Model Serving Concepts v1.2

Model Serving is a core capability of EDB Postgres® AI (EDB PG AI), enabling scalable, flexible, and high-performance serving of AI/ML models on Kubernetes.

It powers:

• Gen AI applications
• Intelligent retrieval systems
• Advanced data pipelines

Model Serving is implemented using KServe, an open-source Kubernetes-native engine for standardized model inferencing. AI Factory integrates KServe with Hybrid Manager to provide enterprise-grade lifecycle management, security, and observability.

Key to Sovereign AI: Models run on your Kubernetes clusters, under your control, with full observability and governance.

Before you start

Prerequisites for understanding Model Serving:

• Familiarity with Kubernetes basics (pods, services, deployments)
• Understanding of InferenceService as a Kubernetes CRD
• Awareness of Model Library and how models are registered and deployed
• Understanding of Sovereign AI principles in EDB PG AI

Suggested path:

• AI Factory Concepts
• Model Library Explained

Why it matters

Model Serving enables your AI models and Postgres data to work together seamlessly — securely and scalably — under Sovereign AI principles:

• Deploy open-source or commercial models to your Kubernetes cluster.
• Serve Gen AI models for Assistants, Knowledge Bases, and RAG pipelines.
• Support multi-modal retrieval (text, image, hybrid search).
• Optimize performance with GPU acceleration and server-side batching.
• Maintain full observability, auditing, and governance over model usage.

See also: Hybrid Manager Model Serving integration in production environments.

Core concepts

InferenceService (via KServe)

At the core of Model Serving is the InferenceService — a Kubernetes-native resource that represents a deployed model.

It defines the end-to-end serving pipeline:

• Predictor — Runs the model server and handles inference.
• Transformer (optional) — Applies pre-processing or post-processing.
• Explainer (optional) — Provides model explainability outputs.

Predictor

The Predictor defines:

• Model format — PyTorch, TensorFlow, ONNX, Triton, etc.
• Model location — S3-compatible storage, OCI registry, PVC.
• Resources — CPU, memory, GPU.
• Autoscaling — Policies for elastic scaling, including scale-to-zero.

ServingRuntime / ClusterServingRuntime

Reusable runtime definitions for serving:

• ServingRuntime — Namespace-scoped.
• ClusterServingRuntime — Cluster-wide reusable runtimes.

Benefits:

• Tailor runtime settings to model type and hardware.
• Standardize runtime configurations across teams and projects.

How it works

Lifecycle flow

1. Register model image in Model Library.
2. Deploy model via Model Serving console or CLI → creates InferenceService.
3. AI Factory + KServe provision Kubernetes resources (pods, services).
4. Model is loaded into runtime container.
5. Kubernetes service endpoint is exposed.
6. Clients send HTTP/gRPC inference requests.
7. Requests may pass through Transformers and Explainers.
8. Inference response is returned.

Key features

• Multi-framework support — PyTorch, TensorFlow, ONNX, XGBoost, Triton, and more.
• GPU acceleration — Native NVIDIA GPU support.
• Autoscaling — Including scale-to-zero via Knative.
• Observability — Prometheus metrics, Kubernetes logging, and Hybrid Manager dashboards.
• Batching — Server-side batching for improved throughput.
• Explainability — Support for model explainability tooling.
• Security and Sovereign AI — Models run on your Kubernetes clusters under your control.

Patterns of use

Gen AI Builder

• Serve LLMs and multi-modal models powering Assistants and Agents.

Knowledge Bases

• Serve embedding and retrieval models used in:
• Knowledge Base indexing
• RAG pipelines

Custom applications

• Expose InferenceService endpoints to:
• Business applications
• Microservices
• ETL/ELT pipelines

Hybrid + Sovereign AI alignment

• All models run on your infrastructure via Hybrid Manager KServe layer.
• You control:
• Which models are deployed
• Resource allocations
• Deployment topology
• Observability and auditing

Best practices

• Always deploy models through the Model Library → Model Serving flow to ensure governance.
• Monitor resource consumption — especially GPU utilization.
• Test scale-to-zero policies carefully before production use.
• Use ServingRuntime and ClusterServingRuntime templates for consistency.
• Tag and document production models clearly in Model Library.
• Audit InferenceService deployments regularly — critical for Sovereign AI.

In AI Factory

Model Serving powers multiple components in EDB PG AI:

AI Factory manages Model Serving through:

• Integrated Model Library for image management.
• GPU resource management and scheduling.
• Centralized observability and logging.
• Seamless Hybrid Manager integration for lifecycle control.

Related topics

• Model Library Explained
• Deploy AI Models
• Deploy an InferenceService
• Verify Model Deployments
• KServe Official Docs
• KServe GitHub Repository

Next steps

• Explore available models in your Model Library.
• Deploy your first model using Model Serving.
• Monitor deployed models through Hybrid Manager observability dashboards.
• Explore advanced Model Serving capabilities:
• Multi-modal pipelines
• Transformers and Explainers
• Scale-to-zero policies

Model Serving gives you a powerful foundation for building intelligent, governed AI applications — securely and scalably — as part of your Sovereign AI strategy with EDB PG AI.