DEVOPS

Weekly Replicate Cold-Start Trend Report to BigQuery

Each week it pulls Replicate prediction history, computes cold-start frequency and latency percentiles, writes the rollup to BigQuery.

CategoryDevOps
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionPage through week of Replicate predictionsReplicateReplicate
  • LogicCompute cold-start rate + latency percentiles vs last week
  • ActionAppend weekly rollup to BigQuery tableGoogle BigQueryBigQuery
  • OutputPost trend digest to SlackSlack

What it does

This workflow gives you the data to decide whether a warm pool or dedicated instance is worth it. Weekly, it aggregates Replicate cold-start behavior — how often workers booted cold, p50/p95 cold-start latency, and how that compares to last week — then persists the rollup to BigQuery and shares a human-readable digest.

When to use it

Use it for capacity and cost planning on Replicate endpoints. When you need evidence to justify (or retire) always-on capacity, this builds the longitudinal record warm-pool tuning decisions should rest on.

How it works

A weekly scheduled trigger starts the run. An action pages through the past week of Replicate predictions, collecting boot and predict timings. A logic step computes cold-start counts and latency percentiles and compares them to the prior week's stored values. An action appends the weekly rollup row to a BigQuery table for long-term trend analysis. Finally the flow posts a Slack digest with the headline numbers, the week-over-week delta, and a recommendation flag when cold starts trend worse.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ReplicateImage, video, and model inference.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  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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