Generative AI on SAP BTP: The Architecture Choices You Need to Understand

2. Generative AI Hub: Not Just an LLM Gateway

The Generative AI Hub is SAP’s control plane for Generative AI. It’s easy to think of it as “just an API to ChatGPT,” but it’s so much more.

Here’s what it actually provides:

Multi-Model Access

• Foundation models from SAP-hosted infrastructure
• Models from partners: Microsoft Azure, Google, AWS
• Standardized APIs across all providers

What This Solves

• Vendor independence – No hard lock-in to a single provider
• Model flexibility – Choose the right model for each use case
• Central orchestration – Manage all AI use cases from one place

Advanced Capabilities

• Prompt Registry – Manage prompt lifecycles centrally
• Prompt Optimization – Refine prompts against target datasets
• Enterprise controls – Governance around AI usage

The Generative AI Hub isn’t just about accessing models. It’s about controlling how AI works in your organization.

3. Orchestration: Where Enterprise AI Becomes Safe AI

This is the part many people overlook — and it’s arguably the most important.

Orchestration is what makes SAP’s AI suitable for regulated environments like finance, HR, and supply chain.

Here’s what Orchestration brings to the table:

Grounding

Integrates external, contextually relevant, domain-specific, or real-time data into AI processes. Your AI doesn’t just generate text — it answers with your business data backing it up.

Templating

Compose prompts with placeholders that get filled during inference. This ensures consistency at scale.

Translation

Automatically translate LLM prompts into chosen target languages. Build global-ready AI workflows from day one.

Data Masking

Anonymize or pseudonymize data before it’s processed by the AI model. In cases of pseudonymization, masked data in the response gets unmasked automatically.

Content Filtering

Restrict the type of content passed to and from the AI model. Responsible AI by default.

The Pipeline Approach

Different orchestration modules combine into a pipeline executed with a single API call. The response from one module becomes input for the next, with execution order centrally defined.

Bottom line: This is why SAP AI works in environments where compliance, privacy, and auditability aren’t optional.

4. HANA Cloud: Turning AI from “Smart” into “Relevant”

LLMs are powerful — but they’re stateless. They don’t remember your business data. They can’t reason about relationships in your organization.

That’s where SAP HANA Cloud comes in, with two game-changing engines:

Vector Engine: Giving AI Memory

The Vector Engine manages unstructured data (text, images) as high-dimensional embeddings.

What this enables:

• Semantic search – Find documents by meaning, not just keywords
• Similarity matching – “Show me contracts similar to this one”
• Context-aware responses – AI that understands your business context
• Retrieval Augmented Generation (RAG) – Combine LLMs with your private business data

Key benefit: Multi-model database architecture. Vector storage sits alongside relational, graph, spatial, and JSON data — all in one place.

Knowledge Graph Engine: Giving AI Reasoning

The Knowledge Graph Engine handles semantically connected relationships.

What it supports:

• RDF and SPARQL – Expose relational data as knowledge graphs
• SQL and SPARQL interoperability – Complex queries that leverage both
• Logical inference – AI that can reason about your business

What this enables:

• Improved decision-making with logical formality
• Interconnected corporate knowledge that powers AI applications
• Enhanced data understanding for complex business queries

Together, Vector Engine + Knowledge Graph = Enterprise AI that’s both smart AND relevant.

5. Multiple Runtimes: Flexibility, Not Confusion

Why does SAP support both Cloud Foundry and Kyma?

Because different AI workloads have different needs.

Cloud Foundry Runtime

• Best for: Traditional CAP services
• Polyglot application development
• Proven, stable platform

Kyma Runtime

• Best for: Event-driven extensions
• Containerized microservices
• Kubernetes-native scalability
• Serverless functions

The principle: You deploy AI where it fits best — not where the platform forces you.

This architectural flexibility means you can start with Cloud Foundry and evolve to Kyma as your needs grow, or run both simultaneously.

Common AI Patterns Enabled by This Architecture

The reference architecture isn’t just components — it enables real AI patterns:

Basic Prompting

Fundamentals of interacting with foundation models through the Generative AI Hub.

Semantic Search & Embeddings

Context-aware, meaning-based search using HANA Cloud’s Vector Engine.

Retrieval Augmented Generation (RAG)

Ground AI responses with actual documents and business data.

AI Agents

Autonomous, adaptive execution of complex enterprise processes.

Agent-to-Agent Interoperability (A2A)

Multiple agents collaborating using the A2A protocol.

Agents for Structured Data

Natural language queries into enterprise data for analytics.

Multi-Tenancy

Support multiple customers/tenants with data isolation and security.

The Real Takeaway

SAP is not building AI demos. SAP is building AI infrastructure for real business systems.

This architecture ensures:

• AI respects authorization and access controls
• AI understands business context and semantics
• AI scales across tenants securely
• AI remains explainable and auditable
• AI integrates with existing SAP investments

That’s why Generative AI on SAP BTP looks the way it does.

Final Thoughts

When you first see this architecture, it looks complex. But each component solves a specific enterprise challenge that you’d otherwise have to build yourself:

• How do I access multiple AI models without vendor lock-in? → Generative AI Hub
• How do I make AI understand my business data? → Vector Engine
• How do I ensure AI is safe and compliant? → Orchestration
• How do I integrate AI with existing processes? → CAP
• How do I reason over business relationships? → Knowledge Graph Engine

This isn’t complexity for its own sake. It’s the difference between a proof-of-concept and a production system.