AI AGENTS

Combined Cloudflare + Vercel weekly bill reconciler

Each week an agent pulls usage and cost from both Cloudflare and Vercel, attributes the week's overage to the single largest cost driver across both platforms.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly scheduled reconciliation
  • ActionFetch weekly Cloudflare usage and cache ratioCloudflareCloudflare
  • ActionFetch weekly Vercel usageVercelVercel
  • LogicNormalize to cost and compute deltas
  • ActionIdentify top driver and prioritized fixOpenAI
  • OutputWrite reconciled report to NotionNotionNotion

What it does

Reconciles spend across your two edge platforms in one pass. It gathers Cloudflare bandwidth/requests and Vercel compute/invocations for the week, normalizes them into cost contribution, and names the single biggest driver of the week's overage along with the highest-leverage fix.

When to use it

When your infra bill is split between Cloudflare and Vercel and you want one weekly artifact that says where the money actually went and what to fix first, instead of two separate dashboards.

How it works

  1. 1A weekly schedule kicks off the reconciliation.
  2. 2The agent fetches Cloudflare analytics (bandwidth, requests, cache ratio) for the week.
  3. 3In parallel it fetches Vercel usage (invocations, duration, bandwidth) for the same window.
  4. 4A logic step normalizes both into estimated cost and computes week-over-week deltas.
  5. 5An OpenAI step identifies the top cross-platform driver and the single prioritized remediation.
  6. 6The reconciled report is written to a Notion page for the team's weekly review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
  2. 2
    Connect VercelDeploys, runtime logs, analytics.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect NotionPages, databases, comments.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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