Serverless Computing: Benefits and Applications

For a long time, web developers’ job responsibilities were to write code to solve the customer’s problems, set up the application’s running environment, manage the servers, and host the process. Most of the developer’s time was spent installing the required operating systems and libraries and resolving dependencies to test and run the web applications. Ultimately, this resulted in decreased productivity for the developer due to divided attention on development and circular maintenance. To deal with this, companies require a solution that relieves developers from purchasing, hosting, and managing the servers, and instead, allows them to focus on code.

Serverless Computing: A New and Easier Approach to Web Development#

Any technology in the world is a response to a specific problem. Similarly, serverless computing came into existence to give a solid response to conventional monolithic web architectures. These are traditional architectures to build web-based applications in which we have to host web and database servers on-premises and employ a team to maintain this architecture. Monolithic architectures have become unusable due to their cost and management overheads.

What Does Serverless Mean?#

After reading or listening to the term “serverless,” the first thing that comes to mind is that there is no server. Absolutely not! Indeed, there is server infrastructure for the deployment and execution of our applications.

Serverless computing is an abstraction of the underlying cloud computing infrastructure. Serverless computing is a cloud computing model in which a cloud provider or a third-party vendor manages the servers for our company. Our company does not require us to purchase, install, host, and manage servers. Instead, our cloud manager provides all these services to us. According to a market survey of serverless computing, it will register a significant CAGR of 23.17%“Serverless Computing Market Size, Trends, Growth, Overview (2021-2026).” n.d. Www.mordorintelligence.com. https://www.mordorintelligence.com/industry-reports/serverless-computing-market. for the forecast period 2019 to 2027.

Serverless computing, also known as Function-as-a-Service (FaaS), ensures that the code exposed to the developer consists of simple event-driven functions. As an outcome, developers become more focused on writing code and providing innovative solutions without the headache of creating test environments and preparing and managing servers for web-based applications. 

Expanding beyond functions: Serverless containers and Kubernetes#

Serverless computing is no longer limited to Functions-as-a-Service (FaaS). Many teams now adopt serverless containers or serverless Kubernetes platforms, which combine the flexibility of containers with the simplicity of serverless deployment.

• Serverless containers: Services like AWS Fargate, Google Cloud Run, and Azure Container Apps let you run containerized applications without managing servers or clusters. You define the container image, and the platform handles scaling, networking, and infrastructure behind the scenes.

• Serverless Kubernetes: Solutions like Knative or KEDA extend Kubernetes with serverless capabilities, allowing workloads to scale to zero and back up automatically based on demand.

This evolution means you’re no longer restricted to short-lived, stateless functions — you can now run full containerized services with serverless convenience.

Serverless at the edge and multi-cloud deployments#

Serverless has moved beyond the central cloud. In 2025, developers increasingly deploy functions at the edge — closer to users — to reduce latency and improve performance. Platforms like Cloudflare Workers, Vercel Edge Functions, Fastly Compute@Edge, and Deno Deploy make it possible to execute code within milliseconds of end users.

At the same time, multi-cloud and hybrid serverless deployments are becoming more common. Tools such as OpenFaaS, Fn Project, and Dapr allow organizations to run serverless workloads across different cloud providers or on-premises environments. This approach reduces vendor lock-in and improves resilience by avoiding dependence on a single platform.

Benefits of Serverless Computing#

Various companies around the world are porting their applications to serverless computing models. For instance, Netflix uses AWS LambdaAmazon Web Services (AWS) offers a computing platform as a web service known as AWS Lambda. It serves as a computing service that executes code in response to events while automatically managing the necessary servers and resources to ensure the successful execution of the code. to offer their product on a large scale. Similarly, many other companies use serverless computing cloud models due to various advantages, including:

• Cost effectiveness: This is one of the significant advantages of serverless computing. In conventional cloud services, users pay for over-provisioning resources like storage and CPU time, which often remain idle. However, serverless computing is a pay-for-value costing model; users only pay for the time their code utilizes CPU time and provisioned storage space.

• Faster turnaround: We can move faster from idea to market because our team can fully concentrate on coding, testing, and iterating without the overhead of operations and server management. We don’t need to update the underlying infrastructure like operating systems or other software patches. We can concentrate on building the best possible features without caring about infrastructure and resources.

• Quicker scalability and elasticity: We don't need to worry about the autoscaling policies or systems. Our cloud vendor is responsible for automatically scaling up the capabilities and technologies to cater to the customer’s demands from zero to peak. Similarly, serverless functions should be automatically scaled down when fewer concurrent users exist. This elasticity makes the serverless computing models a pay-as-value billing model.

• Productivity: Developers don’t need to handle complex tasks like multithreading, HTTP requests, etc. FaaS pushes the developers to focus on building the application rather than configuring it.

Addressing performance challenges and cold starts#

One of the most common challenges in serverless architectures is cold starts — the latency that occurs when a function is invoked after being idle. While platforms have improved significantly, cold starts still affect high-performance systems.

Here are common strategies to mitigate them:

• Provisioned concurrency: Pre-warm a set number of function instances so they’re always ready to respond instantly.

• Smaller deployment packages: Minimize dependency size and reduce initialization time to shorten cold starts.

• Language choice: Use faster-starting languages like Go or Node.js when latency is critical.

• Warm-up patterns: Schedule periodic “ping” events to keep critical functions alive.

These optimizations can make serverless a better fit for real-time or latency-sensitive applications.

Observability, monitoring, and debugging in serverless systems#

While serverless abstracts away infrastructure management, you still need visibility into how your functions behave in production. In fact, observability is even more important because you have less direct control over the environment.

Best practices include:

• Centralized logging: Use built-in tools like AWS CloudWatch or Azure Monitor, or external platforms like Datadog or New Relic, to aggregate logs and metrics.

• Distributed tracing: Adopt OpenTelemetry or AWS X-Ray to trace requests across functions and services.

• Local testing and emulation: Tools like AWS SAM CLI, Serverless Framework, and LocalStack let you run serverless functions locally before deploying them.

These practices help developers troubleshoot issues, understand performance bottlenecks, and ensure their applications run reliably at scale.

Security and permissions best practices#

Security is a critical aspect of any serverless application, and 2025 best practices go far beyond simply restricting access. Because functions are often small and modular, least privilege access becomes essential.

Recommendations include:

• Granular IAM roles: Assign each function the minimal set of permissions it needs, rather than sharing broad roles across multiple functions.

• VPC integration: Connect serverless functions to virtual private networks for secure data access.

• Secrets management: Use managed services like AWS Secrets Manager or Azure Key Vault to store credentials securely.

• Isolation: Take advantage of container-level isolation and runtime sandboxes to protect against side-channel attacks.

By treating security as a first-class design goal, you can ensure serverless workloads remain secure without sacrificing scalability.

Stateful workloads and orchestration patterns#

While serverless functions are inherently stateless, modern platforms support ways to handle stateful and long-running operations. This opens the door to new use cases, including workflows, background processing, and business process automation.

Some useful approaches include:

• Durable workflows: Services like Azure Durable Functions or AWS Step Functions manage state and orchestration for long-running tasks.

• Event sourcing and CQRS: Capture and replay events to build stateful applications on top of stateless infrastructure.

• Orchestration vs. choreography: Choose orchestration for centralized workflow logic or choreography for loosely coupled, event-driven designs.

These patterns make serverless a powerful choice for complex, multi-step systems — not just simple event handlers.

Cost optimization beyond compute time#

While “pay only for what you use” remains a core benefit of serverless computing, cost optimization now goes beyond execution time. Developers must consider additional factors that influence total cost of ownership.

Key areas to monitor include:

• Data transfer: Outbound data and cross-region traffic can significantly impact costs.

• Provisioned concurrency: Reduces latency but adds a baseline cost for reserved capacity.

• Storage and database access: Costs scale with usage and may exceed compute costs in large systems.

• Function duration: Optimizing function code and avoiding unnecessary processing can reduce total runtime costs.

Understanding these variables helps avoid unexpected bills and keeps serverless workloads financially efficient.

Quotas, limits, and scalability considerations#

Serverless platforms automatically scale functions to meet demand, but that scalability is not unlimited. Each provider enforces specific limits on concurrency, execution time, memory, and payload size.

Some best practices:

• Know your quotas: Review provider documentation to understand concurrency limits and request throttling policies.

• Graceful degradation: Implement fallback logic for when requests exceed quotas.

• Batching and throttling: Use event queues or batch processing to manage large workloads efficiently.

Designing with these limits in mind ensures your applications scale predictably under heavy load.

Serverless architectural best practices#

Modern serverless applications follow established patterns that improve reliability, maintainability, and performance. Some of the most common include:

• Event-driven architecture: Use event buses or pub/sub systems to decouple components.

• Fan-out/fan-in: Break down large tasks into parallel functions and aggregate results.

• Idempotency: Ensure functions can safely retry without producing duplicate results.

• Bulkhead isolation: Prevent one failing function from impacting the entire system.

These patterns help you design resilient, production-ready serverless applications that scale gracefully.

Avoiding vendor lock-in and improving portability#

Vendor lock-in is a growing concern as serverless adoption increases. Building directly on provider-specific APIs can limit your flexibility and make migrations costly. In 2025, many teams adopt abstraction frameworks to avoid being tied to a single platform.

Options include:

• Serverless Framework or AWS CDK: Provide reusable infrastructure code across multiple providers.

• Knative or OpenFaaS: Enable serverless workloads to run on-premises or across clouds.

• Dapr: Abstracts away cloud-specific services like messaging and state management.

Designing for portability from the start helps future-proof your applications and maintain flexibility as your infrastructure evolves.

New platform capabilities and serverless AI#

Recent advances are expanding serverless beyond traditional use cases. For example:

• Lambda SnapStart: Pre-initializes function environments to reduce cold start latency.

• Function URLs: Provide lightweight HTTP endpoints without API Gateway overhead.

• Serverless AI inference: Platforms now support running AI models serverlessly for tasks like image classification or text generation.

• Edge integrations: Combine serverless compute with CDN layers for ultra-low-latency applications.

These enhancements demonstrate how serverless is evolving from a niche deployment model into a foundation for the next generation of cloud applications.

Real-world case studies and lessons learned#

Finally, no discussion of serverless is complete without real-world examples. Case studies from companies like Netflix, Slack, and Coinbase reveal common themes: dramatic reductions in operational overhead, rapid scaling to meet unpredictable demand, and cost savings over traditional architectures.

However, they also highlight challenges — from cold-start delays to complex debugging workflows. Sharing these lessons helps teams plan better and avoid common pitfalls when adopting serverless computing.

Serverless Computing Key Vendors#

There are various companies in the market that provide serverless computing services. Some of the market leaders in serverless computing are the following:

• Amazon

• Google

• Microsoft

• IBM

• Alibaba

• Oracle Corporation

• Firebase

Serverless Computing Use Cases#

Serverless architectures have different use cases few of which are outlined below:

• Serverless computing for APIs: One of the most common use cases for serverless computing is REST API. The developer writes a serverless API using serverless functions (e.g., Lambda if AWS is the cloud provider) and receives HTTP requests and data stores (e.g., DynamoDBAmazon provides a NoSQL database service called Amazon DynamoDB as part of its web services. DynamoDB offers key-value based data storage, along with various built-in services such as encryption, autoscaling, backup, replication, and more.) to retrieve and store user data. 

• Serverless computing for storage: In a traditional application development environment, creating and managing data stores is one of the complex tasks. The emergence of serverless data stores like Firebase makes it easier for developers to create and manage databases without the headache of taking backups and other database operations. The cloud manager handles the datastore hosting for end users by following the pay-as-you-used billing model.

• Serverless computing for asynchronous systems: Any system with inconsistent user request patterns can benefit from serverless computing technology. WebhooksIn web development, a webhook is a technique used to enhance or modify the behavior of a web page or web application through custom callbacks. These callbacks can be created, modified, and controlled by third-party users and developers who might not have any direct association with the original website or application. can be an excellent example of inconsistent request patterns, usually fired occasionally. Still, when it happens, a serverless function gets triggered to perform this task and send a response to the user without provisioning a server around the clock. Additionally, alerts and chatbot customer support messages invoked asymmetrically can significantly benefit from serverless architectures.

Conclusion and Next Steps#

Adoption of the serverless computing cloud model is on the rise, particularly in small and medium-sized enterprises (SMEs) and startups, as they want to deliver innovative products and features in minimum time with less operational cost. The serverless computing model is ideal for companies looking for solutions to unburden their development teams so they can spend more time creating innovative and large-scale solutions with agility. So if we want to cut our business costs and quickly move our application from idea to market, this is the best time to adopt serverless computing. It is here to stay! 

Happy learning!