cloudsealed-jit

Detects structural waste in cloud billing exports.

Given a billing export from AWS, GCP or Azure, it models what each day should have cost, reports the days that did not match, and turns the excess into a monthly figure. It is a library, a CLI and an HTTP service.

The problem

Cloud cost anomaly detection is usually done by comparing each day against the period average and flagging anything beyond two or three standard deviations. On billing data that method fails in two specific ways.

Standard deviation is inflated by the very spikes you are looking for. A handful of large anomalies raises σ enough to pull themselves back inside the threshold, and to hide every smaller anomaly with them. This is the masking effect, and it gets worse as the anomalies get bigger.

A flat average ignores the weekly cycle. Most cloud bills have a pronounced weekday/weekend shape. Measured against a flat mean, ordinary Mondays look like overspend and ordinary Sundays look like savings.

The method

Baseline. Expected spend for a day is a level term times a weekday term:

expected[i] = rolling_median(cost, 7)[i] × dow_factor[weekday(i)]

The rolling median follows growth and step changes without being dragged by spikes. The weekday factor is the median ratio of observed spend to the level term for that weekday. It is only estimated with at least two full weeks of data; below that every factor is 1.0.

Scoring. Residuals are scored with a modified z-score built on the median absolute deviation:

z = 0.6745 × (x − baseline) / MAD

The 0.6745 constant makes MAD a consistent estimator of σ for normal data, so the score keeps the familiar "number of deviations" reading while tolerating contamination in roughly half the sample. Days at or above |z| = 3.5 are reported — the threshold recommended by Iglewicz & Hoaglin (1993).

Waste. Only positive excess counts. Waste percentage is the share of total spend sitting above the baseline on anomalous days, which converts directly to currency instead of being a count of unusual days.

Recommendations. Each carries a figure derived from the series itself, normalised to 30 days, and states its assumption in the description. Estimates that depend on facts the analyser cannot observe — whether a workload is production, whether a commitment is acceptable — are labelled conditional rather than presented as findings.

Does it actually work better?

Yes, and it is measured, not asserted. benchmarks/masking_benchmark.py builds synthetic bills whose anomalies are known by construction and scores this method against the textbook mean+standard-deviation approach:

Full derivation and reproduction steps in METHODOLOGY.md; the design of the codebase is in architecture.md. The benchmark runs in CI (--check) and fails the build if the advantage ever regresses.

Install

pip install cloudsealed-jit              # library + CLI
pip install "cloudsealed-jit[jit]"       # + numba-compiled kernels
pip install "cloudsealed-jit[jit,api]"   # + HTTP service

numba is optional. Without it the kernels run on pure NumPy and the results are identical; only large inputs get slower.

Use

CLI

cloudsealed-jit billing-export.csv
cloudsealed-jit billing-export.csv --json > findings.json
cat export.csv | cloudsealed-jit - --type cost-forecast

Library

from cloudsealed_jit import parse_billing_csv, analyze

series = parse_billing_csv(open("export.csv").read())
result = analyze(series)

print(result.metrics.wastePercentage)
for r in result.recommendations:
    print(r.title, r.potentialSavings)

HTTP service

docker run -p 8091:8091 cloudsealed/jit-optimization-engine

GET  /health
POST /v1/analyze-billing

curl -X POST localhost:8091/v1/analyze-billing \
  -H 'Content-Type: application/json' \
  -d '{"companyName":"Acme","csvContent":"date,cost\n2026-01-01,100\n..."}'

Set JIT_OPTIMIZATION_API_KEY to require an X-Api-Key header. Set JIT_MAX_CSV_BYTES to change the 64 MB upload ceiling.

Response shape:

{
  "anomalies": [
    { "date": "2026-01-31", "expectedCost": 99.0, "actualCost": 500.0,
      "deviation": 405.05, "zScore": 7.82, "severity": "CRITICAL",
      "description": "Spend above the day-of-week baseline by USD 401.00 (405.1%)." }
  ],
  "metrics": {
    "averageDailyCost": 106.32,
    "stdDeviation": 51.69,
    "sharpeRatio": 2.06,       // spend stability: mean / stddev of daily cost
    "wastePercentage": 6.29    // share of total spend above the baseline
  },
  "recommendations": [
    { "title": "...", "description": "...", "potentialSavings": 200.5, "effort": "MEDIUM" }
  ],
  "summary": "..."
}

sharpeRatio is a spend stability ratio — mean daily cost divided by its standard deviation, the reciprocal of the coefficient of variation. Higher means more predictable spend. It is named for the field in the consuming API contract; it is not a risk-adjusted return.

Supported exports

Line items are aggregated to calendar days. Days with no line items are inserted as zero-spend days rather than skipped. Rows that cannot be parsed are counted and reported in the summary rather than dropped silently.

Development

pip install -e ".[jit,api,dev]"
pytest

The test suite builds synthetic exports whose correct answer is known in advance — a known spike at a known date, a known weekend-idle service, a stable series that must produce no findings — so the assertions test behaviour rather than the current output.

License

MIT. See LICENSE.