Backend Hardening Under Live Traffic

The Situation

Blitzly runs multiple AI agents — Scout, Brand, Outreach, Legal and Launch — that turn a single business idea into a live launch flow. During public beta, traffic was unpredictable: waitlist signups, agent runs and page loads spiked together in ways lab tests never reproduced.

Standard load-testing tools can approximate volume, but they miss chaotic patterns — uneven agent workloads, long-running sessions and repeat hits on the same launch assets. That leaves hidden risks: memory leaks after sustained use, Supabase query paths that choke under contention, and edge routes on Vercel that overload while background workers sit idle.

The goal was not a greenfield rebuild. Sky Insights needed to harden the stack Blitzly already ran on and prove it under the same live traffic their users were generating — using Blitzly’s own beta as the test rig.

Our Approach

We instrumented Blitzly’s production path end to end — Vercel edge and serverless routes, AWS services, Supabase reads/writes and Redis cache layers — and tuned against live beta traffic, not synthetic benchmarks alone.

Live Traffic as the Test Rig

As Blitzly’s beta grew, request volume passed one million live interactions across agent workflows, landing pages and API calls. Telemetry on Vercel and AWS captured latency, error rates and resource usage in real time — giving us behaviour to tune against instead of lab-only metrics.

Caching Built for Spikes

We added Redis caching around the heaviest read paths — repeated agent outputs, launch previews and shared assets — so burst traffic did not hammer Supabase on every request. Repeat queries served from memory kept response times steady when many users hit the same flows at once.

Concurrency and Routing Optimization

We monitored how traffic distributed across Vercel functions and AWS-backed workers, then refined Nginx routing and backend configuration. Memory allocation was profiled and leaks removed. Routing was adjusted so no single node saturated during asymmetric spikes. The stack now degrades gracefully instead of failing suddenly.

The Results

• Over one million live requests, no downtime – Blitzly’s beta stack stayed stable through sustained, burst-heavy traffic on Vercel, AWS and Supabase.
• Hidden issues flushed out and fixed – Live agent workflows exposed memory leaks and edge-case failures that lab tests had missed; each was patched and re-validated under load.
• Production-ready baseline – The hardened configuration now supports Blitzly’s live platform and informs how Sky Insights approaches high-concurrency SaaS deployments.