AI Model Deployment Types Explained 2026: Batch, Online, Edge, and Serverless
Four deployment types for AI models and exactly when to use each one.
💡 What You Will Learn
Four deployment types for AI models and exactly when to use each one.
📜 Table of Contents
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.
❓ 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.
Written by our editorial team; tools listed here are tested or verified against public sources. Links point to official sites or GitHub repos for reference only โ no paid placements.
