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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.

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Billing data ingest

Each cloud provider exposes billing data through a different API. InfraAudit handles each one:
ProviderSourceGranularity
AWSCost Explorer (GetCostAndUsage)Daily
GCPBigQuery billing export tableDaily
AzureCost Management APIDaily
The cost sync job runs daily at 03:00 UTC by default and pulls the previous day’s finalized billing data.
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.
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 Cost Management data is generally available within 24 hours. Billing data is synced daily automatically once you connect an Azure subscription.

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Forecasting methodology

InfraAudit uses two forecasting approaches:

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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.

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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.

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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

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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.

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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.