Model Serving for Any Model Across Any Infrastructure

Model serving is the production-ready deployment of trained machine learning, LLM, and GenAI models, making them accessible via REST or gRPC APIs. It involves packaging models with dependencies and optimizing them for low-latency inference. This process ensures that models are hosted securely to provide real-time or batch predictions for enterprise applications.

The model serving process starts by loading a trained model from a registry into a container. It is then wrapped in an API, such as FastAPI, allowing applications to send data for inference. The infrastructure scales automatically based on traffic demand to maintain high availability in live environments.

Inference is the specific execution of a model to calculate an output. Model serving is the broader orchestration layer that provides the necessary infrastructure, such as GPUs, to host that logic. Serving manages the interface, setup, and maintenance costs required to bridge the gap between development and production.

There are two main types of model serving, online and batch. Online serving delivers immediate, low-latency predictions for interactive applications like chatbots. Batch serving processes large volumes of data periodically for non-time-sensitive tasks. TrueFoundry supports both methods, along with streaming inference, providing visibility across deployment, usage, and system health for all high-scale workflows.

TrueFoundry offers a unified, cloud-agnostic model serving platform that runs across AWS, GCP, Azure, or on-prem. It simplifies the lifecycle of traditional ML and LLMs by providing built-in cost optimization, full observability, and fine-grained access control. This developer-first design reduces infrastructure overhead while ensuring production-grade reliability.