SUMMARIZATION
Axiom-to-BigQuery Cost Ledger with Notion Summary Page
Weekly, exports per-service Axiom log-cost metrics into a BigQuery ledger table, then summarizes the running trend and publishes a refreshed Notion page for finance…
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionQuery Axiom: weekly ingest + events by serviceAxiom
- ActionAppend cost rows to BigQuery ledgerBigQuery
- ActionRead full multi-week ledger for trendBigQuery
- ActionSummarize trajectory and budget varianceOpenAI
- OutputRefresh canonical Notion cost pageNotion
What it does
Turns ephemeral Axiom log data into a durable cost ledger. Each week it computes per-service ingest cost, appends those rows to a BigQuery table so history accumulates, then queries the full ledger to produce a trend summary and updates a single Notion page that finance and eng leads can bookmark.
When to use it
Use it when you need an auditable, long-lived record of observability spend by service rather than a transient alert. Good for monthly budget reviews and chargeback conversations where you must show the trend, not just this week.
How it works
- 1A weekly schedule trigger starts the run.
- 2Axiom is queried for the trailing week's ingest bytes and event counts per service.
- 3A BigQuery step appends the computed cost rows, tagged with the week ending date.
- 4A BigQuery read pulls the multi-week ledger for trend context.
- 5An OpenAI step summarizes trajectory: rising services, newly appearing services, and budget-vs-actual.
- 6A Notion step overwrites the canonical cost page with the table and summary.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect NotionPages, databases, comments.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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