AI Model Deployment Types Explained 2026: Batch, Online, Edge, and Serverless

Four deployment types for AI models and exactly when to use each one.

## AI Model Deployment Types Explained 2026 Every AI system in production uses one of these four deployment types - or a mix. ### 1. Batch deployment Process large datasets offline: nightly embeddings, fraud scoring on historical data, report generation. Tools: Apache Spark, Airflow (46,348 stars). Cost-efficient, no latency requirements. ### 2. Online (real-time) deployment Serve predictions in milliseconds: chatbots, recommendation engines, fraud detection at checkout. Tools: vLLM (87,794 stars), Triton, FastAPI behind a load balancer. Requires GPUs or optimized CPU serving. ### 3. Edge deployment Run models on the device: phones, cameras, IoT. Quantized small models via llama.cpp (122,228 stars), TensorFlow Lite, ONNX Runtime. Zero latency, full privacy, works offline. ### 4. Serverless deployment Pay per invocation, scale to zero: Modal, RunPod Serverless, AWS Lambda with model on EFS. Perfect for spiky traffic; cold starts of 1-10 seconds are the trade-off. ### Comparison table | Type | Latency | Cost | Best for | |------|---------|------|----------| | Batch | hours | lowest | offline jobs | | Online | ms | highest | real-time apps | | Edge | instant | device cost | privacy | | Serverless | seconds | usage-based | spiky traffic | ### FAQ **Can I mix types?** Yes - most companies run batch for backfill and online for the live product. **Which type is cheapest?** Batch, by far, since you can use spot instances and idle GPUs.