7 Infra-Level Caching Wins That Can Cut Backend Load by 60% (Without Touching a Line of Code)

We recently worked with an e-commerce startup handling 2–3k requests per second. Their backend was solid: hosted on AWS, running on EKS, using Aurora MySQL, Redis, Cloudflare, and a PHP application alongside a Python-based recommendation engine.

Even with this modern stack, they struggled with high database costs, compute bursts, and inconsistent latency. The twist? We brought huge gains without changing a single line of their application code.

Below are seven caching and infrastructure improvements that any growing digital product — SaaS or e-commerce — can benefit from. Each of these wins tackles performance and cost together, leveraging infrastructure alone.

1. Cloudflare CDN: Cut Up to 70% of Origin Traffic

Cloudflare is widely used for security and DNS, but rarely optimized for caching. Many sites serve product and category pages dynamically, even when they're mostly static.

By enabling full-page caching for anonymous users and tuning cache keys for geography and device type, we shifted a massive portion of requests to be served directly from the edge.

Impact: 50–70% reduction in origin traffic
Benefits: Lower app and DB load, faster load times, better UX globally

2. Reverse Proxy Microcaching: Smooth Spikes, Save CPUs

Implementing microcaching (1–10 seconds) at the reverse proxy layer (Nginx, Envoy, or Ingress controller) for high-traffic endpoints like homepage modules and promo blocks helped absorb traffic spikes. This approach reduced backend strain, contributing to higher availability and adherence to our 99.9%+ uptime objectives.

This technique absorbs repeated traffic patterns during peak times, without affecting freshness.

Impact: 30–60% CPU reduction during peak usage
Benefits: Reduced auto-scaling, fewer noisy alerts, more predictable latency

3. Fix Internal Traffic Leaks: Keep Local Calls Local

One of the most overlooked inefficiencies: services calling each other using public URLs. It seems minor, but in reality it adds latency, crosses availability zones, and incurs egress and load balancer fees.

We rewired these internal calls to go through Kubernetes internal DNS or private ALBs.

Impact: 40–70ms latency reduction per internal call
Benefits: Lower AWS bills, less load balancer traffic, faster inter-service communication

4. ProxySQL Query Routing & SQL Caching: Reduce Aurora Load

Aurora performs well but can become a bottleneck under heavy read traffic, risking potential downtime. By implementing ProxySQL, we:

Routed all eligible SELECTs to Aurora replicas
Cached repeatable SELECTs directly at the SQL layer

These measures alleviated pressure on the primary database, enhancing system stability and supporting our commitment to 99.99% uptime.

Together, these moves offloaded Aurora's writer node and reduced replica count.

Impact: 50–60% reduction in load on the writer instance
Benefits: Fewer replicas needed, faster reads, lower RDS cost

5. Redis as Shared Infra Cache: Offload App Logic

Redis is often used for sessions or queues, but we turned it into a smart shared cache layer. Without touching the app, we cached:

Cart totals and discounts
Popular recommendations by segment
Shipping method logic

These were pre-warmed and managed via background workers, not app logic.

Impact: 30–50% CPU savings on app services
Benefits: Faster responses, lower EKS usage, smoother frontend experience

6. MySQL Index Audit: Boost Query Speed Without a Rewrite

Slow queries aren't always the app's fault. In this case, several key read paths lacked composite indexes. Others had bloated or unused indexes slowing down writes.

We did a targeted audit and restructured a few tables for high-frequency queries.

Impact: Reduced query times from 500ms+ to sub-10ms
Benefits: Snappier UX, lighter DB load, smaller instance sizing

7. Observability: Expose Cache Wins, Tune What Matters

We built dashboards across all layers: Cloudflare, Redis, ProxySQL, and the DB itself. Suddenly, it was clear where caching worked, where it failed, and what was driving costs.

Armed with cache hit ratios and traffic patterns, we adjusted TTLs, fine-tuned rules, and scaled with precision.

Impact: Clear visibility into ROI of infra changes
Benefits: Confident tuning, fast troubleshooting, cost justification

Total Potential Win: Up to 60% Backend Load Reduction

Individually, each technique contributed to performance improvements. Collectively, they reduced backend load by up to 60%, halved RDS costs, and enhanced time-to-first-byte—all without altering application code. More importantly, these infrastructure optimizations fortified system resilience, ensuring consistent uptime and fulfilling our enterprise-grade SLA guarantees.

None of these are magic bullets. They require deep knowledge of traffic patterns, infra behavior, and systems thinking. But if your product is scaling past 1k RPS, there’s a good chance many of these gains are just sitting there, waiting.

And when you’re ready to unlock them, it’s best to have the right people in your corner