AI Model Deployment in 2026 — From Prototype to Production

Model serving: the core infrastructure

Serving an AI model means wrapping it in an API that handles inference requests. Key tools for different scenarios:

• vLLM — The standard for serving LLMs. Optimised for throughput with PagedAttention, continuous batching, and tensor parallelism. Handles multiple concurrent users efficiently. OpenAI-compatible API out of the box.
• Text Generation Inference (TGI) — Hugging Face's LLM serving solution. Similar capabilities to vLLM with strong HF ecosystem integration. Good documentation and Docker images.
• Triton Inference Server — NVIDIA's multi-framework serving platform. Supports PyTorch, TensorFlow, ONNX, TensorRT. Best for mixed workloads (LLMs + vision + audio). More complex but most flexible.
• BentoML — Framework-agnostic model serving with built-in packaging, versioning, and deployment. Good developer experience. Handles the full lifecycle from packaging to scaling.
• Simple Flask/FastAPI — For quick deployments of smaller models. Wrap your model in a Python web server. Fine for low-traffic internal tools; use dedicated serving frameworks for anything serious.

For LLM-specific deployment, also consider managed inference services — see our AI API providers guide for hosted options that eliminate infrastructure management entirely.

Containerisation and packaging

Reproducible deployment starts with proper packaging:

• Docker — Package your model, code, and dependencies into a container. This ensures the same environment everywhere: local testing, staging, production. Include model weights in the image or mount them from storage.
• ONNX — Open Neural Network Exchange format. Convert your model to ONNX for framework-independent deployment. ONNX Runtime provides optimised inference across hardware (CPU, GPU, mobile).
• TensorRT — NVIDIA's inference optimiser. Converts models to optimised GPU inference engines. 2-5x faster inference than running PyTorch directly. Worth the setup effort for high-throughput production.
• Model registries — Version and store model artefacts. MLflow Model Registry, Hugging Face Hub, or cloud-native options (SageMaker, Vertex AI). Essential for tracking which model version is deployed where.

Minimum viable deployment: Docker container with your model + a FastAPI server + a health check endpoint. You can deploy this to any container platform (Kubernetes, ECS, Cloud Run, Railway).

Scaling and performance

Handling production traffic efficiently:

• Batching — Process multiple requests together instead of one at a time. Dynamic batching (collecting requests over a short window) dramatically improves GPU utilisation. vLLM and TGI handle this automatically for LLMs.
• Auto-scaling — Scale replicas based on request volume or GPU utilisation. Kubernetes HPA, cloud auto-scaling groups, or serverless containers (AWS Lambda, Google Cloud Run). Scale to zero when idle to save costs.
• Caching — Cache responses for identical inputs. Surprisingly effective — many applications see 20-40% cache hit rates. Use Redis or Memcached. For embeddings, cache at the chunk level.
• Model optimisation — Quantisation (FP16, INT8, INT4) reduces memory and increases throughput with minimal quality loss. Distillation trains a smaller model to mimic a larger one. See our fine-tuning guide for techniques.
• GPU sharing — Multiple models on one GPU via NVIDIA MPS or time-slicing. Efficient when models are small or traffic is bursty.

Monitoring and observability

Production AI needs monitoring beyond standard application metrics:

• System metrics — GPU utilisation, memory usage, request latency (p50, p95, p99), throughput, error rate. Standard infrastructure monitoring (Prometheus, Datadog, CloudWatch).
• Model quality metrics — Track prediction accuracy, confidence distributions, and output quality on production data. A model can be running perfectly (low latency, no errors) while producing increasingly wrong outputs.
• Data drift detection — Production input data evolves over time. If it diverges significantly from training data, model accuracy degrades. Tools like Evidently AI and WhyLabs detect drift automatically.
• Logging and tracing — Log every prediction: input, output, latency, model version, confidence score. This enables debugging, quality auditing, and creating new training data from production examples.
• A/B testing — When deploying model updates, route a percentage of traffic to the new model and compare metrics before full rollout. Canary deployments for AI.

MLOps: the full lifecycle

MLOps brings software engineering best practices to machine learning. The key practices for production AI:

• Version everything — Code, data, model weights, configurations, and training parameters. You need to reproduce any past model and understand what changed between versions.
• Automated pipelines — Training, evaluation, and deployment should be triggered by code changes or data updates, not manual steps. Tools: MLflow, Kubeflow Pipelines, GitHub Actions, Airflow.
• Feature stores — Centralise feature computation for consistency between training and serving. Prevents training-serving skew (a common source of production bugs).
• Continuous training — Retrain models on new data regularly or when drift is detected. Automate the train → evaluate → deploy cycle with quality gates.
• Rollback capability — Always be able to revert to the previous model version instantly. Blue-green deployments or canary releases with automatic rollback on quality degradation.

The principle: treat ML models like software releases. Version them, test them, deploy them gradually, monitor them, and be ready to roll back.