---

name: sap-ai-core description: SAP AI Core — ML model deployment, AI workflows, AI API, model serving (vLLM, Triton), scenario management, execution scheduling, resource groups, artifact management, SAP AI Launchpad, integration with CAP/Fiori. Use when deploying ML models on SAP BTP, creating AI workflows, or integrating AI inference into SAP applications. trigger: keywords: [ai-core, ml, model, deployment, workflow, vllm, triton, btp, inference, launchpad] intent: >- Use when deploying, serving, or managing ML models on SAP AI Core (BTP), creating AI workflows, or integrating AI inference into SAP applications.

SAP AI Core

ML platform on SAP BTP — deploy, serve, and manage AI models at scale.

Architecture

SAP AI Core (BTP)
├── Resource Group (GPU/CPU quota)
├── Scenario → Workflow → Execution
├── Model → Deployment → Serving endpoint
└── Artifact storage (S3-compatible)

AI API

# Create deployment
curl -X POST https://api.ai.core.prod.<region>.aws.cloud.sap/v2/lm/deployments \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "deploymentId": "my-model",
    "modelId": "risk-predictor-v1",
    "scenarioId": "risk-analysis",
    "executableId": "serve-risk-model",
    "targetStatus": "RUNNING"
  }'

# Call inference
curl -X POST https://api.ai.core.prod.<region>.aws.cloud.sap/v2/inference/deployments/my-model/v1/predict \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "features": [[1.2, 3.4, 5.6, 7.8, 9.0, 1.1, 2.2, 3.3,
                   4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 1.0, 2.0,
                   3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.5]]
  }'

Scenario and Workflow

# scenario.yaml
apiVersion: ai.sap.com/v1alpha1
kind: Scenario
metadata:
  name: risk-analysis
spec:
  executables:
    - name: serve-risk-model
      image: docker.io/myorg/risk-predictor:latest
      ports: [{ port: 8080, protocol: HTTP }]
      resources:
        limits: { cpu: "1", memory: "4Gi", "nvidia.com/gpu": "1" }

CAP Integration

// CAP service calling AI Core inference
const cds = require('@sap/cds')
const axios = require('axios')

module.exports = class RiskService extends cds.ApplicationService {
  async init() {
    this.on('analyzeRisks', async (req) => {
      const risks = await cds.run(SELECT.from('Risks'))
      const features = risks.map(r => extractFeatures(r))
      const result = await axios.post(
        process.env.AI_CORE_ENDPOINT + '/v2/inference/deployments/risk-model/v1/predict',
        { features },
        { headers: { Authorization: `Bearer ${await getToken()}` } }
      )
      return result.data.predictions.map((p, i) => ({ ...risks[i], riskScore: p }))
    })
    await super.init()
  }
}

AI Launchpad

SAP AI Launchpad (Fiori): manage scenarios, deploy models, monitor executions, view inference logs.

Gotchas

• GPU quotas limited per subaccount — request increase through SAP
• Model size max: 6GB per deployment (standard plan)
• Inference timeout: 60s for sync, 10min for async (batch)
• AI Core is AWS-based in most regions (not CF-based like other BTP services)