Cost Forecasting: How InfraAudit Models Spend

How InfraAudit ingests daily billing data from AWS, GCP, and Azure to generate 30, 60, and 90-day spend forecasts using provider-native and trend-based models.

InfraAudit ingests billing data from each connected cloud provider and generates forecasts for the next 30, 60, and 90 days. Forecast accuracy improves as billing history accumulates — expect rough estimates for the first two weeks, reasonable estimates after four weeks, and reliable forecasts after eight or more weeks.

Billing data ingest

Each cloud provider exposes billing data through a different API. InfraAudit handles each one:

Provider	Source	Granularity
AWS	Cost Explorer (GetCostAndUsage)	Daily
GCP	BigQuery billing export table	Daily
Azure	Cost Management API	Daily

The cost sync job runs daily at 03:00 UTC by default and pulls the previous day’s finalized billing data.

AWS billing notes

Cost Explorer data has a 24-hour lag. Data for day D is available by approximately 08:00 UTC on day D+1. Applying Cost Allocation Tags in AWS enables resource-level attribution — InfraAudit can break down costs by tag when they’re available.

GCP billing notes

BigQuery billing export must be configured before InfraAudit can ingest GCP cost data. Once configured, data typically appears within 2–4 hours of the billing period. See GCP integration setup for instructions.

Azure billing notes

Azure Cost Management data is generally available within 24 hours. Billing data is synced daily automatically once you connect an Azure subscription.

Forecasting methodology

InfraAudit uses two forecasting approaches:

Provider-native forecasts

For AWS and Azure, InfraAudit sources the forecast from the provider’s own forecasting API:

• AWS — GetCostForecast with a 90-day horizon and daily granularity
• Azure — Cost Management forecast API

These forecasts use proprietary models trained on your account’s full billing history. InfraAudit caches the results and displays them as the primary forecast for those providers.

InfraAudit trend model

For GCP (which doesn’t offer a native forecasting API) and as a secondary validation for AWS and Azure, InfraAudit runs a linear trend model over the last 30 days of billing data:

1. Compute a 7-day rolling average to smooth day-of-week effects.
2. Fit a linear regression to the last 30 days.
3. Project forward using the regression slope.

The confidence interval widens with the forecast horizon — the 90-day forecast has significantly more uncertainty than the 30-day one.

What the forecast shows

In the Cost section of the dashboard, you’ll see:

• This month to date — actual spend through yesterday
• Remaining days — forecast spend for the rest of the current month
• End-of-month estimate — actual spend plus forecasted remainder
• 30/60/90-day outlook — three forecast horizons with confidence bands

Improving forecast accuracy

• More billing history means better forecasts. Accuracy improves noticeably after 8+ weeks of connected data.
• For AWS, enabling Cost Allocation Tags and tagging your resources improves resource-level attribution and forecast breakdown by service or team.
• For GCP, configuring BigQuery billing export as early as possible maximizes the historical data available to the trend model.

Anomaly detection

InfraAudit also checks each new daily data point against the forecast baseline to detect unexpected cost spikes. See Anomaly detection for how that works.