cost-optimization

name	cost-optimization
description	Optimize Cloudflare Developer Platform costs for Workers CPU vs wall time, D1 operations, KV reads/writes, R2 storage/egress, Durable Object hotspots, Queues, Workflows, Workers AI tokens, and architecture-level trade-offs. Use during design and production reviews.
compatibility	Cloudflare Workers TypeScript projects using Wrangler; verify current Cloudflare APIs, limits, and pricing before production use.
metadata	{"source":"Architecting on Cloudflare plus official Cloudflare Developer Platform docs","generated":"2026-04-28"}

Cost Optimization

Use this skill during architecture review, code review, and production hardening.

Cost model stance

• Optimize architecture first, micro-operations second.
• Workers are usually best when code waits on I/O rather than burning CPU.
• AI inference can dominate costs; manage tokens and model choice early.
• R2 is attractive for blob-heavy and egress-sensitive workloads.
• KV write-heavy designs can become expensive or consistency-fragile.
• Durable Object hot keys can become performance and cost bottlenecks.

Code-level cost rules

• Run independent I/O concurrently with Promise.all.
• Cache public/deterministic reads where stale data is acceptable.
• Avoid excessive KV writes; batch or write through the source of truth.
• Avoid unbounded D1 queries; select only needed columns and add indexes.
• Stream R2 objects instead of buffering.
• Use Queues/Workflows to defer work and avoid user-visible long requests.
• Set AI max_tokens; retrieve fewer, better chunks for RAG.

PR review checklist

Add a cost note for changes that introduce:

• New AI model calls.
• New write-heavy KV/D1/Queue paths.
• New Durable Object classes or object-key strategy.
• Large R2 upload/download flows.
• A new Workflow with many steps or retries.
• A new external API call from high-traffic routes.

Cost note template

## Cloudflare cost impact
- Traffic path:
- Expected request volume:
- CPU-heavy work:
- I/O/binding calls per request:
- Storage reads/writes:
- AI model and token cap:
- Cache/TTL strategy:
- Hot-key risk:
- Backpressure/rate limit:

AI cost controls

• Use cheap classifiers/routers before expensive generation.
• Truncate, summarize, or retrieve context rather than passing entire documents.
• Cache safe repeated prompts.
• Use RAG abstention instead of retrying generation blindly.
• Record model, tokens, latency, and tenant/request ID.

Storage cost controls

• D1: index important queries; avoid returning unused columns/rows.
• R2: store blobs once; store metadata in D1; stream responses.
• KV: use for high-read/low-write cache, not write-heavy event streams.
• Queues: batch where safe and make messages small.
• Durable Objects: partition by entity to avoid hot object bottlenecks.

Anti-patterns

• CPU-heavy transforms done synchronously on every request.
• AI prompt includes huge unfiltered context.
• Public static assets served through custom dynamic logic for no reason.
• KV cache invalidated on every request.
• One Durable Object coordinates all tenant traffic.