Beginner

Introduction to Serverless AI Inference

Understand when serverless platforms are the right choice for deploying AI models and how they compare to traditional dedicated inference endpoints.

What is Serverless AI Inference?

Serverless AI inference means running model predictions on cloud functions or container platforms that automatically scale from zero to handle demand, without provisioning or managing any servers. You pay only for the compute time consumed during actual inference requests.

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Key insight: Serverless does not mean there are no servers. It means you do not manage them. The cloud provider handles provisioning, scaling, patching, and availability. You focus entirely on your model and inference code.

Serverless vs Dedicated Endpoints

Aspect	Serverless	Dedicated Endpoint
Scaling	Auto, including to zero	Manual or auto (min instances)
Cost at Low Traffic	Near zero	Fixed (always-on instances)
Cost at High Traffic	Can be higher per request	Lower per request
Cold Start	Yes (seconds)	No (always warm)
GPU Access	Limited (Cloud Run, some services)	Full GPU support
Model Size	Limited by memory/storage	Unlimited
Ops Overhead	Minimal	Significant

When to Choose Serverless

✅

Ideal Use Cases

Bursty or unpredictable traffic, low-volume APIs, lightweight models (NLP classifiers, embeddings), batch processing, and prototype/development endpoints.

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Not Ideal For

Large model inference (LLMs, diffusion models), latency-critical real-time applications, sustained high-throughput workloads, or models requiring GPU acceleration.

Serverless AI Architecture

Model Packaging
Package your model with its dependencies into a container image or deployment package. Optimize for fast loading by using ONNX Runtime, TorchScript, or TensorFlow Lite.
Model Storage
Store model weights in object storage (S3, GCS) or mount via EFS/Filestore. Cache models in the function's ephemeral storage for faster subsequent invocations.
API Gateway
Route inference requests through API Gateway or Cloud Endpoints for authentication, rate limiting, and request/response transformation.
Auto-Scaling
The platform automatically creates new instances when requests arrive and scales to zero when idle. Configure concurrency limits to control maximum parallel executions.

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Best practice: Start with serverless for any new AI inference endpoint. It is the fastest path to production. Migrate to dedicated endpoints only when you have proven demand that justifies the operational overhead and cost of always-on infrastructure.

Next → AWS Lambda

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