Bandwidth Usage Calculator

Who is eating the bandwidth? Bandwidth Usage Calculator parses access-log lines, pulls the bytes-served field that sits right after the HTTP status code, and totals it — then breaks that total down by client IP, request path, and status code so you can see exactly where the volume is going instead of just how much there was.

This matters because average requests-per-second dashboards hide the real cost driver: a handful of oversized responses or one aggressive client can dwarf everything else combined. A single scraper re-downloading a 50 MB export a thousand times a day, or one endpoint that accidentally serves an uncompressed asset to every visitor, is invisible in a hit-count graph but obvious the moment bytes are the metric.

Everything runs against pasted text in your browser — no log shipping, no upload, no waiting on a billing dashboard that only updates once a day.

• Total volume — exact byte count plus a humanized KiB/MiB/GiB figure for the headline total.
• By IP — every client ranked by bytes transferred, top consumer first.
• By path — the heaviest endpoints, useful for spotting an unoptimized asset or a runaway export route.
• By status — bytes grouped by response code, so you can see how much volume 200s vs. 404s vs. 5xx error pages are actually costing.
• Average size — bytes per request, a quick sanity check for payload bloat across the whole log.
• Unique IP count — how many distinct clients contributed to the total, at a glance in the stat row.

1. Paste access-log lines that include a method/path and end in "status bytes" — mix and match sources, one request per line.
2. Calculate. Totals, the average per request, and all three breakdown tables render immediately.
3. Read the per-IP table first to catch outlier clients, then per-path to catch outlier endpoints.
4. Copy the full Markdown-formatted report for a ticket, incident writeup, or capacity-planning note.

Example 1 — Cost attribution. The per-IP table shows one scraping client transferring 60% of the month’s bytes from a handful of IPs — that is your rate-limit or block-list candidate, not the thousand ordinary visitors sharing the rest.

Example 2 — Payload bloat. /report.json shows up at the top of the by-path table because it serves an uncompressed 4 MB response to every client; the fix (gzip, pagination, caching) is obvious once the table names the culprit.

Example 3 — CDN and egress sizing. Multiply the projected daily volume from the totals by your provider’s per-GB egress rate to size a CDN budget or justify moving a heavy asset to a CDN edge instead of your origin.

Example 4 — Error-page cost. The by-status breakdown can reveal that a custom 404 page bundled with a large hero image is quietly costing real bandwidth on a high-traffic broken-link route.

• Hog detection — top IPs and paths surface without manual spreadsheet work.
• Cost insight — volume broken down per status code, per path, and per client.
• Quick math — average request size answers "did that deploy make responses bigger?" in seconds.
• Copy-ready reporting — one click produces a Markdown summary for tickets or postmortems.
• Private — all parsing happens in-browser; logs are never uploaded anywhere.