and ends with a clear savings roadmap — so you know exactly what to expect before committing.
Key Benefits
40-70% Cost Reduction
Most clients see dramatic savings through right-sizing, reserved capacity, and eliminating idle resources.
FinOps Best Practices
Implement cost visibility, accountability, and optimization as ongoing disciplines — not one-time fixes.
AI Cost Control
Optimize model selection, batching, and caching strategies to keep AI inference costs predictable.
What's Included
Use Cases
Proof, not just promises
I run my own products on the same AWS serverless stack I build for clients. GeoHook — a live geofencing-to-webhook service — is built end to end on AWS Lambda, DynamoDB, and API Gateway and deployed as Infrastructure as Code. It's a working example of the architecture, reliability, and cost model described on this page.
Frequently Asked Questions
What is AWS Serverless and why should I care?
AWS Serverless means you run applications without managing servers. Services like Lambda, DynamoDB, and API Gateway automatically scale with demand and you only pay for actual usage. This eliminates idle server costs, reduces operational overhead, and lets your team focus on building features instead of maintaining infrastructure.
How do you ensure GDPR compliance for AI solutions?
I build on AWS Bedrock, which processes data in the EU region (Frankfurt) and doesn't use your data for model training. All my AI architectures include data residency controls, consent management, and audit trails. I design for GDPR compliance from the start — it's not an afterthought.
What does a Fractional CTO engagement look like?
A Fractional CTO engagement typically starts with a technology audit and strategy session. From there, I work with you on an ongoing basis — usually a few days per month — covering architecture decisions, team mentoring, vendor evaluation, and roadmap planning. You get C-level expertise without the full-time salary.
How much can serverless save compared to traditional infrastructure?
Most clients see 40-70% cost reduction after migrating to serverless. The savings come from eliminating idle compute costs, reducing ops overhead, and paying only for actual usage. I provide a detailed cost analysis before any migration to quantify your specific savings potential.
Do you work with international clients?
Yes. I work with clients across Europe and beyond, with experience in cross-border data compliance and multi-region AWS deployments. My GDPR expertise is particularly valuable for companies operating in or selling to the EU market.
What's the typical engagement timeline?
It depends on the scope. A serverless architecture review takes 1-2 weeks. An AI agent MVP typically takes 4-8 weeks. A full cloud migration can span 2-6 months. In a free initial call, I'll map out a timeline tailored to your specific needs and priorities.
Let's Build Something Great
Ready to transform your ideas into reality? Let's discuss how we can help.
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