8 Tips for Scaling APIs to Handle Increased Traffic

Scaling APIs to manage increased traffic is crucial for ensuring uninterrupted service and performance for your customers. Every API is different, and the best techniques to scale your API depends on how it is designed and how it is being used. That's why I decided to collect ideas from across the industry on the best ways to scale APIs. Let's take a look at what API engineering experts suggest:

Proactive Monitoring and Load Testing#

"I've been responsible for managing APIs that serve millions of users daily. We've learned that it's crucial to continuously monitor our API performance and anticipate potential bottlenecks before they occur.

One specific example is how we've implemented a comprehensive load testing suite that simulates various traffic patterns and scales up to 10x our expected peak usage. This has allowed us to identify and address performance issues early on, ensuring our APIs can handle the increasing demands placed on them.

My advice to other engineers would be to never underestimate the value of thorough testing and monitoring. Invest the time and resources up front to build a resilient API architecture that can adapt to changing user needs. It's a lot easier to scale proactively than to play catch-up when your system is already overloaded."

Harman Singh, Senior Software Engineer, StudioLabs.

Great tips from Harman - for further reading, you can check out our guides on end-to-end API testing and also learn about some API monitoring tools.

Build Scalability In from the Start#

"One of the most important lessons I have learned in scaling APIs is that it's not only about handling more traffic, it's more about keeping systems reliable, efficient, and cost-effective. Over the years, working with large-scale systems at ZoomInfo, Wayfair, Walmart, and IBM, I have found that the following strategies make the biggest impact:

Scalability Should Be Built In, Not Added Later:

  • Retrofitting a monolithic system for scalability is difficult and costly. Microservices, Kubernetes, and Serverless architectures allow systems to grow seamlessly. \

  • Recently, I built an event-driven Kafka-based system that significantly reduced bottlenecks and improved scalability.

Rate Limiting and Traffic Control Are Essential:

  • Without rate limiting and traffic shaping (using tools like API gateways), a spike in requests can overwhelm APIs and the overall system. \

  • Smart limits prevent system overload while ensuring fair access for users.

Smart Caching Makes a Huge Difference:

  • Caching at multiple levels (CDN, Redis, Memcached, GraphQL persisted queries) reduces database load and speeds up API responses. \

  • I have seen caching optimizations improve response times by 70% while lowering backend costs.

Asynchronous and Event-Driven Processing Prevents Bottlenecks:

  • APIs should not be waiting on slow processes. Kafka, RabbitMQ, and AWS SQS help offload tasks, reducing latency. \

  • This approach was crucial in the Phoenix Project, where moving to an event-driven model improved reliability and reduced delays.

Observability Is Key:

  • You can't fix what you can't see. Real-time monitoring with Prometheus, Datadog, OpenTelemetry, and distributed tracing (Jaeger, Zipkin) helps detect performance issues early.

Auto-Scaling Saves Both Money and Performance:

  • Over-provisioning resources is wasteful. Kubernetes auto-scaling (HPA) and predictive ML-based scaling ensure APIs handle traffic spikes efficiently. \

  • In one of my projects, predictive scaling reduced AWS costs by 30% while maintaining near-100% uptime.

At the end of the day, scaling APIs isn't just about adding more servers; it's about designing systems that can grow while staying reliable and efficient. A combination of event-driven architecture, caching, and automated scaling has helped me build APIs that handle high traffic while keeping performance strong."

Dileep Kumar Pandiya, Principal Engineer, ZoomInfo

Use a Reverse Proxy for Efficiency#

"Use a reverse proxy. Let's imagine your REST API needs to handle 10K+ requests per second. Scaling up with more CPU is costly and inefficient. A reverse proxy (Nginx for example) will redistribute traffic and cache the responses. Load balancing will help you prevent a situation where a single server is overwhelmed while others sleep. And caching prevents repetitive database queries. Static content (images, CSS, JS) stays cached for long periods, while API responses (like popular search results or frequently accessed data) can be cached for seconds or minutes.

At peak hours in my game analytics platform, repeated database queries cause huge slowdowns. Adding Nginx caching, I reduced the database load by 80% and sped up responses. But also, some level of trade-off takes place here. Some trending rankings were slightly outdated due to caching delays. To fix this, I bypassed the cache for games that are trending right now while keeping historical data cached. This gave us high-speed performance while keeping critical data fresh."

Lucas Wyland, Founder & CTO, Steambase

Optimize Architecture, Caching, and Load Balancing#

"One of the biggest lessons learned when scaling APIs to handle increased traffic is that scalability isn't just about adding more servers—it requires optimizing architecture, caching, and load balancing from the start. Simply throwing more infrastructure at a problem can lead to cost inefficiencies and latency issues if the API isn't designed to scale efficiently.

One key piece of advice: implement caching strategically. Using Redis or CDN caching for frequently requested data can drastically reduce API load and improve response times. Additionally, rate limiting and throttling are essential to prevent abuse and ensure fair resource distribution.

Another critical approach is asynchronous processing and event-driven architecture. Instead of making API calls synchronous (which can block resources), use message queues like Kafka or RabbitMQ to handle heavy loads without degrading performance.

Lastly, monitor and optimize continuously. Tools like Prometheus, Grafana, and distributed tracing (e.g., OpenTelemetry) can help detect performance bottlenecks before they impact users.

By focusing on caching, event-driven design, and proactive monitoring, businesses can ensure their APIs scale efficiently, handle spikes smoothly, and deliver a seamless experience under increased demand."

Sergiy Fitsak, Managing Director, Fintech Expert, Softjourn

Start with Aggressive Caching#

"I've found that caching is absolutely game-changing for handling API traffic spikes, especially after our WordPress plugin hit 100,000 users. We implemented Redis caching for frequently accessed endpoints, which cut our database load by 80% and kept response times under 100ms even during peak hours. My biggest piece of advice is to start with aggressive caching on your most-hit endpoints and gradually fine-tune based on real usage patterns—don't wait for performance issues to start thinking about caching strategy."

Joshua Odmark, CIO and Founder, Local Data Exchange

Add Hardware to Buy Time for Refactoring#

"In such cases, the simplest approach is to tackle the problem by adding more hardware to buy time for refactoring the application.

While this provides an immediate solution, scaling APIs is an ongoing process. Once the issue is instantly resolved by allocating more resources, the next step is to analyze the application itself, the database queries, and other factors.

Often, some queries can be optimized, resulting in significant performance improvements. Additionally, breaking the service into microservices and scaling services can be very helpful in these situations.

There are many approaches, and these are just the most straightforward ones."

Slava Shahoika, Head of Engineering, Vention

Use the Right Load Balancer#

"The right load balancer is key to auto-scaling. The biggest lesson I have learned about scaling APIs to handle increased traffic is that it is crucial to use the right load balancer. The right load balancer shares the workload evenly across the available pool of servers, which is critical to increasing your application's reliability and capacity. Deploying an ineffective load balancer will do the exact opposite thing catching you unawares if the server falls over.

We use the AWS load balancing, which helps us to build a load balancing service into our API infrastructure, making it relatively easy to launch servers on demand. If you are running a high-traffic application, consider using a mix of load balancing platforms. For example, you can use Nginx and HAProxy to direct traffic to each. Although the infrastructure of your API depends on many factors, we have found load balancing to be very effective in dealing with unexpected traffic spikes."

Roman Milyushkevich, CEO and CTO, HasData

Shift to Event-Driven Architecture#

"Shifting from a request-response model to an event-driven architecture completely changed how my API handled traffic spikes. Instead of overwhelming the system with synchronous processing, message queues like Kafka helped distribute the load more efficiently. This allowed background tasks to run asynchronously, keeping response times fast even during peak usage.

Decoupling services made scaling smoother and prevented bottlenecks that used to slow everything down. Managing high traffic doesn't just mean adding more servers, it's about designing an architecture that naturally absorbs the load."

Stanislav Khilobochenko, VP of Customer Services, Clario

Wrapping Up#

Hopefully you found those tips useful and relevant for scaling your API. If you're interested in improving your API's performance with a lightweight, edge-deployed API gateway - get in touch.

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