While application acceleration and API acceleration have much in common, in fact they are two different ways to look at the same thing. In this post we will examine the nuances and differences between the approaches, taking into account different application topologies such as microservices based, monolithic, multi-tiered, employee focused and external uses focused applications. 

As organizations evolve and grow, so do their expectations that IT systems and applications will continue to meet performance SLAs and maintain high-availability. In parallel they must meet new regulatory requirements and enable the business to adopt new practices such as improved customer self-service, with proactive initiatives that alert customers when their orders are shipped, delayed or delivered, or when an item they waited for is now available, and much more. 

To support modern digital business practices, organizations must provide a top-performing solution to all their users. As many organizations have existing IT systems that were not initially designed to support hundreds of thousands or even millions of customers, and as users are no longer tolerant to poor user experiences, organizations must improve the performance and scale of their applications. 

For monolithic applications, such as a mainframe-based application, there is very little organizations can do. They can either scale up, adding more power to the mainframe or other hardware box, or design an extensible architecture using modern design principles to enhance the performance and reduce the dependency on the back-end legacy systems; we will explore this latter approach in this post.

Multi-tiered applications are a bit more modern and in certain cases, will leverage application servers or other frameworks to provide high availability as well as load balancing. In those cases, scaling can be utilized, but not as easily as with the modern microservices-based architecture. 

While multi-tiered applications and microservices-based applications rely on APIs, their performance will be limited by the performance and availability of the APIs. With monolithic applications, their performance will rely on both the implementation methods as well as the box they run on. Surprisingly, after more than 25 years, most application performance issues relate to where the information is stored — the database.  

API Monetization 

API monetization has revolutionized how businesses operate and interact with their customers. Driven by the increasing demand for digital services, the proliferation of mobile devices, IoT, and the growing adoption of cloud computing, this rapidly growing ecosystem enables businesses and developers to use APIs to exchange digital services and assets. API monetization enables an organization to reuse, share, and monetize its core assets through APIs that can extend the reach of existing services or provide new revenue streams. 

These APIs act as intermediaries, enabling seamless communication and data sharing between different software applications and platforms that also incorporate legacy and third-party systems and data. Developers can leverage these APIs to build innovative applications and services. This interconnectedness allows for rapid innovation, collaboration, and value creation within the digital landscape. 

API Strategy

An API strategy should start with business priorities to ensure that developments are tied to enterprise goals, and that they not just cool tech for the sake of cool tech. Enterprise architects are key stakeholders who can bridge the gap between business goals and implementation, and monitor and mentor the IT and technical teams who implement these strategies. They can define best practices for easy to use, well documented and secure APIs. As the usage of an API grows, the system must be able to handle increased traffic without experiencing performance degradation or downtime.

Some of the design and implementation considerations of an API strategy include:

Legacy Systems

Most performance issues are related to database constraints, even for more modern applications. Systems that have tightly coupled architectures and lack advanced API capabilities may require customization or even system modernization to handle growing demand. 

Security and Privacy Concerns

Ensuring secure access control, data protection, and adherence to privacy regulations.

Governance and Compliance

Establishing proper compliance with industry regulations, data protection laws, and intellectual property rights. SLAs of performance, availability and predictability may be subject to regulation in certain industries, for example, financial regulations related to Open Banking. Especially for these industries, optimizing app performance is essential, not to enhance performance and ensure compliance, but also to avoid payment of fines. 

Ecosystem Alignment

Ensuring interoperability, onboarding partners, and addressing varying technology stacks. 

Design

Even the most powerful servers and modern technology do not guarantee fast performance if proper design principles have not been implemented. The performance and response times of stateless and stateful APIs differ because stateless can be faster since they do not need to maintain the session state or perform additional processing to handle session management. Optimizing Kubernetes and microservices for top performance involves optimizing at the pod level, followed up with continuous monitoring of CPU and memory consumption for fine tuning purposes. Some of the best practices for optimizing the performance of microservices include using asynchronous communication, implementing caching strategies, optimize service design and adopting service mesh patterns. 

Improving the performance of APIs 

Two main strategies can improve the performance of APIs – API Scaling and Application Acceleration. API scaling is specifically concerned with handling increasing API traffic and requests, and ensuring that the API remains available and responsive during traffic spikes. Application acceleration technology focuses on optimizing an application’s performance and user experience, by reducing latency or performance bottlenecks and improving data transfer efficiency. API scaling employs techniques such as horizontal and vertical scaling, caching and load balancing, while application acceleration utilizes code, query and protocol optimization, caching, and CDNs. 

Check out this infographic for more information

In general, for APIs that are experiencing high traffic spikes, API throttling is often used. This approach is not always sufficient to meet business objectives, in which case API Scaling can be used instead. Application acceleration technology is recommended for APIs that need to improve their performance on a more consistent basis. Scaling both the API and the application may be necessary when resource utilization is high. With both strategies, no matter how optimized the applications are with the most advanced technology and best practices, unless the data sources that feed the application serve the data are running at top speed, API performance will suffer. 

An operational data hub enables the development of an external ecosystem with minimal dependencies on the existing backends, by providing decoupling at the data level. The Hub decouples between the data and the applications, and then replicates the operational data into a low-latency, high-performance layer that provides ultra-fast read performance. For more modern applications, this architecture also exposes the data to the API channels. An operational data hub such as Smart DIH is designed to address the challenges of API scaling over legacy architectures: 

• APIs are built over a scalable data platform; scaling out and up only when required 
• Offers load balancing and automatic failover 
• Data is replicated and consolidated into a single system, simplifying scaling operations by abstracting the data into a unified data model

The no-code development tools offered in Smart DIH empower data professionals to launch new services without having to rely on programming expertise. As a result, organizations can scale APIs in response to business needs and rapidly deliver new digital services while ensuring high throughput, low latency, and always-on service availability.

Last words

For APIs, it all comes down to the speed, availability and the reliability of implementing APIs quickly and ensuring they meet their SLAs. Whereas application acceleration technology reduces latency or performance bottlenecks and improves data transfer efficiency utilizing code, caching, and CDNs, API scaling employs horizontal and vertical scaling, to ensure that the API remains available and responsive during traffic surges. Both approaches require data that is served at the fastest possible speed from all sources, to ensure that applications provide responses based on ultra-fresh data. Both API scaling and application acceleration technology have much to offer to improve API performance, on their own and especially in tandem, to ensure a top user experience and a more efficient utilization of resources.