The gihyo.jp article Reducing Backend Memory Consumption has sparked wide discussion. Backend memory usage is not merely a technical metric. Higher memory consumption demands larger instances, driving up cloud bills, while memory shortages lead to outages from OOM (out of memory) errors. Memory reduction is an investment that simultaneously lowers costs and enhances stability.
Meanwhile, in custom development, incidents where "teams run systems on oversized instances as a stopgap, letting cloud costs balloon while still suffering occasional OOM crashes" never seem to end. Supporting system development for clients, we view this not as a matter of "getting it to run," but as an architectural challenge of "incorporating into design whether we can optimize based on measurement to keep it lightweight, affordable, and crash-free, and hand it over embedded into operations." Connecting with our previous articles on p99 Latency Improvement Client Services via Adaptive Hedged Requests (GH Media) regarding latency tuning, Cost Optimization Client Services for Continuous Profiling with Pyroscope 2 (GH Media) regarding continuous profiling, and Robust Workflow Backend Services with Postgres / SQLite for Clients (GH Media) regarding backend reliability, this article structures "Backend Performance and Cost Optimization Support" into a packaged client offering.
Why memory reduction "now"?
| Dimension | Lavish operations (traditional) | Measurement-based optimization (2026) |
|---|---|---|
| Instances | Oversized by default | Right-sized |
| Cloud bills | Continuously ballooning | Can be reduced |
| OOM | Occasional crashes | Headroom secured |
| Cause | Guessed by intuition | Identified by profiling |
| Improvement | Ad hoc | Evidence-based reduction |
| Deliverable | Expensive and unstable | Affordable and stable |
In short, "running" and "being lightweight, affordable, and crash-resilient" are completely different things, and in custom development, "trimming memory based on measurement, reducing costs, preventing OOM errors, and handing over systems embedded into operations" has become a prerequisite for quality. This enables us to guarantee a "lightweight, affordable, and crash-resilient backend" as a tangible deliverable.
Three structural changes beneficial to custom development projects
Structure 1: From "oversized by default" to "right-sized"
Excessive instance capacity bleeds costs every month. In custom development, we downsize to the necessary and sufficient configuration through measurement-driven sizing.
Structure 2: From "intuitive guessing" to "profiling"
Tuning based on gut feeling misses the mark. In custom development, we pinpoint root causes through memory profiling to deliver evidence-based reductions.
Structure 3: From "occasional OOM crashes" to "securing headroom"
Memory starvation triggers outages. In custom development, we deliver a crash-resilient backend through leak remedies and upper-bound design.
The 5 phases of "Backend Performance and Cost Optimization Support" provided for clients
Phase 1: Current state audit (1 week)
- Measuring memory / CPU usage
- Analyzing cloud billing breakdowns
- Checking occurrences of OOM and excessive GC
- Preparing profiling environments
Phase 2: Optimization strategy design (1 week)
- Identifying hotspots through profiling
- Surfacing memory leaks and excessive allocations
- Policies for sizing and autoscaling
- Setting target costs and stability metrics
Phase 3: Implementation (2–3 weeks)
- Optimizing memory usage points (fixing leaks / improving data structures)
- Right-sizing instance capacities
- Reviewing cache and buffer configurations
- Verifying under load testing
Phase 4: Verification and adoption (1 week)
- Remeasuring memory and costs
- Verifying OOM headroom
- Establishing monitoring and alerting
Phase 5: Ongoing operations (continuous)
- Periodic monitoring of memory and costs
- Continuous profiling data collection
- Assessing impacts when introducing new features
Standard technology stack set for custom development
| Layer | Recommended technology | Alternative |
|---|---|---|
| Profiling | Pyroscope / language-specific profilers | Heap dump analysis |
| Load testing | k6 | Gatling / Locust |
| Monitoring | Memory / GC metrics | Standard cloud monitoring |
| Scaling | Autoscaling / right-sizing | Fixed large instances |
| Cache | Appropriate TTL / size limits | Uncapped caching |
| Visualization | Cost / memory dashboards | Manual invoice reviews |
Which projects need this and which do not
| Projects requiring this | Low-priority projects |
|---|---|
| Cloud expenses are ballooning | Costs are negligible |
| Occasional crashes due to OOM | Consistently stable with ample headroom |
| Running on oversized instances | Already right-sized |
| Mission-critical system operated long-term | Short-lived evaluation environments |
| Traffic is growing | Virtually idle internal tooling |
Six clauses to include in client contracts
| Clause | Details | What the client should verify |
|---|---|---|
| Target scope | Services to optimize | Agreement on priorities |
| Goals | Cost / memory / stability | Target standards |
| Measurement | Profiling / load testing | Verification method |
| Safety | Maintaining performance and stability | Prerequisite of zero degradation |
| Handover | Configuration / runbook | Maintenance framework |
| Ongoing maintenance | Monitoring / improvement | Operating costs |
Client ROI estimate (assuming enterprise systems)
| Item | Existing (lavish) | Memory optimization | Difference |
|---|---|---|---|
| Instances | Oversized with continuous billing | Right-sized | Sustained reduction in cloud costs |
| OOM outages | Occasional crashes | Headroom secured | Reduction in incident response |
| Performance | Unstable from excessive GC | Stable | Improved perceived quality |
| Scaling | Indiscriminate expansion | Efficient scaling up and down | Improved cost predictability |
| Annual benefit | — | — | Ongoing cloud cost reduction + elimination of OOM outages |
Even with a reasonable upfront investment, it is fully justified through continuous monthly cloud savings and avoiding even a single OOM outage.
Five common pitfalls
Pitfall 1: Trimming based on gut feeling without measurement
It damages performance. Identify bottlenecks through profiling.
Pitfall 2: Leaving instances oversized by default
You are billed continuously every month. Right-size your infrastructure.
Pitfall 3: Overlooking memory leaks
Usage gradually creeps up until it crashes. Pinpoint and fix leaks.
Pitfall 4: Leaving caches uncapped
They consume all available memory. Set size limits and TTLs.
Pitfall 5: Stopping after an initial trim
Usage will bloat again. Monitor continuously.
90-day action plan
| Week | Action |
|---|---|
| Week 1 | Measuring memory and costs + analyzing breakdowns |
| Week 2 | Profiling + designing optimization strategies |
| Week 3〜5 | Implementing reductions + sizing + load testing |
| Week 6 | Remeasurement + establishing monitoring |
| Week 7〜13 | Commencing monitoring + continuous optimization operations |
Summary — From "lavish operations" to "handing over lightweight, affordable, and crash-resilient systems"
Reducing backend memory achieves cloud cost savings and stability enhancements simultaneously. From our position supporting systems through custom development, trimming memory based on measurement, cutting costs, preventing OOM errors, and handing over systems embedded into operations through "Backend Performance and Cost Optimization Support" is our new flagship service delivering lightweight, affordable, and crash-resilient backends as tangible deliverables.
If you are facing issues like "cloud costs are ballooning," "systems occasionally crash from OOM," or "we are running on oversized instances," please feel free to reach out via our contact form.
Sources
- Reducing Backend Memory Consumption (gihyo.jp 2026-05-27)
- p99 Latency Improvement Client Services via Adaptive Hedged Requests (GH Media)
- Cost Optimization Client Services for Continuous Profiling with Pyroscope 2 (GH Media)
- Robust Workflow Backend Services with Postgres / SQLite for Clients (GH Media)
- SaaS Business Continuity Design Services for Clients: Lessons from Railway and GCP Outages (GH Media)
