What serverless inference options are available in GCP?

Introduction

Serverless computing has revolutionized how businesses deploy and scale machine learning (ML) models by eliminating infrastructure management. Google Cloud Platform (GCP) offers multiple serverless inference options, enabling developers to deploy ML models without provisioning or managing servers. These solutions fall under the broader category of AI Inference as a Service, where prediction workloads are handled in a fully managed, auto-scaling environment.

This knowledge base explores the key serverless inference solutions in GCP, their use cases, benefits, and how they enable AI Inference as a Service.

1. What is Serverless Inference?

Serverless inference refers to the ability to deploy and run ML models without managing underlying infrastructure. Key characteristics include:

Automatic Scaling – Resources scale up/down based on demand.

Pay-per-Use Pricing – Charges apply only when inference requests are processed.

No Infrastructure Management – No need to configure servers, clusters, or VMs.

GCP provides several serverless inference options, making AI Inference as a Service seamless for businesses.

2. Serverless Inference Options in GCP

2.1. Cloud Functions (for Lightweight Inference)

Overview:
Cloud Functions is a lightweight, event-driven serverless compute service that can execute small ML inference tasks.

Use Cases:

Real-time predictions for lightweight models (e.g., sentiment analysis, text classification).

Trigger-based inference (e.g., processing data from Cloud Storage or Pub/Sub).

How It Works:

Deploy a Python or Node.js function that loads a pre-trained model (e.g., TensorFlow Lite).

Trigger via HTTP requests or event-based sources.

Pros:

Fast deployment.

Low latency for small models.

Tight integration with GCP services.

Limitations:

Limited execution time (up to 9 minutes).

Not optimized for large or complex models.

Example:

python

from tensorflow import keras

import numpy as np

model = keras.models.load_model('model.h5')

def predict(request):

    data = request.get_json()

    input_data = np.array(data['input'])

    prediction = model.predict(input_data)

    return {'prediction': prediction.tolist()}

2.2. Cloud Run (for Containerized Model Serving)

Overview:
Cloud Run is a fully managed serverless platform for running containerized applications, including ML models.

Use Cases:

Deploying custom ML models in containers (e.g., TensorFlow Serving, PyTorch).

High-throughput inference with autoscaling.

How It Works:

Package the model in a Docker container with a REST API endpoint.

Deploy to Cloud Run with auto-scaling.

Pros:

Supports any framework (TensorFlow, PyTorch, Scikit-learn).

Scales to zero when idle (cost-efficient).

Customizable CPU and memory allocation.

Limitations:

Cold starts may introduce latency.

GPU support is not natively available (requires workarounds).

Example (Dockerfile for TensorFlow Serving):

dockerfile

FROM tensorflow/serving

COPY saved_model /models/my_model

ENV MODEL_NAME=my_model

2.3. Vertex AI Prediction (Managed Serverless Endpoints)

Overview:
Vertex AI is GCP’s unified ML platform, offering serverless inference via Vertex AI Prediction.

Use Cases:

Batch and online predictions for large-scale models.

AutoML or custom-trained models.

How It Works:

Upload a trained model to Vertex AI Model Registry.

Deploy to an endpoint with serverless scaling.

Pros:

Fully managed AI Inference as a Service.

Supports AutoML and custom models (TensorFlow, PyTorch, XGBoost).

Automatic scaling with low latency.

Limitations:

Pricing can be higher for high-throughput workloads.

Example (Deploying a Model via Vertex AI):

python

from google.cloud import aiplatform

aiplatform.init(project="my-project", location="us-central1")

model = aiplatform.Model.upload(display_name="my-model", artifact_uri="gs://my-bucket/model")

endpoint = model.deploy(machine_type="n1-standard-4", min_replica_count=1, max_replica_count=10)

2.4. BigQuery ML (SQL-Based Inference)

Overview:
BigQuery ML allows running ML inference directly in BigQuery using SQL.

Use Cases:

Running predictions on structured data without moving it.

Simple regression/classification models.

How It Works:

Train a model using CREATE MODEL in BigQuery.

Run predictions via ML.PREDICT.

Pros:

No need to export data.

SQL-friendly interface.

Limitations:

Limited to BigQuery-supported models (linear regression, logistic regression, etc.).

Example:

sql

CREATE MODEL `mydataset.mymodel`

OPTIONS(model_type='logistic_reg') AS

SELECT * FROM `mydataset.training_data`;

SELECT * FROM ML.PREDICT(MODEL `mydataset.mymodel`, TABLE `mydataset.new_data`);

2.5. AI Platform (Legacy) with Serverless Endpoints

Overview:
Before Vertex AI, AI Platform provided serverless model serving. It is now part of Vertex AI but still available.

Use Cases:

Legacy ML model deployments.

How It Works:

Similar to Vertex AI but with older APIs.

Pros:

Supports TensorFlow, Scikit-learn, XGBoost.

Limitations:

Being phased out in favor of Vertex AI.

3. Comparing Serverless Inference Options in GCP

4. Benefits of AI Inference as a Service in GCP

Cost Efficiency – Pay only for what you use.

Zero Infrastructure Management – No servers to maintain.

Automatic Scaling – Handles traffic spikes effortlessly.

Fast Deployment – Deploy models in minutes.

Integration with GCP Ecosystem – Works seamlessly with BigQuery, Pub/Sub, and more.

5. Choosing the Right Serverless Inference Option

For lightweight models → Cloud Functions.

For custom containers → Cloud Run.

For enterprise ML models → Vertex AI Prediction.

For SQL-based analytics → BigQuery ML.

6. Conclusion

GCP offers a robust suite of serverless inference solutions, making AI Inference as a Service accessible to businesses of all sizes. Whether deploying lightweight models via Cloud Functions, custom containers on Cloud Run, or enterprise-grade models on Vertex AI, GCP provides scalable, cost-effective, and fully managed options.

By leveraging these services, organizations can focus on building ML models rather than Cloud infrastructure, accelerating their AI adoption while optimizing costs.