Function as a Service (FaaS): Developers write and deploy individual functions that are executed in response to events. AWS Lambda, Azure Functions, and Google Cloud Functions are popular examples.
Backend as a Service (BaaS): Third-party services handle backend functionalities such as authentication, databases, or real-time updates. Firebase, AWS Amplify, and Supabase are common BaaS solutions.

Cost Efficiency: Serverless architecture can be more cost-effective than traditional server-based models. With serverless, you only pay for the execution time of your functions, rather than for idle server resources. This pay-as-you-go model can significantly reduce costs, especially for applications with variable or unpredictable traffic.
Scalability: Serverless platforms automatically scale based on demand. Whether your application needs to handle a few requests or millions, the serverless provider adjusts resources to meet the demand, ensuring consistent performance without manual intervention.
Simplified Development: Serverless allows developers to focus on writing code without worrying about server management, deployment, or scaling. This can accelerate the development process and reduce the operational burden, especially for small teams or startups.
Faster Time to Market: With serverless, developers can quickly prototype and deploy applications. The reduced complexity of infrastructure management and the ability to integrate with other cloud services streamline the development lifecycle, enabling faster releases.

Cold Starts: When a serverless function is called after being idle, it may take longer to execute due to the time needed to spin up resources—this is known as a cold start. This can lead to latency issues, particularly for latency-sensitive applications.
Vendor Lock-In: Relying on a specific cloud provider’s serverless offerings can lead to vendor lock-in, making it difficult to switch providers or migrate applications to another environment.
Complex Debugging: Debugging serverless applications can be more complex than traditional applications because of the distributed nature of serverless environments. Traditional debugging tools may not be as effective, and developers may need to rely on logging and monitoring tools to troubleshoot issues.
Limited Execution Time: Most serverless platforms impose a maximum execution time for functions (e.g., AWS Lambda has a 15-minute limit). This can be restrictive for long-running tasks or processes that require more time to complete.

Microservices: Serverless is ideal for microservices architectures, where each function performs a specific task. This modular approach allows for better scalability and easier management of individual components.
Event-Driven Applications: Serverless is a natural fit for event-driven applications, such as processing data streams, handling file uploads, or responding to user actions in real-time.
API Backends: Many developers use serverless functions to build API backends, where each API endpoint triggers a specific serverless function. This approach is both cost-effective and scalable.
Automated Workflows: Serverless can be used to automate workflows, such as data processing pipelines, where each stage of the pipeline is managed by a serverless function.

The Rise of Serverless Architecture: What It Means for Developers