Service
Memory Optimization & Scaling
Memory bloat quietly inflates hosting costs and causes restarts at the worst moments. We tune Puma, Sidekiq, allocators and code paths so each process does more with less — then design for the next 10× in traffic.
Leaner
smaller processes, same work
Sound familiar?
- →R14 / R15 memory errors or OOM-killed containers
- →Paying for larger dynos or instances just to stay up
- →Sidekiq workers ballooning over time
- →Uncertainty about whether the app can handle a launch
Our approach
- 01
Profile
memory_profiler, derailed_benchmarks and heap dumps to find allocation hot spots and leaks.
- 02
Tune
jemalloc, Puma workers/threads, YJIT, Sidekiq concurrency and connection pool sizing.
- 03
Scale
Load testing, caching layers, read replicas and queue architecture for predictable growth.
Questions
Will this lower my hosting bill?+
Often significantly — fewer, smaller processes doing the same work.
Do you work on Heroku, AWS and Kubernetes?+
Yes, plus Render, Fly.io, DigitalOcean and self-hosted setups.
Need help with memory optimization & scaling?
Book a free 30-minute call. You'll talk directly to a senior Rails engineer — no sales team, no hand-offs.