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DATA OPS

Daily Axiom Ingest Spike Attributor with Cleanup Ticket

Each morning compares yesterday's Axiom ingest volume against a trailing baseline.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule after Axiom day closes
  • ActionQuery Axiom per-service ingest vs 7-day baselineAxiom
  • LogicFlag services over cost/percent threshold
  • ActionQuery Axiom for top log lines driving bytesAxiom
  • ActionOpen GitHub cleanup issue with evidenceGitHubGitHub
  • OutputPost issue link to Slack data channelSlack

What it does

Runs a daily check on Axiom ingest volume, attributes any spike to the specific service and log pattern that caused it, and files a GitHub issue so the bill jump becomes an owned, actionable task instead of a surprise on the invoice.

When to use it

Use it when your Axiom bill is driven by ingest GB and you want an early warning the day after a noisy deploy, debug-logging leak, or runaway retry loop, before a full billing cycle compounds the cost.

How it works

  1. 1A daily schedule fires the run after Axiom's previous day has closed.
  2. 2It queries Axiom for per-service ingest bytes for yesterday and for the trailing 7-day average.
  3. 3A logic step flags any service whose volume exceeds its baseline by the configured percentage and dollar threshold.
  4. 4For each flagged service it runs a second Axiom query to rank the top log lines or fields driving the bytes.
  5. 5It opens a GitHub issue tagged to the owning team with the service, delta GB, estimated cost, and the offending log sample.
  6. 6A Slack message links the issue to the on-call data channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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