Microsoft specifically documents AI-enabled applications as an Azure Functions scenario, making Functions relevant to modern cloud-native and AI architectures.

Google Cloud Functions / Cloud Run Functions

Google Cloud has changed the terminology around its serverless functions platform.

Cloud Functions is now called Cloud Run functions. The current platform deploys functions as Cloud Run services, while the Cloud Functions v2 API remains available for compatibility.

Cloud Run functions allow developers to write single-purpose functions that respond to HTTP requests or cloud events without managing servers.

It integrates with Google Cloud services such as:

• Cloud Storage

• Pub/Sub

• Eventarc

• Firestore

• Vertex AI

• Cloud Run

• Cloud Build

• Artifact Registry

The current architecture also provides more flexibility than the original first-generation Cloud Functions model. For example, Cloud Run functions can support higher concurrency, configurable resources, and Cloud Run capabilities such as traffic splitting and Direct VPC configuration.

Common Cloud Run Functions Use Cases

Organizations can use Cloud Run functions for:

• File processing

• Event-driven applications

• Streaming data processing

• IoT workloads

• AI and machine learning integrations

• API backends

• Automation

• Firebase applications

AWS Lambda vs Azure Functions vs Google Cloud Functions

The table describes broad architectural characteristics. Specific limits, runtimes, pricing, regions, and features can change, so organizations should verify current provider documentation before implementation.

Serverless and AI in 2026

Serverless is increasingly being used alongside AI applications.

For example, an application could follow this architecture:

User Request → API → Serverless Function → AI Service → Database → Response

A function might validate a request, retrieve data, call an AI model, process the result, and return the response.

Azure Functions, for example, includes documented scenarios for AI-enabled applications. Google Cloud also documents AI/ML use cases for Cloud Run functions.

This model can be useful for applications where AI processing needs to scale according to demand.

Serverless Cost Considerations

One of the major attractions of serverless is usage-based billing.

However, serverless does not automatically mean cheaper.

Costs can depend on:

• Number of invocations

• Execution duration

• Memory or compute allocation

• Network traffic

• Storage

• API calls

• Database usage

• Logging and monitoring

• AI model usage

For applications with predictable, continuous workloads, other cloud compute models may sometimes be worth evaluating.

A good FinOps strategy should therefore consider the entire application architecture rather than looking only at the function’s execution cost.

Serverless Security Considerations

Serverless reduces infrastructure management but does not eliminate security responsibilities.

Organizations should implement:

• Least-privilege identity and access

• Secure secrets management

• Input validation

• Encryption

• Network controls

• Dependency management

• Logging and monitoring

• API authentication

• Vulnerability management

The function itself may be small, but its permissions and connections to other cloud services can create significant security implications.

How to Choose Between AWS Lambda, Azure Functions and Google Cloud

The choice often starts with the cloud ecosystem already used by the organization.

Consider AWS Lambda When:

• Your applications are primarily on AWS.

• You rely heavily on AWS event and data services.

• Your architecture uses services such as S3, DynamoDB, EventBridge, or API Gateway.

Consider Azure Functions When:

• Your organization is invested in Microsoft Azure.

• Applications use Azure services extensively.

• You need integration with Microsoft identity, data, messaging, or AI services.

Consider Cloud Run Functions When:

• Your workloads are built around Google Cloud.

• Your architecture uses Pub/Sub, Eventarc, Cloud Storage, or Firebase.

• You want functions deployed using the Cloud Run-based model.

• Your applications have strong Google Cloud data or AI integration requirements.

Key Serverless Trends for 2026

Several trends are shaping serverless architecture.

AI integration: Functions can act as lightweight orchestration and integration layers for AI applications.

Event-driven architecture: Businesses are increasingly connecting applications through events rather than tightly coupled services.

Container-based serverless: Platforms are combining function simplicity with container-based deployment and configuration.

Workflow orchestration: Long-running and multi-step processes require more sophisticated orchestration capabilities.

FinOps: Teams are paying greater attention to execution costs, traffic, observability, and downstream service consumption.