Apps running just 100ms slower can reduce conversion rates by 7%. It can potentially cost you millions in lost revenue. Yet, many companies overlook powerful AWS optimization options hiding in plain sight.
This guide is made to change it. Here, we’ll discuss tools and AWS development services that will help you create experiences users love.
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AWS Global Accelerator
Many developers mistakenly treat Global Accelerator as merely an alternative to CloudFront. In reality, these services solve fundamentally different problems.
Key Differences from CloudFront:
| Feature | CloudFront | Global Accelerator |
| Purpose | Content caching | Network path optimization |
| Protocol Support | HTTP/HTTPS | TCP/UDP (including HTTP/S) |
| Cache Behavior | Configurable rules | No caching |
| Use Case | Static content delivery | Dynamic content/APIs |
| Pricing Model | Data transfer + requests | Hourly + data transfer |
Global Accelerator works its magic through AWS’s private network backbone. When a user connects to your application, traffic enters AWS’s network at the edge location closest to them (instead of traversing the public internet’s unpredictable routes). This creates a more direct path to your app’s origin, reduces latency, and improves reliability.
Implementation Path with Configuration Examples
Step 1: Create a Global Accelerator in the AWS Management Console.
Step 2: Add listeners for TCP or UDP traffic.
Step 3: Attach endpoints (EC2 instances, ALBs, or NLBs).
Example Configuration:
- Listener: TCP Port 443
- Endpoint Group: US-West-2 and EU-Central-1
- Health Checks: Enabled
Performance Benchmarks Before/After Implementation
- Before: Latency spikes during peak hours, averaging 250ms.
- After: Consistent latency under 50ms globally.
When Global Accelerator Outperforms Standard Solutions
- Multi-region gaming servers;
- Real-time financial trading platforms;
- High-availability SaaS applications.
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ElastiCache with Redis
Pipelining allows you to batch multiple commands together. It effectively reduces network roundtrips. Lua scripting enables complex operations to execute atomically on the server side. Sorted sets offer efficient data structures for leaderboards and time-series data without complex database queries.
These are just some of many Redis features that can enhance performance. The implementation strategy should vary by application layer.
Implementation Strategies for Different Application Layers
- Session Storage: Replace DynamoDB with Redis for faster user sessions.
- Real-Time Analytics: Use Redis Streams to process event data.
- Leaderboards: Store ranked data with Sorted Sets.
When monitoring Redis performance, focus on key CloudWatch metrics such as DatabaseMemoryUsagePercentage, CacheHitRate, SwapUsage, and CurrConnections. Additionally, implement custom metrics to track command latency, key size distribution, and operation type patterns to identify optimization opportunities.
Cost-Benefit Analysis with Real Numbers
- Cost: $0.065/hour per node (cache.t3.micro).
- Benefit: Reduced database load by 60%, saving $1,000/month on RDS costs.
1.Aurora Serverless
Even if your app sits idle, your database costs might spiral out of control. Aurora Serverless is designed to address this issue. It scales up when traffic surges and scales down when things are quiet.
Aurora Serverless adjusts compute and memory resources in real-time. That means you don’t need to guess capacity needs when you set it up. I switched a client’s product catalog database last year and watched their monthly bill drop by 43%. Herewith, performance improved during peak shopping hours.
Here’s why it works so well:
- Pay only for what you use – The database literally shuts down (while maintaining state) during idle periods;
- Auto-scaling within seconds – Handles traffic spikes without you frantically adjusting instance sizes;
- Same MySQL/PostgreSQL compatibility – No need to rewrite your application code.
Setting It Up Is Simple
- In the RDS console, select “Create database” and choose “Aurora”.
- Select “Serverless” as the capacity type.
- Define your minimum and maximum ACU (Aurora Capacity Units).
I recommend starting with a minimum of 0.5 (basically idle mode) and a reasonable maximum like 8 or 16 that matches your peak needs. Monitor for a few days, then adjust as needed.
2.Lambda@Edge
Content delivery networks (CDNs) are great. But they’re often just passive file servers. Lambda@Edge lets you run code directly at the edge locations, milliseconds away from your users.
I think of it as tiny programmable robots stationed at over 310+ global locations. They are always ready to transform content before it reaches your users. I’ve used it to dynamically resize images, authenticate users, and rewrite URLs without touching my core application.
Lambda@Edge executes at 4 possible points:
| Trigger Point | Best Used For | Typical Latency Impact |
| Viewer Request | Authentication, redirects | 1-5ms |
| Origin Request | Content transformation, header modification | 2-10ms |
| Origin Response | Response customization, error handling | 2-10ms |
| Viewer Response | Content personalization, analytics injection | 1-5ms |
The performance gains can be substantial. One e-commerce site I worked with reduced their page load times by 440ms globally. It took moving their product pricing calculations to Lambda@Edge instead of calling back to their main servers.
3.SQS with DAX
If you’re using DynamoDB, you might think you’re already optimized. But combining Amazon SQS (Simple Queue Service) with DAX (DynamoDB Accelerator) results in a performance powerhouse few developers exploit.
DAX serves as an in-memory cache that sits in front of DynamoDB. It reduces read latencies from milliseconds to microseconds (nearly a 10x-100x improvement). When you pair this with SQS to manage processing queues, you get a system that handles massive spikes without breaking a sweat.
Here’s how to implement this combination:
- Set up your DAX cluster through the AWS console (smaller than you think – even a single t3.small node makes a huge difference).
- Configure your DynamoDB client to use DAX instead of connecting directly.
- Create an SQS queue to manage processing.
- Send work to the queue rather than processing synchronously.
Conclusion
Start by auditing your current architecture. Identify bottlenecks and prioritize tools based on effort vs. impact. Partner with experts like Velvetech to ensure seamless implementation.
Implementation Priority Guide Based on Effort vs. Impact
| Tool | Effort Level | Impact Level | Recommended For |
| Global Accelerator | Medium | High | Multi-region apps |
| ElastiCache | Low | Medium | Real-time analytics |
| Aurora Serverless | Medium | High | Variable workloads |
| Lambda@Edge | High | High | Edge-based customizations |
| SQS with DAX | Medium | Medium | High-throughput architectures |
