APIs are the backbone of modern applications, handling traffic between clients
and backend services. But poor performance can lead to slow response times,
system overloads, and unhappy users. Here's how to fix that.
The 6 Key Strategies to Increase API Performance
Caching: Store frequent responses
to reduce backend load.
Rate Limiting: Control traffic to
prevent overloads.
Regional Endpoints: Serve users closer to
their location to lower latency.
Efficient Authentication:
Use serverless functions (ex. Lambda authorizers) to manage security without
slowing down requests.
Performance Monitoring: Track metrics
like latency and error rates to spot and fix issues early.
Each strategy tackles specific challenges like high latency, resource
bottlenecks, and slow payloads. For example, caching alone can drastically cut
response times, while rate limiting ensures stability during traffic spikes.
Quick Overview
In case you have a specific problem you are trying to solve, here are the
sections you should read about:
Challenge
Solution
High Latency
Caching, Regional Endpoints
Resource Bottlenecks
Rate Limiting, Quotas
Slow Payloads
Payload Optimization, Compression
1. Use Effective Caching Strategies
Adding caching to your API can significantly boost performance by lowering
backend workload and speeding up response times. You can consider it a
double-win as your customer gets faster responses while your backend is freed up
to process other requests. When done right, caching becomes a powerful tool for
improving your API's efficiency.
Why Caching Matters
Caching helps minimize backend strain, reduce delays, and handle more requests,
which translates to better scalability, smoother user interactions, and smarter
resource use. Tools like and
have shown impressive results in
high-demand API setups
.
Caching is especially important in AI models
where compute is expensive.
How to Set Up Caching Policies
First-and-foremost, it is much easier to roll out caching across your entire API
when using an API gateway (its actually one of the
most popular features of API gateways).
A gateway will help you build a consistent caching implementation (aka a caching
policy) that can be reused across endpoints.
To make caching work effectively, focus on these four key areas:
TTL (Time-to-Live): Decide how long cached data remains valid based on how
often it changes. For example, use a short TTL for frequently updated data and
a longer one for static content. Some gateways like AWS API Gateway supports
TTL values ranging from 0 to 3600 seconds
[2],
while others like Zuplo are
fully customizable.
Cache Keys: Define unique cache keys using elements like headers, URL
paths, or query parameters. This allows for precise control over which
responses get cached
[2].
Capacity Planning: Select a cache size that balances performance needs
with budget constraints. Monitor hit rates and adjust capacity as usage
patterns evolve
[2][5].
Cache Invalidation: Schedule updates to cached data during times of low
traffic to keep information accurate.
While caching is great for reducing backend demand, combining it with strategies
like optimizing payload sizes can further cut latency and speed up data
transfer. Together, these methods form a solid foundation for improving API
performance.
2. Reduce Payload Sizes
Cutting down payload sizes plays a big role in improving API gateway
performance. Less data being transmitted means lower latency and better
throughput for your API operations.
Transforming Payloads
Beyond caching, reducing the size of transmitted data speeds up API responses.
Again, this is something you should consider doing at the gateway level so you
don't have to rewrite serialization/compression across multiple codebases. Many
API gateways, offer built-in tools for
transforming payloads
to make data transfer more efficient.
One standout method is using Protocol Buffers,
which create payloads that are 3-10 times smaller than JSON equivalents
[1].
The tradeoff here is that you lose the ability to manually inspect and
understand your traffic, as well as the need to use a protobuf implementation on
your client to deserialize your payload.
Payload Optimization
A quick win you can get is to remove unnecessary fields in your response
body AND headers. Schematizing your responses with OpenAPI and JSON schema helps
a lot in keeping track of all your fields. Once you have those definitions, you
can use JSON schema validation (aka as Contract
Testing in APIs) to ensure your backend is not sending anything unnecessary.
If your API consists of a large list of entities being sent back in the API
response, consider supporting
pagination
to limit the amount of data being sent back without compromising your users'
ability to fetch multiple records.
Applying Compression
Compression is another way to shrink payloads significantly. The algorithm you
choose affects both performance and compatibility.
Compression Method
Advantages
GZIP
Works well with text-based data and is widely supported.
Brotli
Offers 17-25% better compression than GZIP, perfect for modern web apps.
Client compatibility: Can your clients handle the chosen algorithm?
Processing overhead: Balance the CPU cost against bandwidth savings.
Data type: Text-based data may benefit more from compression than binary.
Reducing payload sizes helps cut latency, but pairing it with rate limiting
ensures your system stays reliable during heavy traffic.
3. Apply Rate Limiting and Quotas
Rate limiting and quotas help maintain
your API's performance by controlling request volumes and ensuring resources are
distributed fairly. This keeps your APIs responsive and prevents system
overloads.
Setting Thresholds
Many API rate limiting implementations (whether as a 3rd party module, or as a
policy in an API gateway)
allow you to configure specific thresholds based on various factors.
Here are some common threshold types and their use cases:
Threshold Type
Example
Purpose
Per User
Twitter: 300 tweets/3 hours
Avoids excessive usage by one user
Per IP Address
Region-based quotas
Protects against DDoS attacks
Per Application
10,000 requests/day (free tier)
Supports tiered service plans
When defining thresholds, consider these factors:
Traffic patterns: Analyze peak and off-peak usage trends.
System capacity: Ensure limits align with your infrastructure's
capabilities.
User plans: Adjust thresholds for free and paid users.
Handling Bursts and Throttling
Token bucket algorithms
are a great way to manage traffic bursts. They allow short-term flexibility
without compromising system stability.
To manage bursts effectively:
Use burst tokens: Let users accumulate tokens during low-traffic times for
later use.
Queue excess requests: Instead of dropping them, buffer extra requests to
process them later.
Adaptive throttling: Dynamically adjust limits during high-traffic periods
to maintain performance.
Offer upgrade paths for users who need higher limits.
Monitor system performance and fine-tune thresholds over time.
Apply gradual penalties for violations instead of outright blocking users.
Once rate limiting is in place, you can further improve API performance by
deploying regional endpoints to minimize latency
[3].
4. Deploy Regional Endpoints
Setting up regional endpoints helps improve API performance by reducing latency
for users located in different parts of the world. Pairing this with caching and
rate limiting can make APIs even more efficient.
Why Regional Deployments Matter
Deploying APIs in regions closer to your users can make a noticeable difference.
According to AWS, placing APIs in the same region as your users can cut latency
by as much as 70% compared to a single-region setup. Less distance for data to
travel means faster response times.
Benefit
Impact
Example
Reduced Latency
40-70% faster response times
US-East users accessing US-East endpoint
Higher Throughput
25-35% better request handling
Balancing regional traffic during peak hours
Improved Reliability
99.9%+ uptime with redundancy
Automatic failover between regions
Consider Edge Deployments
You can take this approach a step further by
deploying your API to the Edge. Edge
locations are far more numerous than cloud regions (ex. Cloudflare has over 300+
deployment locations) and provide much better latency, with the tradeoff of
having to use a custom runtime. A good middle-ground is to have an
edge-deployed gateway
so you can perform throughput heavy operations (ex. authentication) closer to
your user. Caching really shines when combined with edge deployments - edge
cache will likely provide the fastest possible response to your user.
Key Steps for Multi-Region Deployment
Major platforms like Netflix use DNS routing and
load balancing to deliver fast, reliable service globally. Here are some ways to
set up regional endpoints effectively:
Configure DNS Routing: Use DNS services like
Route 53
to direct users to the nearest endpoint automatically.
Enable Load Balancing: Deploy global load balancers to manage traffic
based on location, endpoint health, and capacity.
Monitor Performance: Tools like AWS
CloudWatch
can help track latency, error rates, and traffic flow across regions.
When choosing regions for deployment, focus on areas with the highest user
demand to maximize impact.
Once regional endpoints are in place, the next step is to secure and streamline
access.
5. Use Serverless Functions for Authentication
Serverless functions (ex. Lambda, Cloudflare workers) can automatically scale to
meet traffic demands - and can be applied to your auth code to avoid
bottlenecks. If you're an AWS API gateway user, you might be familiar with
Lambda authorizers
which implement this concept well. This setup improves gateway performance by
spreading the workload more evenly.
Tips for Better Performance
Allocate enough memory for complex authentication processes.
Cache authentication results to avoid repetitive validation.
Keep response times under 100ms to maintain a smooth user experience.
6. Monitor Performance Metrics
Keeping an eye on performance metrics isn't just about keeping things running -
it's about spotting and fixing potential issues before they affect users. By
regularly tracking key indicators, you can ensure your API stays efficient and
ready to handle demands.
Key Metrics to Keep an Eye On
Here are the metrics that matter most for tracking your API's health:
Latency: Aim for response times under 100ms.
Error Rates: Keep errors below some threshold defined by your business
(ex. 1%).
Throughput: Monitor to ensure it matches your system's capacity.
Resource Usage: Stay below 70% to avoid overloading.
These metrics give you a clear picture of your system's performance and help you
quickly spot any red flags.
Tools and Practices for Performance Monitoring
There are
several API monitoring tools
on the market that can provide advanced error tracking, trend analysis, and deep
insights.
Here’s how to make the most of these tools:
Set Up Monitoring for Every Endpoint: Ensure every API endpoint is being
tracked. I hate to beat a dead horse here but PLEASE use a gateway for
this - whether you buy one,
build your own,
self-host an open source gateway -
centralization will make your life easier.
Establish Alerts: Configure alerts for key thresholds like high latency or
error spikes.
Track Resource Usage: Keep an eye on patterns to predict and prevent
overloading.
Analyze Trends for Better Optimization
Understanding performance trends is just as important as monitoring. Dive into
the data to uncover patterns:
Identify peak traffic times and adjust resources accordingly.
Review error logs to spot recurring issues.
Monitor how response times change under different loads.
Study resource usage trends to plan for scaling needs.
Conclusion
Once performance monitoring is in place, the focus shifts to refining
optimization strategies for sustained success. Optimizing an API is a continuous
process, crucial for handling the demands of modern applications. The six
strategies outlined here tackle major API performance challenges effectively.
For instance, implementing these methods can increase API success rates by up to
50%. Netflix achieved this with tools like
EVCache, a distributed caching solution
designed to improve performance
[1][3].
To maximize the benefits of these strategies, keep these priorities in mind:
Start with monitoring: Leverage tools such as Moesif or DataDog to
establish a performance baseline before making any changes.
Implement step-by-step: Focus first on strategies that address your most
critical performance issues (see the chart at the top).
Track and refine: Continuously measure key metrics to confirm improvements
and make necessary adjustments.
If you’re ready to level up your API's performance,
check out Zuplo. It's an
edge-deployed API gateway that helps you provide a fast and intuitive developer
experience for your customers.
Frequently Asked
Questions
Learn about API management and how Zuplo helps your team build better APIs.
