Daniel Akhabue
Daniel is an AI/ML Engineer and Cloud Solutions Architect with hands-on experience in building AI-driven solutions across diverse domains, including data science, machine learning and generative AI. He has led and contributed to impactful projects across a range of industries such as EdTech, Assistive Technology, HealthTech, FinTech, and Supply Chain Technology, consistently delivering solutions that address real-world challenges.
As a community champion at Data Scientists Network (DSN), Daniel has won multiple Data Science and AI hackathons, and has led AI communities in Nigeria, driving innovation and knowledge sharing. He is also a technical writer, sharing insights and expertise through articles published on leading online platforms in the Data Science and AI space.
In his spare time, Daniel enjoys reading and reviewing research papers, as well as playing chess. He is driven by a deep passion to build solutions that create meaningful societal impact and foster socio-economic development
Session
AI systems face six dimensions of scale: data, model, user, operational, infrastructure, and cost. While most talks focus on infrastructure and user scale, this session tackles the hardest dimension: Cost Scale, maintaining predictable and optimized compute costs as usage grows. Drawing from production experience, I'll demonstrate how I achieved 70% cost reduction through three architectural patterns: semantic caching that eliminates repeat LLM calls, model cascading using hybrid architecture (open-source SLMs + API-based LLMs), and optimized conversation state management.
You'll see real production metrics, honest failure post-mortems, and the critical trade-offs between batch vs. real-time inference, monolithic vs. microservices, and open-source vs. API models. Learn how architectural choices directly impact unit economics, and why scaling AI is fundamentally different from scaling traditional software.
Walk away with reusable Python patterns (FastAPI, Pydantic, Redis, DynamoDB) and decision frameworks for building economically sustainable agentic AI systems.