With the increasing adoption of AI in SAP S/4HANA, SuccessFactors, and the supply chain, companies are finding it challenging to move from isolated machine learning initiatives to production-grade AI. This is because unconnected runtimes, unmanaged model versions, and the absence of monitoring capabilities can result in chaos. What was once innovation turns out to be unconnected AI deployments that are difficult to scale and manage. 

SAP BTP AI Core and AI Launchpad form a structured platform layer in the SAP Business Technology Platform. SAP AI Core offers a Kubernetes-based runtime environment for containerized model training and inference, and SAP AI Launchpad offers centralized lifecycle management, monitoring, and artifact management.  

In this blog, we will explore how SAP BTP AI Core and AI Launchpad serve as the enterprise platform layer for AI, how they are structured and governed in the SAP Business Technology Platform, and how companies can scale AI operations with a structured KaarTech-led approach.

 

Why SAP BTP AI Core & AI Launchpad Are Critical for Enterprise AI 

As enterprises begin to scale AI beyond proof of concept, the complexity of management escalates. AI is no longer a domain of data science teams working in silos; it is now integrated across ERP, supply chain, HR, and customer platforms. This brings about new risks of AI management, such as model drift, uncontrolled deployment, infrastructure complexity, and lack of auditability. 

This transition is also reflected in industry research. Gartner’s research on AI engineering and MLOps states that enterprises need to industrialize AI delivery through standardized platforms that deliver lifecycle governance, reproducibility, and monitoring. Without a governed platform layer, AI projects remain stuck at proof of concept. 

In SAP landscapes, the challenge is further aggravated by the adoption of cloud and clean core strategies. The AI models need to be integrated with S/4HANA and other SAP applications without impacting the digital core. SAP BTP AI Core and AI Launchpad solve this problem by decoupling execution and governance. The two solutions together provide secure, flexible, and cloud-standard-compliant enterprise-grade AI.

 

AI Core for Model Training and Deployment 

SAP AI Core serves as the managed execution environment for machine learning workloads in SAP BTP. It abstracts infrastructure complexity and offers a standardized execution environment for deploying AI artifacts across enterprise landscapes. 

Fundamentally, AI Core is developed on a Kubernetes-based infrastructure that allows for controlled resource allocation and workload isolation. This ensures that model training and inference tasks run in isolation but can still be managed centrally. 

Technically, AI Core supports: 

• Workflow-Based Execution Templates: Standardized training and inference workflows for controlled, repeatable AI execution. 

• Declarative YAML Deployment Configurations: Infrastructure-as-code capabilities for standardized, version-controlled model deployment processes. 

• Predefined Execution Environments: Offer containerized runtime environments with dependencies for Python, TensorFlow, PyTorch, and custom frameworks. 

• Elastic Compute Orchestration: Dynamically provision CPU and GPU compute capacity with Kubernetes-managed workload scaling. 

• Secure REST Inference Endpoints: Enable deployed models as secured APIs for real-time application integration. 

• Controlled Data Connectivity: Provide secure access to SAP and external data sources without exposing core systems. 

This architecture allows data science teams to deploy models as managed workloads instead of scripts. By decoupling runtime execution from application layers, AI Core enables organizations to scale AI efforts without integrating models with core SAP systems. 

 

AI Launchpad for Governance and Monitoring 

SAP AI Launchpad is the operational control plane for managing AI workloads deployed using AI Core. While AI Core is responsible for execution, AI Launchpad offers a centralized view for models, scenarios, and runtime instances in the SAP BTP environment.  

It offers the ability to have structured visibility over the entire AI lifecycle, ensuring that models stay traceable, measurable, and compliant in an enterprise setting.  

The technical capabilities are:  

• Centralized Model Registry: Logical grouping of scenarios, executables, configurations, and deployments across multiple AI Core instances.  

• Execution Monitoring: Real-time visibility into job status, runtime logs, and execution history.  

• Metrics and Performance Tracking: Monitoring inference endpoints and training jobs to assess operational fitness.  

• Role-Based Access Control (RBAC): Fine-grained authorization integrated with SAP BTP security services.  

• Instance Management: Governance of multiple AI Core runtimes across landscapes (dev, test, prod).  

• Auditability: Traceability of deployment actions and configuration changes.  

With the operational control plane integrated into a single point, AI Launchpad ensures that AI efforts stay manageable at scale. It turns fragmented model deployments into governed, enterprise-monitored resources instead of siloed technical workloads. 

 

Architecture of SAP BTP AI Core & AI Launchpad on SAP BTP 

The SAP AI Core and SAP AI Launchpad architecture on SAP Business Technology Platform (SAP BTP) is designed to industrialize AI operations by applying a systematic separation of execution, orchestration, and governance. Instead of hardwiring machine learning logic into application tiers, SAP envisions AI as a platform service that runs in parallel and is securely integrated with SAP systems.  

At the base layer is SAP AI Core, which functions as a managed Kubernetes runtime environment. AI workloads are encapsulated as containerized executables that are launched into separate resource groups within a tenant. These resource groups establish compute domains, govern quotas, and provide workload isolation across dev, test, and production topologies. Execution is driven through standardized AI API endpoints, which manage training jobs, inference services, and batch processing pipelines. Since the runtime is based on Kubernetes orchestration, SAP AI Core can dynamically provision CPU and GPU-backed infrastructure, scale workloads, and manage job lifecycles without human intervention for infrastructure management. 

On top of this execution layer is the SAP AI Launchpad, acting as the control and governance point. The Launchpad is connected to one or more instances of AI Core and offers a centralized view of scenarios, configurations, deployments, and executions. The Launchpad supports structured artifact management, versioning of models, and tracking the status of executions across environments. With the integration of SAP BTP Identity Authentication and Authorization services, the Launchpad supports role-based access control (RBAC) to ensure that data scientists, developers, and operations personnel collaborate securely on AI-related tasks.  

The layered architecture approach, with runtime execution in AI Core and lifecycle governance in AI Launchpad, ensures that AI workloads are always scalable, traceable, and enterprise ready. This approach is consistent with SAP’s overall cloud-native and clean core vision of keeping AI logic outside the digital core but still securely connected via APIs to SAP applications and data services. 

 

KaarTech as Your Strategic Partner for SAP BTP AI Core & AI Launchpad 

A global manufacturing business had found it difficult to transition their predictive maintenance models from pilots to production. The data scientists had developed accurate models for forecasting, but the deployment issues, inconsistencies in runtime environments, and lack of monitoring capabilities had hindered their adoption at an enterprise level. It was not the lack of AI capability that the business was facing but the need for a governed platform layer to securely and scalable leverage it. 

This is where KaarTech comes into play. With extensive knowledge of SAP BTP, SAP AI Core, and AI Launchpad, KaarTech assists businesses in moving from disintegrated AI projects to organized and production-ready AI platforms. Our process starts with data readiness and AI maturity assessments and then moves on to designing a Kubernetes-aligned runtime environment in SAP AI Core. We set up governed by deployment pipelines, define resource groups, and provide secure model sharing through1 API-based integrations. 

On the governance front, we deliver AI Launchpad frameworks that provide lifecycle visibility, role-based access control, and operational monitoring that are aligned with enterprise security policies. Most importantly, we ensure that AI systems remain clean core compliant, with intelligence decoupled from core SAP systems but fully integrated.  

If you’re ready to scale AI with structure and governance using SAP BTP AI Core and AI Launchpad, connect with KaarTech and let’s get started. 

 

 

FAQ’s

1. What is SAP AI Core?

SAP AI Core is a managed runtime service on SAP BTP that enables organizations to train, deploy, and scale machine learning models using containerized workloads and Kubernetes-based orchestration.

2. What is SAP AI Launchpad?

SAP AI Launchpad is a centralized governance interface that allows enterprises tomonitor, manage, and control AI Core workloads, model lifecycles, scenarios, artifacts, and execution environments securely. 

3. Can SAP AI Core integrate with S/4HANA?

Yes. Models deployed through SAP AI Core can be integrated with S/4HANA and other SAP applications using secure APIs and SAP BTP connectivity services. 

4. Is SAP AI Core cloud-native?

Yes. SAP AI Core is built on Kubernetes and delivered as a cloud-native service withinSAP Business Technology Platform, supporting scalable, containerized, and infrastructure-abstracted AI workloads.