17 Pros and Cons of Serverless Architecture for Modern Apps

When deploying machine learning models, you want fast and flexible solutions. The challenge is that traditional approaches for machine learning deployment models can be slow and rigid, making it challenging to achieve your goals. For this reason, many organizations are turning to serverless architecture to ease machine learning deployment. In this article, we’ll cover the pros and cons of serverless architecture to help you make the best choice for your next project.

Inference's AI inference APIs can help you reach your goals by providing a flexible and easy-to-use solution for deploying machine learning models that integrates with your existing tools and workflows.

What is a Serverless Architecture?

Serverless architecture describes a way for companies to build and run applications without managing infrastructure. It provides a way to remove architecture responsibilities from their workload, including:

• Provisioning
• Scaling
• Maintenance

Our Serverless Success on AWS

Since developing this new technology, we’ve seen substantial growth through Amazon Web Services (AWS). A recent O’Reilly survey found that 40 percent of organizations adopted serverless architecture. The main reasons behind the adoption include:

• Reduced costs
• Scalability
• Developer productivity and more

At its heart, serverless is about abstraction. While traditional server setups require you to provision, configure, and maintain physical or virtual machines, serverless abstracts these details, letting you concentrate on writing and deploying code.

How Does Serverless Architecture Work?

Serverless architecture creates an ecosystem that supports modern application needs, from rapid prototyping and cost-efficient scaling to handling real-time data processing and global API distribution. Each component can be tailored to your use case, ensuring flexibility and resilience without the operational burden of traditional infrastructure management.

Core Components of Serverless

Function-as-a-Service (FaaS)

Function-as-a-Service (FaaS) allows you to execute code responding to events without managing underlying servers. Each function is stateless and designed for specific tasks, making it an excellent choice for microservices, event-driven applications, and real-time processing. With built-in scalability and a pay-per-use model, FaaS ensures that you only pay for active invocations:

• AWS Lambda
• Azure Functions
• Google Cloud Functions

Serverless Storage

Serverless storage solutions offer highly scalable, pay-as-you-go object storage for the following:

• Static assets
• Data lakes
• Media files for streaming

These services eliminate the need to manage capacity and performance manually, enabling seamless integration with serverless applications. They also support advanced capabilities like lifecycle policies and versioning to optimize storage costs and management. (e.g., S3, Blob Storage, Cloud Storage)

Serverless Databases

Both SQL and NoSQL serverless database options eliminate the need to manage servers while automatically scaling based on demand. Relational options support traditional SQL workloads with serverless benefits like on-demand scaling and minimal downtime, while NoSQL solutions provide high-performance storage for key-value and document-based data.

These databases are ideal for event-driven, real-time analytics and user-focused applications. (e.g., Aurora Serverless, Cloud Spanner, DynamoDB, Cosmos DB, Firebase Realtime Database)

Serverless Containers

Serverless container platforms enable you to deploy containerized applications without worrying about:

• Server provisioning
• Scaling
• Patching

This approach is perfect for custom runtimes, larger dependencies, or workloads requiring significant control over the environment while maintaining serverless flexibility. (e.g., Fargate, Cloud Run, Container Instances)

Serverless API Management

API management platforms allow you to build, publish, and monitor APIs at scale without the complexity of managing backend infrastructure. These tools integrate with serverless functions to streamline API lifecycle management and enhance security, scalability, and monitoring. (e.g., API Gateway, API Management)

Serverless Orchestration and Workflow Automation

Serverless orchestration tools automate workflows across distributed services, making managing complex, event-driven applications easier. They enable you to:

• Sequence tasks
• Handle errors gracefully
• Maintain state across functions

This significantly reduces the effort required to build robust workflows. (e.g., Step Functions, Logic Apps, Cloud Workflows)

Related Reading

• CPU Acceleration
• AI Workloads

17 Pros and Cons of Serverless Architecture

1. Amplified Scalability

Serverless architecture can effortlessly manage variations in application workloads. Serverless environments reduce latency and enhance overall performance by automatically scaling to handle an increase in concurrent users or devices.

This is especially useful when geographically dispersed end users access applications, as a serverless model can minimize delays by leveraging cloud providers’ points of presence near users.

2. Cost-Efficiency

One of the most significant advantages of serverless architecture is cost-effectiveness. According to a study by Deloitte, serverless computing can reduce operational costs by up to 70% for specific workloads. In a serverless setup, you only pay for the computing time you consume.

Optimizing Costs with Serverless Efficiency

There’s no charge when your code is not running. This pricing model can lead to substantial cost savings compared to traditional hosting. It’s particularly advantageous for applications with variable usage patterns, where server load fluctuates dramatically, showcasing the benefits of serverless computing.

3. Easy Deployment

Serverless architectures also contribute to faster time to market. A survey by O’Reilly found that 40% of organizations using serverless reduced their time to market for new products and features. Developers can focus purely on writing code and creating features without worrying about the underlying infrastructure.

Unleashing Developer Velocity

Reducing administrative tasks accelerates development cycles. As a result, organizations deploy applications or updates faster than with traditional models. This agility is crucial in a competitive market where speed determines market leadership, and that’s another reason why serverless is beneficial.

4. Better Productivity

Serverless computing makes developers more efficient. Studies show that developers spend 30% to 40% of their time struggling with infrastructure issues. Developers should focus on writing code, not investing time and energy into operating and maintaining frameworks. If you opt for serverless computing, developers will have more time to focus on their core tasks.

5. Enhanced User Experience

End-users don't care about the backend, infrastructure, or code you've written. They care about the frontend, the interfaces they're seeing and experiencing. With server maintenance out of the way, organizations can invest their effort and time in enhancing elements that delight end users.

An app with a fantastic UX design is crucial if you want to attract and engage your users strongly.

6. Security

Many articles on serverless architecture list security as a disadvantage. This post will mention some of the most commonly shared security concerns in the next section. Still, it’s essential to understand that top cloud vendors dedicate themselves to providing the most secure, performant, and available service possible.

Shared Responsibility, Unwavering Security

That is a key component of their business model, so it stands to reason that they employ some of the best in the business to create and maintain these services and ensure they provide the absolute best practices.

Developers still need to consider some security related to the application itself. Still, industry experts handle the bulk of it for you, which is a significant advantage.

7. Reduced Latency

Thanks to Content Delivery Networks (CDN) and Edge Networks, serverless functions can be run on servers closer to end users all over the globe. Popular examples of edge computing providers for the Jamstack include:

• Cloudflare Workers
• AWS Lambda@Edge
• Netlify Edge
• Vercel Edge Functions

8. Reduced Operational Complexity

Serverless computing offloads responsibilities like:

• Server maintenance
• Patching
• Management of the cloud providers

Simplifying Operations, Amplifying Focus

This way, it significantly reduces operational complexity. Thanks to this shift, IT teams devote more time and resources to improving product features and customer experiences rather than getting bogged down in maintenance tasks.

9. Improved Reliability

Reliability improves in a serverless environment because the cloud provider maintains a highly available, fault-tolerant computing environment. According to IgMin research, up to 60% of downtime incidents will be avoided, and significant savings will result from avoided outages.

If a server fails, the provider automatically redirects the functions to another server without downtime, maintaining the application's availability and performance.

10. Modernized Infrastructure

The shift to serverless architecture enables organizations to update their infrastructure by tapping into cloud-native services. In so doing, they eliminate the need for traditional server maintenance and management, leading to quicker provisioning, scalability, and agility that anticipates future changes.

Disadvantages of Serverless Architecture

11. Vendor Lock-in

While it is possible to pick and choose services from different vendors, the most straightforward approach is to use a single cloud services provider, such as AWS, since each vendor will have its way of doing things.

This can be challenging if you want to migrate to a different provider. You will completely rely on vendors to provide optimal service at all times. If there is an infrastructure problem, you have to wait for them to fix it.

12. Security

Cloud providers often run code from several customers on the same server simultaneously. They do this by using a technique known as Multitenancy. Simply put, customers are tenants with only access to their server share. This presents a scenario where data is exposed due to a server misconfiguration.

13. Performance Issues

Serverless computing can also introduce performance issues, particularly related to cold starts. A cold start happens when a system invokes a serverless function after it has been idle and must load it into memory before execution. This delay impacts performance, especially for applications requiring immediate response times.

14. Debugging and Testing

Debugging is complex due to the reduced visibility of backend processes managed by the cloud provider. Additionally, a serverless environment can be challenging to replicate to perform integration tests. It’s not all bad news, though. As the serverless ecosystem grows, new platforms and services are released to solve such issues.

15. Cold Start Latency

Serverless functions have an inherent startup time, known as cold start latency, which occurs when a function is invoked for the first time or after a period of inactivity. During cold starts, the serverless platform provisions resources and initializes the environment, which can introduce delays in application response time.

Initial Pause, Sustained Speed

While the subsequent invocations benefit from low latency, the initial latency may impact real-time or latency-sensitive applications. It is essential to evaluate the specific use cases and performance requirements to determine the impact of cold start latency on the application's overall user experience.

16. Complex State Management

State management in a serverless architecture is complex since each function execution is stateless. Maintaining state across multiple functions or managing user sessions requires additional architecture components like external databases and storage systems. This complicates the application design and increases costs.

17. Limited Control Over Infrastructure

Serverless architectures abstract away much of the infrastructure management, providing simplicity and ease of use. Nevertheless, this abstraction can also limit fine-grained control over the underlying infrastructure. Developers control the underlying hardware, networking, and system-level configurations less.

Customization and fine-tuning may be restricted, which can be a concern for applications with:

• Specific performance
• Security
• Compliance requirements

It is essential to assess the level of control needed for the application and evaluate whether serverless architecture aligns with those requirements.

Related Reading

• AI Cloud Computing
• Edge AI vs. Cloud AI
• GPU vs. CPU for AI
• Edge Inference

When to Use Serverless Architecture

Serverless architecture is ideal when you want to build applications quickly, scale dynamically, and pay only for what you use. Nevertheless, it’s not a one-size-fits-all solution. Let’s break down the scenarios where serverless shines and highlight specific use cases to help you decide when it’s the right choice.

Event-Driven Applications

No matter how complex your system, events will always occur that can trigger actions and workflows. Serverless architecture is ideal for applications that respond to these discrete events, especially when the events are unpredictable and appear at a high velocity.

• Use case: Trigger functions in response to specific events such as file uploads, database changes, or user actions.
• Example: A serverless function resizes images uploaded to an S3 bucket or processes payments triggered by an API call.

Real-Time Data Processing

Serverless functions collect and process real-time high-velocity data streams, such as logs, IoT events, or social media feeds.

• Use case: Process high-velocity data streams in real time, such as logs, IoT events, or social media feeds.
• Example: Using AWS Lambda with Kinesis or Azure Functions with Event Hubs to analyze and filter sensor data from IoT devices.

Microservices Architecture

Serverless functions are a natural fit for microservices architectures. If you’re breaking a monolithic application into smaller, loosely coupled services that communicate via APIs, deploying serverless functions for individual microservices can simplify operations.

• Use case: Break monolithic applications into smaller, loosely coupled services that communicate via APIs.
• Example: Deploying serverless functions for individual microservices, such as user authentication, payment processing, and notifications, each independently scaled.

Static Website Hosting

Serverless architecture can serve static content like HTML, CSS, and JavaScript without needing a web server.

• Use case: Serve static content like HTML, CSS, and JavaScript without a web server.
• Example: Hosting a static website on AWS S3 with an AWS CloudFront CDN for global delivery.

API-First Development

In today’s cloud-based world, applications are often built around APIs. Serverless functions are ideal for creating lightweight, scalable APIs.

• Use case: Build serverless APIs for lightweight, scalable backend services.
• Example: Using AWS Lambda with API Gateway to create a RESTful API that supports a frontend application.

Workflow Automation

Serverless functions automate business processes, data ingestion, or periodic tasks.

• Use case: Automate business processes, data ingestion, or periodic tasks.
• Example: Scheduling AWS Lambda functions with CloudWatch Events to perform daily ETL (Extract, Transform, Load) operations.

Continuous Integration/Continuous Deployment (CI/CD) Pipelines

You can leverage serverless workflows to automate code testing and deployment workflows.

• Use case: Automate code testing and deployment workflows.
• Example: Leveraging serverless workflows to integrate, test, and deploy code using AWS CodePipeline and CodeBuild.

Batch Processing and Scheduled Tasks

Serverless functions are perfect for running batch jobs or periodic processes without provisioning dedicated servers.

• Use case: Run batch jobs or periodic processes without provisioning dedicated servers.
• Example: Scheduling AWS Lambda to process nightly data uploads, clean datasets, or generate reports.

Internet of Things (IoT) Applications

Serverless functions are well-suited for handling device-generated events, aggregating data, and providing real-time analytics.

• Use case: Handle device-generated events, aggregate data, and provide real-time analytics.
• Example: Using Azure Functions with Azure IoT Hub to process telemetry data from smart devices and trigger alerts.

Prototyping and MVP Development

You can build and test new ideas quickly with minimal infrastructure overhead.

• Use case: Build and test new ideas quickly with minimal infrastructure overhead.
• Example: Creating a serverless backend for a minimum viable product (MVP) that scales as needed.

Scalable Machine Learning Inference

Serverless functions can perform on-demand predictions from pre-trained ML models.

• Use case: Perform on-demand predictions from pre-trained ML models.
• Example: Deploying serverless inference endpoints using AWS Lambda or Google Cloud Functions to classify images or recommend products.

When to Avoid Serverless

While serverless has numerous benefits, there are situations where it may not be the best choice:

• High, Predictable Traffic: Applications with steady, heavy workloads may benefit from reserved or dedicated instances for cost efficiency.
• Long-Running Processes: Serverless functions have execution time limits (e.g., AWS Lambda’s max of 15 minutes). Long-running tasks are better suited for containerized or VM-based solutions.
• High Throughput Applications: Applications requiring persistent connections or high transaction rates (e.g., chat servers) may not perform optimally with serverless.
• Latency-Sensitive Workloads: Cold start latency can impact performance for applications with strict latency requirements.
• Highly Specialized Infrastructure Needs: Serverless offerings may not be compatible with workloads requiring specific hardware configurations or unusual operating environments.

High-Performance, Cost-Effective Serverless LLM APIs and Specialized AI Solutions

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

Best Practices for Serverless Architecture

Optimize Cold Start Performance

Cold starts can be a hassle when serverless functions spin up a fresh container to handle an incoming request. Choose smaller runtimes and lightweight dependencies to reduce the time needed to run your tasks. For example, if you’re building an application with Python, consider using a simple Lambda function instead of a container image.

Minimizing Dependencies

Try to minimize the number of libraries, modules, or packages your function depends on. Use provisioned concurrency for latency-sensitive functions. This feature keeps a specified number of function instances "warm" and ready to respond immediately to incoming requests, reducing cold start times to nearly zero.

Design for Observability

Observability is essential for maintaining serverless applications. Use centralized logging systems like AWS CloudWatch or Azure Monitor to gain insights into your functions’ performance and errors. Implement tracing tools like AWS X-Ray or Google Cloud Trace for distributed applications.

These can help you track the flow of requests through each component of your serverless application so you can identify performance bottlenecks and troubleshoot errors.

Secure Your Serverless Environment

Use Serverless for the Right Workloads

Serverless computing is not a one-size-fits-all solution. While it has many advantages, it should be avoided for applications with persistent connections or high throughput that may benefit from dedicated servers.

For example, online gaming and real-time communications (RTC) applications may not perform well on a serverless architecture, as they require low latency and stable connection times.

Leverage Serverless Containers for Complex Apps

Serverless containers can be an excellent alternative for complex applications that require custom runtimes or significant dependencies. While traditional serverless functions like AWS Lambda have a quick startup time, they can get sluggish if they need to load a lot of data or package dependencies on cold starts.

Serverless container platforms like Google Cloud Run are more suitable for workloads like these as they allow for more customization and can handle larger packages.

Incorporate CI/CD Pipelines

Serverless functions are often part of a larger application. This means changes to the code for one function can impact the performance of others. To ensure serverless deployments are smooth and don’t disrupt application performance, automate the deployment process with CI/CD pipelines.

These should include testing and monitoring for serverless functions to identify issues before they reach production. Infrastructure-as-code tools like:

• AWS SAM
• CDKs
• Pulumi

Serverless applications can expose your organization to risks if not configured correctly. Follow security best practices to mitigate vulnerabilities. Start by applying the principle of least privilege in IAM roles. This means granting function roles permissions only to the resources they need to perform their tasks.

Secure APIs and data using:

• API keys
• Encryption
• Input validation

Adopt Event-Driven Design

Serverless functions are ideal for handling discrete events. When a function is triggered, it dashes and stops, incurring no charges until the next event occurs. To optimize performance, architect applications around events, such as:

• Database triggers
• API calls
• Storage events

Mitigate Vendor Lock-In

Serverless computing is often touted as a way to reduce infrastructure management. Nevertheless, it can also create a new set of dependencies, making it difficult to change providers if needed. To mitigate this risk, abstract cloud-specific components using frameworks like Terraform. Opt for multi-cloud-compatible solutions to keep your options open.

Plan for Data Governance

When designing serverless workflows, it’s important to consider data governance and compliance requirements. For example, if your application processes personal data from users in the EU, it must adhere to GDPR. To ensure compliance, document your serverless application and design it to meet relevant requirements. Sensitive data should be encrypted at rest and in transit.

• Terraform can be used to streamline deployments

Establish Cost Controls

Serverless computing can help reduce costs, but monitoring usage is essential to avoid unexpected charges. For example, a serverless function that processes uploaded files might incur low costs during normal operations. Nevertheless, if the application suddenly hits with a traffic spike, the function could start invoking thousands of times to handle the uploads.

Monitoring usage metrics and setting budgets or alerts can help prevent runaway costs, especially with high-volume invocations.

Related Reading

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Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.