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sap-ai-core Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs. GPL-3.0
version last_verified production_tested
1.1.0 2025-11-27 Yes, examples verified against SAP documentation

SAP AI Core & AI Launchpad Skill

Related Skills

  • sap-btp-cloud-platform: Use for platform context, BTP account setup, and service integration
  • sap-cap-capire: Use for building AI-powered applications with CAP or integrating AI services
  • sap-cloud-sdk-ai: Use for SDK integration, AI service calls, and Java/JavaScript implementations
  • sap-btp-best-practices: Use for production deployment patterns and AI governance guidelines

Table of Contents

  1. Overview
  2. Quick Start
  3. Service Plans
  4. Model Providers
  5. Orchestration
  6. Content Filtering
  7. Data Masking
  8. Grounding (RAG)
  9. Tool Calling
  10. Structured Output
  11. Embeddings
  12. ML Training
  13. Deployments
  14. SAP AI Launchpad
  15. API Reference
  16. Common Patterns
  17. Troubleshooting
  18. References

Overview

SAP AI Core is a service on SAP Business Technology Platform (BTP) that manages AI asset execution in a standardized, scalable, hyperscaler-agnostic manner. SAP AI Launchpad provides the management UI for AI runtimes including the Generative AI Hub.

Core Capabilities

Capability Description
Generative AI Hub Access to LLMs from multiple providers with unified API
Orchestration Modular pipeline for templating, filtering, grounding, masking
ML Training Argo Workflows-based batch pipelines for model training
Inference Serving Deploy models as HTTPS endpoints for predictions
Grounding/RAG Vector database integration for contextual AI

Three Components

  1. SAP AI Core: Execution engine for AI workflows and model serving
  2. SAP AI Launchpad: Management UI for AI runtimes and GenAI Hub
  3. AI API: Standardized lifecycle management across runtimes

Quick Start

Prerequisites

  • SAP BTP enterprise account
  • SAP AI Core service instance (Extended plan for GenAI)
  • Service key with credentials

1. Get Authentication Token

# Set environment variables from service key
export AI_API_URL="<your-ai-api-url>"
export AUTH_URL="<your-auth-url>"
export CLIENT_ID="<your-client-id>"
export CLIENT_SECRET="<your-client-secret>"

# Get OAuth token
AUTH_TOKEN=$(curl -s -X POST "$AUTH_URL/oauth/token" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials&client_id=$CLIENT_ID&client_secret=$CLIENT_SECRET" \
  | jq -r '.access_token')

2. Create Orchestration Deployment

# Check for existing orchestration deployment
curl -X GET "$AI_API_URL/v2/lm/deployments" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json"

# Create orchestration deployment if needed
curl -X POST "$AI_API_URL/v2/lm/deployments" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json" \
  -d '{
    "configurationId": "<orchestration-config-id>"
  }'

3. Use Harmonized API for Model Inference

ORCHESTRATION_URL="<deployment-url>"

curl -X POST "$ORCHESTRATION_URL/v2/completion" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "module_configurations": {
        "llm_module_config": {
          "model_name": "gpt-4o",
          "model_version": "latest",
          "model_params": {
            "max_tokens": 1000,
            "temperature": 0.7
          }
        },
        "templating_module_config": {
          "template": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "{{?user_query}}"}
          ]
        }
      }
    },
    "input_params": {
      "user_query": "What is SAP AI Core?"
    }
  }'

Service Plans

Plan Cost GenAI Hub Support Resource Groups
Free Free No Community only Default only
Standard Per resource + baseline No Full SLA Multiple
Extended Per resource + tokens Yes Full SLA Multiple

Key Restrictions:

  • Free and Standard mutually exclusive in same subaccount
  • Free → Standard upgrade possible; downgrade not supported
  • Max 50 resource groups per tenant

Model Providers

SAP AI Core provides access to models from six providers:

  • Azure OpenAI: GPT-4o, GPT-4 Turbo, GPT-3.5
  • SAP Open Source: Llama, Falcon, Mistral variants
  • Google Vertex AI: Gemini Pro, PaLM 2
  • AWS Bedrock: Claude, Amazon Titan
  • Mistral AI: Mistral Large, Medium, Small
  • IBM: Granite models

For detailed provider configurations and model lists, see references/model-providers.md.

Orchestration

The orchestration service provides unified access to multiple models through a modular pipeline with 8 execution stages:

  1. Grounding → 2. Templating (mandatory) → 3. Input Translation → 4. Data Masking → 5. Input Filtering → 6. Model Configuration (mandatory) → 7. Output Filtering → 8. Output Translation

For complete orchestration module configurations, examples, and advanced patterns, see references/orchestration-modules.md.

Content Filtering

Azure Content Safety: Filters content across 4 categories (Hate, Violence, Sexual, SelfHarm) with severity levels 0-6. Azure OpenAI blocks severity 4+ automatically. Additional features include PromptShield and Protected Material detection.

Llama Guard 3: Covers 14 categories including violent crimes, privacy violations, and code interpreter abuse.

Data Masking

Two PII protection methods:

  • Anonymization: MASKED_ENTITY (non-reversible)
  • Pseudonymization: MASKED_ENTITY_ID (reversible)

Supported entities (25 total): Personal data, IDs, financial information, SAP-specific IDs, and sensitive attributes. For complete entity list and implementation details, see references/orchestration-modules.md.

Grounding (RAG)

Integrate external data from SharePoint, S3, SFTP, SAP Build Work Zone, and DMS. Supports PDF, HTML, DOCX, images, and more. Limit: 2,000 documents per pipeline with daily refresh. For detailed setup, see references/grounding-rag.md.

Tool Calling

Enable LLMs to execute functions through a 5-step workflow: define tools → receive tool_calls → execute functions → return results → LLM incorporates responses. Templates available in templates/tool-definition.json.

Structured Output

Force model responses to match JSON schemas using strict validation. Useful for structured data extraction and API responses.

Embeddings

Generate semantic embeddings for RAG and similarity search via /v2/embeddings endpoint. Supports document, query, and text input types.

ML Training

Uses Argo Workflows for training pipelines. Key requirements: create default object store secret, define workflow template, create configuration with parameters, and execute training. For complete workflow patterns, see references/ml-operations.md.

Deployments

Deploy models via two-step process: create configuration (with model binding), then create deployment with TTL. Statuses: Pending → Running → Stopping → Stopped/Dead. Templates in templates/deployment-config.json.

SAP AI Launchpad

Web-based UI with 4 key applications:

  • Workspaces: Manage connections and resource groups
  • ML Operations: Train, deploy, monitor models
  • Generative AI Hub: Prompt experimentation and orchestration
  • Functions Explorer: Explore available AI functions

Required roles include genai_manager, genai_experimenter, prompt_manager, orchestration_executor, and mloperations_editor. For complete guide, see references/ai-launchpad-guide.md.

API Reference

Core Endpoints

Key endpoints: /v2/lm/scenarios, /v2/lm/configurations, /v2/lm/deployments, /v2/lm/executions, /lm/meta. For complete API reference with examples, see references/api-reference.md.

Common Patterns

Simple Chat: Basic model invocation with templating module RAG with Grounding: Combine vector search with LLM for context-aware responses Secure Enterprise Chat: Filtering + masking + grounding for PII protection Templates available in templates/orchestration-workflow.json. "masking_providers": [{

Troubleshooting

Common Issues:

  • 401 Unauthorized: Refresh OAuth token
  • 403 Forbidden: Check IAM roles, request quota increase
  • 404 Not Found: Verify AI-Resource-Group header
  • Deployment DEAD: Check deployment logs
  • Training failed: Create default object store secret

Request quota increases via support ticket (Component: CA-ML-AIC).

References

Reference Documentation:

  1. references/orchestration-modules.md - All orchestration modules in detail
  2. references/generative-ai-hub.md - Complete GenAI hub documentation
  3. references/model-providers.md - Model providers and configurations
  4. references/api-reference.md - Complete API endpoint reference
  5. references/grounding-rag.md - Grounding and RAG implementation
  6. references/ml-operations.md - ML operations and training
  7. references/advanced-features.md - Chat, applications, security, auditing
  8. references/ai-launchpad-guide.md - Complete SAP AI Launchpad UI guide

Templates:

  1. templates/deployment-config.json - Deployment configuration template
  2. templates/orchestration-workflow.json - Orchestration workflow template
  3. templates/tool-definition.json - Tool calling definition template

Official Sources:

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