Solo Scale Feature Flags to 60M Requests/Mo on Cloud Run for $180
Build and scale a Go app on multi-region Cloud Run to 60M reqs/mo solo: use multi-stage Docker, Cloud Armor regex filtering, and 1-min batch writes to Firestore/BigQuery to keep costs at $180/mo with zero SRE time.
Deploy Multi-Region Serverless Apps That Auto-Scale
Rocket Flag, a feature flag service, handles gradual rollouts (e.g., 1% user exposure to new features) with instant rollback via UI, no redeploys needed. Deploy in Go on Cloud Run across regions for low cold starts and global speed without ops team. Cloud Run auto-scales from 2M to 60M requests/month (peak 26 reqs/sec in Europe), serving HTTP while Go routines manage background tasks like batching—ideal for constant traffic. For bursty loads, queue to Pub/Sub for worker batching instead. Use Cloud Run traffic splitting for rollouts, but feature flags excel for independent toggles, user targeting, and non-dev access via separate web UI.
Secure and Filter Traffic to Cut Noise and Costs
Bad actors probe for files; counter with multi-stage Docker builds copying only the Go binary to scratch image, exposing nothing. Block garbage traffic (cluttering logs) using Cloud Armor regex matching valid URLs—requests fail before hitting app or logs. This keeps monitoring clean as traffic surges.
Batch Database Writes to Flatten Costs at Scale
Tracking flag usage per request spiked Firestore/BigQuery writes (charged per op). Fix: Hold increments in memory, batch-write to Firestore and load to BigQuery every 60 seconds. Deploy slashed Firestore write slope flat post-8AM. Result: $252 AUD ($180 USD) total December bill (free tier covered initial Cloud Run; networking dominated early). Beats multi-VM TCO—no redundancy costs, zero SRE (patching, uptime). Monitor bills continuously; serverless like Firestore/BigQuery affordable with optimizations, else costly traps.
Lessons: Launch Fast, Operate Solo, Optimize Ruthlessly
Cloud Run enables quick launches and auto-scaling for side projects. Offload infra (no server management) to focus on features/clients—one person handles high traffic. Every component must scale affordably: batch, filter, watch bills. Architecture upfront (e.g., stateless Go, serverless DBs) minimizes ops, maximizes velocity.