As enterprise AI matures, developers and architects are constantly looking for the most efficient, secure, and scalable ways to connect Large Language Models (LLMs) to their business data.
If you are building low-code/no-code applications using SAP Build, you have likely run into two different schools of thought:
- The Native SAP Route: Leveraging SAP AI Core and SAP AI Launchpad (via the Generative AI Hub).
- The Open-Standard Route: Utilizing Model Context Protocol (MCP) servers to orchestrate AI tools and data access.
At first glance, these two approaches might seem to compete. You might ask: “If I already have a robust MCP server acting as a secure gatekeeper for my data, why do I need SAP AI Core at all?”
Let’s dive into the architectural realities of both, how they handle governance, and how to choose the right path for your SAP Build projects.
The Contenders: What Do They Actually Do?
1. SAP AI Core & Launchpad: The Enterprise AI Backbone
SAP AI Core is SAP’s native infrastructure engine for running, scaling, and managing AI models within the SAP Business Technology Platform (BTP). Together with SAP AI Launchpad (the administrative control center), it provides the Generative AI Hub—a secure gateway to top-tier foundation models (like GPT-4, Claude, and Gemini) pre-configured with enterprise-grade data privacy.
2. MCP (Model Context Protocol): The Open-Standard Gatekeeper
Developed as an open standard, MCP allows developers to build secure, modular servers that expose specific data sources and tools to LLMs. Instead of giving an LLM free rein over your backend, an MCP server acts as a strict API proxy. The LLM can only interact with the exact schemas, tools, and databases that your MCP server explicitly exposes.
The Governance Showdown: Two Paths to the Same Goal
Both architectures place a massive emphasis on governance and security, but they approach it from different angles:
| Control Point | Developer-Defined: You write the code to explicitly expose only the tools and data schemas the LLM is allowed to touch. | Platform-Defined: Built-in enterprise data masking, tenant isolation, and strict compliance policies (GDPR, SOC2). |
| Maintenance | Self-Managed: Your team is responsible for hosting, scaling, securing, and auditing the MCP server infrastructure. | SAP-Managed: SAP handles the infrastructure, security patching, scaling, and compliance certifications out of the box. |
| Integration | Custom APIs: Requires exposing the MCP server securely to the internet and configuring custom API destinations in SAP Build. | Native Destinations: Seamlessly integrates with SAP Build Apps and Process Automation via native BTP destinations and SSO. |
Why You Might Not Need SAP AI Core (The Case for MCP)
If you are a hands-on developer or part of an agile team with strong infrastructure capabilities, MCP servers are incredibly powerful.
- Absolute Granular Control: You act as the ultimate gatekeeper. The LLM cannot access a single byte of backend data unless your MCP server explicitly permits it.
- Flexibility: You aren’t locked into any specific cloud ecosystem. You can run your MCP servers locally, on AWS, Azure, or on-premise.
- Rapid Prototyping: For custom developer workflows, local testing, and highly specialized tool-calling, MCP offers an elegant, lightweight developer experience.
Why Enterprise IT Prefers SAP AI Core (The Case for Native)
While MCP is brilliant for custom control, large-scale enterprises often face hurdles when managing dozens of self-hosted MCP servers. This is where SAP AI Core shines:
- Zero-Trust Data Privacy: The Generative AI Hub guarantees that your business data is never used to train public LLMs. It automatically handles data masking and encryption without you having to write a single line of security code.
- Unified Monitoring (AI Launchpad): If five different departments are building SAP Build apps, how do you track token usage, costs, and latency? SAP AI Launchpad gives IT administrators a single dashboard to monitor and budget all AI consumption across the entire enterprise.
- No Infrastructure Overhead: You don’t have to worry about hosting, scaling GPUs, or maintaining uptime for your custom servers. SAP manages the entire lifecycle.
The Verdict: Which Should You Choose?
The choice between MCP and SAP AI Core isn’t necessarily an “either/or” decision—it depends on your organizational strategy:
- Choose MCP if you want maximum architectural flexibility, have the resources to host and secure your own middleware, and want absolute, code-level control over how your LLMs interact with your custom tools.
- Choose SAP AI Core if you are building production-grade applications in SAP Build that require instant compliance, native BTP security, zero-maintenance infrastructure, and centralized cost monitoring.
The Hybrid Future: In many modern architectures, these technologies can even work together. You can use SAP AI Core to securely access foundation models via the Generative AI Hub, while using your custom MCP servers to safely expose specific, non-SAP legacy databases to those models.
What is your take? Are you leaning toward open-standard MCP servers for your AI integrations, or does the out-of-the-box compliance of SAP AI Core make it the winner for your SAP Build projects? Let’s discuss in the comments below!
Read More Technology Blog Posts by Members articles
#abap