Serverless computing is not just about running code—it’s about simplifying application development and eliminating the need for infrastructure management. This is made possible through various backend services that form the foundation for building modern, cloud-native applications:

• Function as a service (FaaS)
  FaaS is the core offering of serverless computing, allowing developers to write discrete functions triggered by events. These functions are stateless, meaning they do not retain data between executions. This simplifies scaling and makes these functions highly efficient for handling individual tasks. Cloud providers fully manage the underlying containers, ensuring that resources are allocated dynamically, and clients are billed only for the time and resources used. 

• Backend as a service (BaaS)
  BaaS offers pre-configured backend functionalities—authentication, push notifications, database management, file storage, etc. Developers can integrate these services via API integrations, allowing them to rapidly build modular applications without having to ‘reinvent the wheel.’ This approach is particularly useful for accelerating development timelines while maintaining flexibility and interoperability with other cloud-native services. 

• Serverless databases and storage 
  Serverless platforms provide highly scalable SQL and NoSQL databases that dynamically adjust to workload demands without requiring manual intervention. These databases are designed to support event-driven workflows, making them ideal for applications with unpredictable data traffic.

• Event-driven architecture
  Serverless computing excels in enabling event-driven architectures, where workflows are triggered by real-time data or system events. Many platforms allow developers to process streams of data or react to changes in external systems, such as database updates or API calls. This enhances responsiveness, making it ideal for applications requiring real-time analytics, IoT integrations, or automated business logic execution.

Serverless computing redefines how cloud resources are provisioned and managed, offering a distinct approach compared to traditional models. Unlike these traditional models, which often require active management and configuration, serverless abstracts infrastructure management entirely, delivering an event-driven, fully managed environment where resources scale automatically. 

Serverless vs. infrastructure as a service (IaaS)

IaaS provides on-demand access to virtual machines, storage, and networking resources, offering flexibility for users to configure their own infrastructure. However, this model may require developers to actively manage provisioning, patching, and scaling servers, which can add complexity to maintaining application performance. 

Likewise, in IaaS, capacity planning remains the user’s responsibility, as over- or under-provisioning can result in wasted resources or degraded performance. With serverless, scaling happens automatically, without manual intervention, ensuring applications handle traffic spikes or lulls.

Serverless vs. platform as a service (PaaS)

PaaS simplifies application deployment by providing pre-configured environments, including operating systems, runtime libraries, and development tools. Developers do not have to set up or maintain underlying infrastructure, but they still must define resource requirements and manage scaling policies. Serverless, on the other hand, abstracts these requirements, scaling functions in real-time based on demand without requiring user input.  

PaaS typically uses fixed runtime environments, which can limit flexibility for developers working with multiple programming languages or frameworks. Serverless platforms support polyglot development, giving developers the freedom to use their preferred languages for each function.

Containers and Kubernetes

Containers offer portability and consistency by packaging applications and their dependencies together, ensuring they run uniformly across all environments. However, containers require orchestration tools like Kubernetes to manage tasks such as scaling, load balancing, and resource allocation, where serverless computing abstracts and automatically manages these operational complexities. 

Unlike serverless, containers often involve idle resource costs, as users typically allocate resources upfront even if they are underutilized. Serverless avoids this by provisioning resources dynamically and shutting them down immediately after execution, ensuring 100% efficiency. Additionally, while containers are well-suited for long-running applications, serverless functions excel at handling short-lived, event-driven tasks, offering a more focused solution for specific use cases like API calls, data processing, or event handling. 

Serverless computing is designed to simplify application development, but with this convenience comes a different set of potential challenges. Understanding these challenges is critical for evaluating whether serverless is a business.

New technology

As a relatively recent innovation, serverless computing is still maturing, which can lead to challenges in adoption. Limited best practices, evolving toolsets, and a lack of experienced developers can create a steep learning curve, particularly for organizations with complex or large-scale applications. Teams may also encounter compatibility issues when integrating serverless with legacy systems or workflows. 

Investing in training programs or hiring developers with serverless expertise (where available) can help organizations bridge the knowledge gap. Similarly, open-source community or vendor-specific support channels can provide a source of guidance and tools to address compatibility challenges. Organizations can also consider using hybrid models, where serverless is applied incrementally alongside traditional architectures, reducing the risk of disruption during adoption. 

Not suitable for all applications

Serverless is ideal for short-lived, stateless tasks—it struggles with long-running processes or applications requiring persistent state. Workloads that demand consistent low latency may encounter performance bottlenecks due to cold starts (delays that occur when a serverless function is invoked for the first time or after it has been idle). Additionally, applications requiring extended processing times may exceed the runtime limits imposed by serverless platforms, leading to interruptions.

For long-running tasks, organizations can split workloads into smaller, event-driven functions to fit within serverless constraints. To address cold starts, enabling ‘provisioned concurrency’ can minimize latency. For applications requiring persistent state, hybrid architectures that combine serverless with managed databases or traditional services may provide a more balanced approach. 

Migration

Adopting serverless often involves vendor lock-in, as cloud providers use proprietary APIs, architectures, and management tools. Migrating serverless workloads to a different provider—or back to an on-premises environment—can be costly and time-intensive, demanding significant reconfiguration or rewriting of application code.

To reduce vendor lock-in, organizations can adopt open-source serverless frameworks, which provide portability across multiple cloud platforms. Using standardized tools and APIs wherever possible can also simplify future migrations.

Serverless platforms automatically scale applications up or down based on real-time demand, eliminating the need for manual capacity planning. This dynamic scaling ensures that applications can handle sudden traffic spikes without overprovisioning resources. Additionally, the ability to scale down to zero when idle makes serverless solutions particularly cost-effective for infrequent or unpredictable workloads.

Serverless accelerates the software development lifecycle. Developers can push updates or bug fixes directly to production without worrying about what is happening with the underlying infrastructure.

Traditional models like IaaS and PaaS often require customers to pay for reserved capacity, which can result in overprovisioning and higher costs during low-usage periods. Serverless platforms charge on a true pay-as-you-go basis, billing for actual usage rather than pre-allocated resources. This aligns expenses with actual activity, making serverless an ideal choice for businesses seeking predictable and efficient budgeting.

With serverless computing, teams no longer need to worry about tasks like patching, scaling, or monitoring servers, which streamlines workflows and reduces context-switching. This productivity boost accelerates development cycles and gives developers more freedom to do higher value work.

Serverless computing’s flexibility and efficiency make it an ideal solution for a wide range of application scenarios, especially those requiring dynamic scaling, event-driven execution, or reduced operational overhead. As organizations increasingly seek agile, cost-effective solutions, more and more companies are discovering a growing list of possible use cases: 

• Batch processing
  Tasks like image resizing, PDF generation, and video transcoding can be triggered by batch uploads. Serverless scales automatically to handle unpredictable volumes without incurring idle costs. 

• Data processing
  Real-time data streams (such as IoT sensor reading) can be processed efficiently using serverless computing. Functions can validate, enrich, or transform data on the fly, integrating directly with databases and storage.

• Third-party integration
  Serverless supports API-driven workflows, making it ideal for integrating external services. Examples include payment processing, authentication, and analytics tools.

• Web applications
  Serverless platforms support dynamic web and mobile applications by providing scalable backend services. Developers can build APIs, manage user authentication, and integrate cloud storage without provisioning servers.

In most cases, serverless computing operates on an event-driven architecture. Developers write code as functions or services, which are deployed in containers managed by the cloud provider. These containers are stateless and ephemeral, meaning they run only when triggered, and then self-terminate after execution.  

The process typically follows these steps: 

1. Write and deploy code
   Developers package their code as a function or container image and upload it to the cloud platform. 

2. Trigger events 
   Events such as API calls, file uploads, or scheduled tasks activate the function.

3. Automatically allocate resources 
   The cloud provider provisions the necessary resources to execute the code.

4. Scale dynamically
   Resources scale automatically based on demand, with no manual intervention required.

5. Deallocate resources
   Once the function completes execution, resources are released, and billing stops.

As serverless computing continues to evolve, its potential to transform cloud-native development grows. Building on its strengths in scalability, efficiency, and cost-effectiveness, serverless is expanding into new use cases and addressing existing limitations. This trend is likely to continue throughout the coming years. Emerging innovations and open-source projects are paving the way for broader adoption, ensuring serverless remains a driving force in modern application development.

• Improved support for stateful applications 
  Efforts to integrate state handling will expand the range of use cases suitable for serverless, enabling applications with persistent data requirements.

• Open-source solutions
  Projects like Knative are driving greater portability and interoperability, allowing organizations to deploy serverless workloads across multiple cloud platforms or on-premises environments. 

• Enhanced performance 
  As previously addressed, serverless computing is still in its infancy, and as such it suffers from some performance related issues. That will soon change; innovations will eliminate cold starts and ensure faster startup times, making serverless more competitive for latency-sensitive applications.

• Hybrid cloud adoption 
  Serverless will increasingly integrate with hybrid and edge computing environments, making it possible for businesses to balance workloads between public clouds, private clouds, and on-premises systems.

The phrase ‘managing servers’ shouldn’t have to be in your developers’ job descriptions. Serverless computing cuts that responsibility, allowing your development teams to focus on innovation and delivering value. But serverless needs some support to be fully effective. To maximize these benefits, your organization needs to ensure seamless integration, monitoring, and operational efficiency across your entire IT infrastructure. 

ServiceNow enhances the serverless approach by providing tools that promote agility and visibility throughout cloud-native and traditional IT landscapes. The Now Platform® streamlines workflows, improves collaboration, and ensures alignment between IT and business priorities, while ServiceNow Service Observability offers AI-powered insights to monitor, detect, and respond to changes in serverless and monolithic applications alike. Together, these capabilities enable organizations to create a more responsive and efficient IT ecosystem. 

See for yourself how ServiceNow can help optimize your serverless strategy; request a demo today!