"𝘛𝘩𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 𝘪𝘴𝘯'𝘵 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘈𝘐 𝘸𝘪𝘭𝘭 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴, 𝘣𝘶𝘵 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘺𝘰𝘶'𝘭𝘭 𝘭𝘦𝘢𝘥 𝘵𝘩𝘢𝘵 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘰𝘳 𝘣𝘦 𝘥𝘪𝘴𝘳𝘶𝘱𝘵𝘦𝘥 𝘣𝘺 𝘪𝘵" 🚀

As a Principal Solutions Architect at AWS, I architect transformative cloud solutions that drive business outcomes, with deep expertise in Machine Learning and Generative AI. My evolution from hands-on developer to enterprise architect gives me a unique lens for building scalable, efficient solutions that bridge technical excellence with business value.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 🎯 I specialize in Media & Entertainment, guiding organizations through complex digital transformations. As a founding member of AWS’s global expert group for Advertising and Marketing Technology, I combine deep industry knowledge with practical implementation experience to solve real-world challenges.

𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 & 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 💡 I’m passionate about demystifying emerging technologies, particularly Generative AI. Through industry speaking engagements, technical deep-dives, and strategic keynotes, I help teams understand not just the “how” but the “why” and “what’s next” of technology adoption. My content focuses on practical implementation while thoughtfully examining the broader implications of technological change.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 🏔️ When I’m not architecting cloud solutions, you’ll find me exploring mountain trails. This love for nature provides the perfect counterbalance to my technical work and often inspires fresh perspectives on complex problems.

𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗮𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻? 🎯 Whether you’re looking to architect your next breakthrough solution, navigate emerging AI technologies, or transform your organization’s approach to cloud innovation—let’s connect. I’m always excited to discuss how technology can unlock new possibilities for your business.

==> 📩 𝗗𝗿𝗼𝗽 𝗺𝗲 𝗮 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 | 🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗳𝗼𝗿 𝗔𝗜 & 𝗖𝗹𝗼𝘂𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 | 🤝 𝗟𝗲𝘁’𝘀 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲

This is a personal blog. Opinions expressed here are my own and do not represent the views or positions of my employer.

Recent Blog Posts

Why AI Tokens Are So Expensive — and What Actually Makes Them Cheaper

TL;DR: Autoregressive LLM generation has two phases with different bottlenecks: prefill processes input positions in parallel and is usually compute-intensive, while decode produces tokens sequentially and is often limited by memory bandwidth. Long contexts also grow the KV cache, consuming memory that could otherwise support more concurrent requests. In a multi-turn chat, repeatedly sending the full history makes the number of input tokens grow quadratically with the number of similarly sized turns; dense-attention arithmetic is a separate measure and grows faster still. Amazon Bedrock prompt caching does not make those costs disappear or change their Big-O by fiat: it reuses matching prefix state and charges cache reads at a reduced rate, with AWS reporting up to 90% lower input-token cost and up to 85% lower latency for supported models and workloads.

During some time off in Austria, three of us sat at a marble café table and lost most of an afternoon to a single question: why does an AI token cost what it costs? Two of us have worked deeply on these systems, and the Melange kept coming while we argued over napkins about memory bandwidth, KV caches, and what the machine is doing when it “prefills” a prompt. I walked out with the same feeling I get from the best hallway conversations: the answer is not exotic. It comes down to two things every computer science graduate already carries around. Memory bandwidth, and Big-O.

By Stefan Christoph

Laws & Disorder #1 — Conway's Law: Your Org Chart Is Your Real Architecture (and AI Is Redrawing Both)

🎬 Also available as a blog walkthrough video — a short narrated screencast of this post.

TL;DR: Conway’s Law says a system’s structure ends up mirroring the communication structure of the organization that built it. On AWS your service and account boundaries drift toward your team boundaries whether you planned it or not, so the Inverse Conway Maneuver is real power: shape the teams first to get the architecture you want. In my own years holding an architecture together across many services and many teams, the existing team structure was almost always the hard wall I hit when I tried to change the overall design. The new twist, as of 2026: AI coding assistants lower the coordination cost between teams, the exact cost Conway’s Law runs on, and fleets of AI agents are starting to show up on the org chart too. Whether that loosens the org-to-architecture mapping or just replicates the old one faster is the open question worth sitting with.

This is the first post in “Laws & Disorder,” a short series where I take a software-engineering “law” we all half-quote, admit it’s a heuristic and not a law of physics, and ask what it means for building on the cloud in the age of AI. First up: the one that keeps quietly winning arguments in architecture reviews.

By Stefan Christoph

Post-Quantum Crypto Is Here — What It Means on AWS

TL;DR: Post-quantum cryptography stopped being a research-paper topic. The reason to care today is “harvest-now, decrypt-later”: an adversary records your encrypted traffic now and decrypts it once a large quantum computer exists, so anything with a long secrecy lifetime is already exposed. In August 2024 NIST published the first finalized standards (FIPS 203 ML-KEM, FIPS 204 ML-DSA, FIPS 205 SLH-DSA), which turned “someday” into a concrete plan. On AWS the practical news is that AWS KMS already supports hybrid post-quantum TLS (ML-KEM) for connections and ML-DSA (FIPS 204) post-quantum signatures, and data at rest under KMS already uses AES-256-GCM, which is quantum-resistant. The action that ages well is not a rushed migration; it is crypto-agility: know where your long-lived secrets are, and be able to swap algorithms without re-architecting.

Disclaimer: I’m a solutions architect, and this is an explainer plus field guidance, not authoritative security advice. Every “AWS supports X” claim links to official documentation; verify currency there, because service support moves quickly. Confirm the specifics for your own runtime, region, and compliance needs before you rely on them.

This is Part 3 of the “Whiteboard to Cloud” series, where I take an academic explainer and trace it to what you can actually do on AWS. The earlier parts covered vector search and why compression behaves like intelligence. This one starts at a whiteboard about quantum computers and ends at a KMS key spec.

By Stefan Christoph

About

Recent Blog Posts

Why AI Tokens Are So Expensive — and What Actually Makes Them Cheaper

Laws & Disorder #1 — Conway's Law: Your Org Chart Is Your Real Architecture (and AI Is Redrawing Both)

Post-Quantum Crypto Is Here — What It Means on AWS

More

How My Setup Learns While You Sleep

Compression Is Intelligence

Your Obsidian Vault Is a Knowledge Graph

Vector Search, From the Whiteboard to the Cloud