Calculator Inputs
Enter sizes in megabytes. The form keeps a single column page layout, while the input grid adapts to large, small, and mobile screens.
Example Data Table
This example shows a typical service image with build dependencies, compiled artifacts, and optimization settings.
| Input or Output | Example Value | Unit |
|---|---|---|
| Base Image Size | 72 | MB |
| OS Packages | 38 | MB |
| Runtime Dependencies | 95 | MB |
| App Source Size | 24 | MB |
| Build Dependencies | 140 | MB |
| Compiled Artifacts | 60 | MB |
| Static Assets | 18 | MB |
| Temporary Files | 22 | MB |
| Layers Count | 14 | Layers |
| Metadata Per Layer | 220 | KB |
| Cleanup Efficiency | 70 | % |
| Multi-Stage Reduction | 85 | % |
| Compression Ratio | 28 | % |
| Estimated Final Size | 215.12 | MB |
| Estimated Storage for 12 Tags | 2.52 | GB |
Formula Used
1. Payload Total (MB)
Base Image + OS Packages + Runtime Dependencies + App Source + Build Dependencies + Compiled Artifacts + Static Assets + Temporary Files
2. Layer Metadata (MB)
Layers Count × Metadata Per Layer (KB) ÷ 1024
3. Gross Image Size (MB)
Payload Total + Layer Metadata
4. Cleanup Savings (MB)
(Temporary Files × Cleanup Efficiency) + (Compiled Artifacts × 0.40 × Cleanup Efficiency)
5. Multi-Stage Savings (MB)
(Build Dependencies + Remaining Artifact Portion × 0.60) × Multi-Stage Reduction
6. Final Compressed Size (MB)
max(Gross Image Size − Cleanup Savings − Multi-Stage Savings, 0) × (1 − Compression Ratio)
7. Registry Storage (MB)
Final Compressed Size × Retained Tags
8. Pull Time (seconds)
Final Compressed Size × 8 ÷ Network Speed (Mbps)
This model estimates release-ready size. Real builds can differ when registries reuse layers or when compression behaves differently across file types.
How to Use This Calculator
- Enter the base image size and the package sizes your image includes.
- Add runtime dependencies, source files, artifacts, and temporary content in megabytes.
- Set the number of layers and the estimated metadata overhead per layer.
- Adjust cleanup efficiency to reflect how well your build removes temporary files.
- Set multi-stage reduction to estimate how much build-only content stays out of the final image.
- Choose a compression ratio that reflects your registry or distribution workflow.
- Enter retained tags to estimate long-term registry storage usage.
- Submit the form to view results, chart trends, and export CSV or PDF summaries.
Frequently Asked Questions
1. What does this calculator estimate?
It estimates a container image’s likely final compressed size by combining major content groups, layer overhead, cleanup behavior, multi-stage savings, and registry compression. It helps with planning, comparisons, and release reviews.
2. Why does the calculator include layer metadata?
Each layer carries metadata and filesystem overhead. While it is often small compared with package content, many tiny layers can still increase total size and management complexity, so it is useful to model.
3. What does cleanup efficiency mean?
Cleanup efficiency represents how much temporary data and leftover build residue you remove during the image build. Higher values mean your Dockerfile removes more waste before the final image is published.
4. Why is multi-stage reduction separate from cleanup?
Cleanup removes files inside a stage. Multi-stage design prevents build-only tools and outputs from reaching the final runtime stage. They solve different problems, so modeling them separately makes estimates more useful.
5. Is final compressed size the same as local disk usage?
No. Local disk usage can differ because your machine may store uncompressed layers, cached layers, and reused content from other images. The calculator focuses on an estimated release or transfer footprint.
6. Can I use this for registries and CI storage planning?
Yes. The retained tags field is especially useful for estimating ongoing registry storage needs in CI pipelines, release channels, and rollback strategies where multiple tagged images remain available.
7. Why does pull time depend on network speed?
Transfer time is tied to how many megabits must move across the network. A smaller image or faster link reduces pull time, which can improve deployment speed and scaling responsiveness.
8. How can I reduce image size in practice?
Use smaller base images, separate build and runtime stages, remove package caches, exclude unused files, compress assets, reduce layer count where helpful, and avoid shipping build tools in production images.