Course Objective & Scope
Organisations adopting generative AI face a pivotal dilemma: leverage the speed and frontier capability of cloud LLM APIs, or keep full data residency with self-hosted open-weight models? This course replaces rules of thumb with rigorous analysis across data classification, regulation, latency and real total cost of ownership.
Direct provider APIs, managed enterprise cloud platforms (Azure AI Foundry, Amazon Bedrock, Vertex AI), self-hosted open-weight serving (Llama, Qwen, Mistral on vLLM) and hybrid LLM gateway architectures are compared side by side. Participants produce a model hosting policy by data tier and a TCO break-even model for their organisation.
Who Should Attend?
- CIOs, CTOs, CISOs and enterprise architecture leaders
- Infrastructure, cloud platform and AI engineering managers
- Information security, data privacy (KVKK/GDPR) and compliance officers
- Decision-makers in regulated sectors such as banking, insurance, healthcare, defence and public sector
Key Takeaways
- Distinguish public cloud APIs, managed enterprise cloud, private endpoints/VPC and on-premise deployments
- Map KVKK, sector data-residency rules and EU AI Act obligations to architectural options
- Calculate the break-even point between token-based API pricing and dedicated GPU infrastructure plus MLOps overhead
- Evaluate open-weight models (Llama, Qwen, Mistral) on quality, latency and operational burden
- Design a hybrid LLM gateway routing policy based on data sensitivity and task complexity
Curriculum
The Spectrum of Deployment Options
- Direct frontier provider APIs (OpenAI, Anthropic, Google) and enterprise tiers
- Managed cloud platforms: Azure AI Foundry, Amazon Bedrock, Vertex AI (Private Link, region residency)
- Self-hosted open-weight models (Llama, Qwen, Mistral, Gemma) on on-prem or sovereign cloud GPUs
Data Security, Privacy and Regulatory Analysis
- KVKK cross-border transfer rules, banking/financial regulations and data sovereignty constraints
- Zero-data-retention (ZDR), customer-managed keys (CMK) and no-training guarantees
- PII redaction, data masking and hybrid RAG patterns keeping sensitive data on-premise
Cost and Break-Even Modelling
- Input/output token pricing, prompt caching, batch APIs and provisioned throughput
- GPU CAPEX vs. cloud GPU OPEX, power, cooling and 24/7 MLOps engineering overhead
- Break-even analysis by traffic volume, concurrency and context length
Performance, Quality and Operational Complexity
- Reasoning and Turkish-language performance: frontier APIs vs. small/medium open models
- Basics of quantization, vLLM/TGI inference optimisation and hardware sizing
- Comparing model upgrades, security patching and SLA ownership
Hybrid Architecture and Enterprise Policy Workshop
- Dynamic model routing via an LLM Gateway based on sensitivity and task complexity
- Building an approved model matrix across data classification tiers
- Architecture and cost decision simulation on the organisation's real scenarios
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