Model RAM use for apps, VMs, and containers. Include buffers, page cache, and safety margin. See totals instantly, then download CSV or PDF reports.
Engineering note: treat page cache as reclaimable only if your workload tolerates it.
| Scenario | Total RAM | OS Reserved | Services | Overhead | Peak | Estimated Used |
|---|---|---|---|---|---|---|
| Small API node | 8 GB | 1.5 GB | 2× workers (450 MB), DB (1.8 GB) | 5% | 1.20 | ~5.2 GB |
| Cache-heavy node | 16 GB | 2 GB | Web (2×600 MB), DB (2.5 GB), Cache (512 MB) | 5% | 1.20 | ~8.6 GB |
| Batch spike node | 32 GB | 3 GB | Workers (8×900 MB), Queue (1 GB) | 7% | 1.40 | ~13.8 GB |
RAM sizing is more than summing application footprints. This calculator separates operating system reserve, service memory, and cache or buffers so teams can model repeatable scenarios. In practice, most nodes show a baseline “always-on” load, plus variable load driven by concurrency. Keeping each component visible reduces guesswork when you migrate workloads, change instance counts, or introduce new services.
Each service line uses per‑instance memory multiplied by instances to produce a process total. For example, 6 workers at 450 MB contribute 2,700 MB (≈2.64 GB). This makes horizontal scaling explicit: doubling replicas doubles memory demand unless you also reduce per‑instance settings such as heap limits, thread pools, or cache sizes.
Measured RSS often exceeds configured limits because allocators, metadata, page tables, and fragmentation consume space. A conservative overhead of 3–8% is common on general workloads; memory‑intensive runtimes or frequent allocations may justify 10%+. Applying overhead to the base total reflects how “invisible” costs grow with every additional process and cache.
Workloads rarely remain flat. Batch windows, traffic bursts, and compactions can push memory above steady state. The peak multiplier models this surge; values like 1.15–1.35 are typical for web systems, while bursty pipelines may need 1.40+. The safety margin then adds headroom for growth, version changes, and new features without immediate hardware churn.
Utilization compares estimated used memory to total RAM. At 70% you can monitor trends; at 85% the system is at higher risk of swapping, eviction, or out‑of‑memory events under load. If the calculator reports negative free memory, reduce instance counts, lower per‑process limits, or increase RAM to restore a stable buffer.
Use typical resident memory (RSS) per instance during normal operation. If you only have limits, start with the limit, then validate with monitoring after deployment.
Include it when your workload benefits from warm caches or holds large read sets. If your system can reclaim cache quickly, keep the value small and rely more on the peak multiplier.
Start with 5% for mixed services. Raise it when you see consistent gaps between configured memory and observed RSS, or when fragmentation and metadata are significant.
It estimates short‑term surges above steady state from bursts, compaction, GC pressure, or queue backlogs. Use monitoring to tune it after major traffic or workload changes.
Recommended RAM includes peak usage plus a safety margin, providing headroom for growth and reducing risk during deployments, failovers, and workload shifts.
Yes. Enter each container or VM as a service row with its typical memory usage and instance count. Use a higher safety margin if limits are strict or workloads are bursty.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.