FINANCE

Monthly Budget Burn-Rate Forecast from BigQuery to Discord

Each week projects month-end cloud spend from current BigQuery burn rate, compares it to the budget.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionPull MTD spend, burn rate, and top servicesGoogle BigQueryBigQuery
  • LogicProject month-end and classify variance band
  • ActionWrite forecast narrativeOpenAI
  • OutputPost forecast to finance DiscordDiscordDiscord

What it does

This workflow answers "are we going to blow the budget this month?" before the invoice arrives. Weekly it sums month-to-date spend from BigQuery, extrapolates to a month-end projection using the current daily burn rate, compares the projection to your set budget, and posts a narrated forecast naming the services driving the trajectory.

When to use it

Use it when you commit to a monthly cloud budget and want early warning rather than an end-of-month surprise. It is ideal for finance partners who report to leadership and need a defensible projection with named cost drivers, not just a raw number.

How it works

  1. 1A weekly schedule triggers the forecast.
  2. 2A BigQuery query returns month-to-date spend, daily burn rate, and the top services by spend.
  3. 3A logic step projects month-end spend and computes the variance against the configured budget.
  4. 4The branch tags the run as on-track, at-risk, or over-budget based on the variance band.
  5. 5OpenAI writes the forecast narrative with the projected total, variance, and main contributors.
  6. 6The forecast posts to the finance Discord channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect DiscordCommunity channels + voice + bots.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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