What You Need to Know About Serverless Computing vs Edge Computing

What You Need to Know About Serverless Computing vs Edge Computing

In the world of technology, there are a range of options available when it comes to hosting your applications. From traditional server-based infrastructure to cloud computing and now Serverless Computing and Edge Computing. In this blog post we will explore the differences between Serverless and Edge Computing and discuss what you need to know before making a decision on which option is best for your organization’s needs. We will cover performance metrics, scalability, security considerations, integration/interoperability requirements as well as future trends in order to help you make an informed choice.

Serverless Computing

What is  serverless computing

Serverless computing is a cloud-based technology that allows developers to build and run applications without having to manage the underlying infrastructure. It enables organizations to focus on developing their business logic, while taking advantage of the scalability, performance, and cost benefits offered by cloud computing. While serverless does not eliminate all of the complexity associated with traditional hosting models, it still offers many advantages compared to these methods.

Serverless computing examples

Mobile and web applications

Traditional applications utilize serverless computing for its cost-effectiveness and scalability, ensuring seamless performance for users.

Live video broadcasting/ streaming services

Serverless computing efficiently handles image processing and data streams in services requiring repetitive functions, such as live video broadcasting and streaming.


Serverless architecture is well-suited for task-based systems like chatbots, leading to reduced operational costs and efficient functioning.


Several key benefits of serverless computing include:


Serverless computing automatically scales resources based on demand, ensuring cost savings.

Cost efficiency

Is serverless cheaper than edge computing? Yes. So many online businesses are beginning to take advantage of this technology as they look for ways to reduce costs and improve customer experience.

Faster time to market

Developers can quickly deploy and update applications without the need to configure or manage servers.

Event-driven architecture

Serverless computing is inherently event-driven, allowing applications to respond to events in real-time.

Global availability

Serverless platforms are often hosted in multiple regions, ensuring global availability and low-latency access for users around the world, enhancing user experience.

Built-in fault sustainability

In case of hardware failures or other issues, applications can automatically failover to healthy instances without user impact.

Integration capabilities

Serverless services are designed to easily integrate with other cloud services, APIs, and third-party services, allowing developers to create complex and feature-rich applications.


Serverless architectures offer limited customization compared to traditional hosting models, potentially lacking certain features. Security concerns are addressed by major providers but may still require attention. Integration challenges arise due to the distributed nature, making a unified monitoring complex.

Despite drawbacks, serverless computing is attractive for high scalability, especially when combined with edge computing for efficient data processing near endpoints.

Edge computing

Compute at edge?

Edge computing processes data locally, enhancing performance, scalability, security, and cost-efficiency compared to centralized cloud systems. It reduces latency and offers greater privacy control by keeping sensitive data within the local network, inaccessible to external parties without permission from internal IT staff.

Edge computing examples


Edge computing is deployed in manufacturing setups to monitor equipment in real time, enabling instant analytics and enhancing product quality by identifying and rectifying defects promptly.

Self-driving cars

Real-time analytics powered by edge computing is crucial for self-driving cars, allowing them to make split-second decisions that ensure the safety of passengers and others on the road.

Healthcare devices

Utilizing IoT (Internet of Things) devices and edge computing drastically reduces response time during medical emergencies. For instance, a diabetes monitoring device equipped with edge computing can automatically administer medication, potentially saving lives through swift and automated responses.


Several key advantages of edge computing include:

Minimized latency

With data processing occurring in close proximity to its origin, there is virtually no distance between data generation and processing points, leading to nearly imperceptible latency in edge computing environments.

Optimized bandwidth usage

Placing computational power at the data generation site enables devices to function efficiently with reduced bandwidth requirements. This streamlined approach allows devices to operate on smaller bandwidths, enhancing overall network efficiency.

Real-time processing and analysis

Thanks to remarkably low latency, edge devices possess the capability to provide essential analytics instantaneously. Real-time processing and analysis become possible, enabling swift decision-making and responsiveness in various applications.

Congestion mitigation

Edge devices possess the intelligence to filter and process only essential data, transmitting pertinent information to servers for further analysis. This selective data transmission significantly alleviates data congestion over the internet, ensuring a smoother and more streamlined flow of information.


Despite offering significant benefits, however, edge computing architectures have drawbacks  compared to their cloud counterparts (particularly when it comes to security).

Firstly, due to  distributed nature, protecting against malicious actors becomes more complex than usual, since any attack could potentially affect multiple nodes simultaneously increasing system vulnerability overall. This risk may be diminished somewhat through careful implementation alongside  additional security measures, such as encryption protocols & access authentication mechanisms etc.

Pros and cons comparison

When it comes to comparing the pros and cons of edge vs serverless computing, the main advantages that serverless brings to the table are scalability, cost savings and ease of deployment. Serverless architectures offer a highly elastic platform that can quickly scale up or down as needed.This  means businesses can easily respond to changing demand without having to continuously manage their infrastructure. Additionally, due to its distributed nature there is no need for hardware procurement which further reduces costs. Finally, serverless deployments require minimal configuration making them ideal for rapid prototyping & development cycles; which is particularly attractive for those looking for quick time-to-market solutions.

On the other hand, one of the primary benefits offered by edge computing is improved performance; since data processing occurs at local endpoints rather than in remote cloud servers, latency times are reduced significantly allowing applications to feel more responsive and provide users with near real-time results. Additionally, as mentioned previously, privacy concerns may also be lessened when compared with traditional models; since sensitive information never leaves an organization’s internal network it cannot be accessed by external parties without expressed permission from IT staff – potentially increasing customer confidence and trust in your services.

Performance metrics and scalability

Performance metrics are an essential tool for understanding how well a system is performing. They can provide valuable insights into the operation of software systems, allowing developers to rapidly identify and address any issues that may arise. In addition, performance metrics also serve as a benchmark for scalability – enabling organizations to ensure their applications remain capable of meeting increasing demands over time.

When considering performance metrics there are several key indicators one should take into account. These include:

  • Throughput -measuring the amount of data processed in a given period; latency -the time it takes for requests to be fulfilled;
  • Resource utilization -how much hardware or memory space is being utilized;
  • Uptime/availability -ensuring service availability.

By analyzing factors like system performance, scalability, and caching techniques, architects can assess workload capacities.

In terms of scalability, this refers to the ability of an application or system architecture to design itself so that it remains capable of handling increased load without compromising on quality-of-service requirements such as response times or security protocols. This involves two possible types of scaling - horizontal and vertical. Horizontal scaling means that additional node instances are dynamically added upon demand. Vertical scaling is when individual nodes become more powerful by running better processors/memory etc. Additionally, caching techniques can also help reduce latencies by storing frequently requested data close at hand rather than having to search through entire datasets every single request.

Overall, therefore, when designing systems architects and engineers must consider both performance metrics and scalability in tandem.

Security considerations

Security considerations are a critical aspect of any system’s design and must be addressed in order to ensure the safety and integrity of data.

The first step is to adopt industry-standard security protocols such as TLS/SSL for communication between devices, encrypting sensitive data at rest or in transit, using firewalls to block unauthorized access attempts, and properly authenticating users before granting them access.

In addition to traditional measures there are also more modern solutions available. These include:

  • Identity and access management systems which allow administrators to control who has access to what resources. And configure user permissions accordingly;
  • API key authentication schemes for establishing secure connections between services;
  • Various forms of two-factor authentication which require additional passwords/tokens etc. even after initial logins have been successful.

In edge computing, security risks multiply due to its distributed nature. To safeguard against attacks, dedicated solutions, including encryption and access authentication, are crucial.

Regular penetration testing should also be conducted on all systems, regardless whether they are cloud based or deployed locally at the edge.Тhis will help identify potential vulnerabilities within your codebase before attackers can exploit them thereby ensuring that only legitimate traffic is being processed through your network. All things considered, therefore, when designing applications or deploying infrastructure businesses must always keep their security requirements front-of-mind.

Integration and interoperability

Integration and interoperability are vital in modern distributed computing. Integration combines technologies into a cohesive system, while interoperability enables data exchange between multiple systems. These concepts enhance customer experiences, facilitate communication, and reduce costs. In edge computing, they are crucial due to the mix of local and cloud resources. Specialized solutions ensure compatibility across devices and platforms, making updates faster and invaluable during critical times.

The future of technology is always advancing and new trends and innovations are constantly emerging. AI is transforming industries by automating tasks, enhancing customer service, and detecting fraud. As more companies adopt AI, its integration into everyday life will increase.

Additionally, edge computing, a distributed architecture located close to devices, offers greater data control, reduced costs, and lower latency compared to traditional centralized solutions. Both trends are reshaping the future of technology.

Finally, blockchain technology also promises to transform the way businesses operate by providing secure and immutable record keeping capabilities; this means important information such as financial transactions can be stored securely without fear manipulation or fraudulence thereby allowing trust between parties involved become much stronger. In addition, smart contracts can also help streamline processes and reduce costs associated with manual labor traditionally required sign offs.

Serverless edge computing

The emergence of serverless and edge computing technologies has revolutionized the way businesses operate in the digital age. Serverless architecture offers organizations a cost-effective means of managing their applications while still maintaining scalability and flexibility. Meanwhile, edge computing provides real-time data processing capabilities at an incredibly low latency rate – something that is invaluable when it comes to predictive analytics or industries where time is of the essence such as healthcare or transportation.

Combining serverless and edge technologies enhances performance and efficiency, minimizing downtime compared to traditional cloud solutions. This synergy allows easy deployment and upgrades, managed centrally using containerization tools like Docker Swarm and Kubernetes. This approach is not only financially advantageous but also improves security, making it a promising choice for organizations.


What is the fundamental difference between serverless computing vs edge computing?

Serverless computing refers to cloud-based services where the cloud provider automatically manages the infrastructure, while edge computing involves processing data closer to the data source, reducing latency and enabling real-time processing.

What is the difference between edge functions vs serverless

In summary, edge functions are optimized for low-latency, real-time processing at the network edge, while serverless functions are versatile, event-driven, and scalable, making them suitable for a broader range of applications where immediate response is not the primary concern. The choice between the two depends on the specific requirements of the application, especially concerning latency, real-time processing needs, and scalability.

What is an edge function?

Edge function is a specific piece of code designed to execute on edge computing infrastructure, which is located closer to the data source or end-users than traditional centralized cloud servers. Unlike traditional cloud computing, which processes data in remote data centers, edge computing processes data locally on devices, gateways, or servers at the edge of the network.