Model Serving
The infrastructure layer that exposes a trained AI model as a network endpoint, handling request batching, load balancing, autoscaling, observability, and version management.
How it works
Model serving is to AI what application servers are to web apps: the production substrate. Common serving frameworks include vLLM (high-throughput LLM inference with paged attention), TensorRT-LLM (NVIDIA-optimised inference), Triton Inference Server, BentoML, and SageMaker / Vertex / Azure ML for managed deployments. Production model serving handles concurrent requests, batches them for GPU efficiency, surfaces metrics for observability, supports rolling model upgrades, and integrates with the firm's existing authentication and authorisation. For UK enterprise deployment, model serving infrastructure is part of the system Ayoob AI ships and operates inside the client's tenancy alongside the application code.
Related terms
AI Inference
The process of running a trained AI model on input data to produce an output, distinguished from training (which produces the model) and fine-tuning (which adapts it).
Private AI
AI deployed on infrastructure the client controls (on-premise, in the client's cloud tenancy, or air-gapped), with no third-party LLM provider in the data path and no inference-time data export.
On-Premise AI
AI deployed on hardware the client owns and operates inside their own data centre or office facility, with no dependency on external cloud or model providers for inference.
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