SAP AI Core Features

Ensures that the service is reliable and available when needed, minimizing downtime and service interruptions.

Allows the user to scale the training of models efficiently, making it easier to deal with larger datasets and more complex models.

Provides the user the ability to get quick and low-latency responses during the inference stage, which is critical for real-time applications.

Provides the user with access to dedicated GPU or accelerated compute resources for training and inference, allowing capacity to scale with demand.

Offers the user a transparent pricing model for API calls, enabling better budget planning and cost control.

Provides the user the ability to allocate computational resources based on demand, making it cost-effective.

Allows the user to minimize energy usage during both training and inference, which is becoming increasingly important for sustainable operations.

Offers the user the flexibility to deploy across multiple cloud providers, reducing the risk of vendor lock-in.

Provides the user the ability to seamlessly connect with various data sources and pipelines, simplifying data ingestion and pre-processing.

Allows the user to easily integrate the generative AI models into existing workflows and systems via APIs.

Allows the user to deploy the infrastructure on-premises or in a hybrid configuration, supporting data residency and compliance requirements.

Helps the user maintain compliance with GDPR and other data protection regulations, which is crucial for businesses operating globally.

Allows the user to set up access controls based on roles within the organization, enhancing security.

Ensures that data is encrypted during transit and at rest, providing an additional layer of security.

Provides the user with comprehensive and clear documentation, aiding in quicker adoption and troubleshooting.

Allows the user to gauge the level of community support and third-party extensions available, which can be useful for problem-solving and extending functionality.

Allows the user to access and route between multiple third-party or open-source models rather than being limited to a single provider.

Allows the user to fine-tune or customize models on their own data for specific use cases.

Provides the user with access to a catalog of pre-trained, ready-to-deploy models to accelerate development.