惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

F
Fortinet All Blogs
爱范儿
爱范儿
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog
WordPress大学
WordPress大学
Jina AI
Jina AI
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
N
Netflix TechBlog - Medium
腾讯CDC
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
阮一峰的网络日志
阮一峰的网络日志
The GitHub Blog
The GitHub Blog
V
Visual Studio Blog
Google DeepMind News
Google DeepMind News
月光博客
月光博客
博客园 - Franky
Y
Y Combinator Blog
MyScale Blog
MyScale Blog
大猫的无限游戏
大猫的无限游戏
Martin Fowler
Martin Fowler
雷峰网
雷峰网
小众软件
小众软件
H
Hackread – Cybersecurity News, Data Breaches, AI and More

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
60% of My $312 Anthropic Bill Came From One Missing Patte...
강해수 · 2026-06-24 · via DEV Community

강해수

Last month's Anthropic invoice was $312. After one architectural change, May came in at $156 — exactly half. The culprit wasn't prompt bloat or model choice. It was the absence of compensating actions in my multi-step agent workflow.

The pattern is embarrassingly common: a 5-step pipeline fails at Step 4, so you restart from the top. Every restart re-runs every LLM call before the failure point. My ad analytics SaaS runs Claude Sonnet to summarize raw data in Step 2. That step averages ~8K input tokens per advertiser. At $3/M tokens (Sonnet 3.7), one restart costs $0.024 — trivial alone, but I have 200+ advertisers and this pipeline was failing repeatedly. Step 2 alone burned $40–50 in duplicated calls over April.

The deeper problem: I had no rollback mechanism at all. When Step 5 (a Slack webhook to an advertiser portal) failed with a 503 on a cold-start Worker, R2 already had the file, D1 already had the log row. Restarting the pipeline created duplicate files, duplicate database rows, and one advertiser asking why they got the same report twice. I'd assumed "restart = safe." That assumption was wrong.

The fix has two parts. First, I write a pipeline_runs row at the start of every run, updating it with a step_completed checkpoint and a step_output_ref (the actual R2 key or D1 row ID) after each step succeeds. Second, on failure, a rollbackPipelineRun() function reads those refs and deletes whatever was written — R2 file gone, D1 row gone, status flipped to rolled_back. On retry, the agent checks for an existing in-progress run and skips already-completed steps entirely:

if (existingRun && existingRun.step_completed >= 2) {
  summary = existingRun.cached_summary; // no Claude call
} else {
  summary = await callClaude(data);
}

One thing idempotency keys don't solve here: they prevent duplicate side effects, but they don't prevent re-spending tokens on an identical LLM call. You need both — idempotency on the storage writes and checkpointed caching on the inference steps.

There are still rough edges: a race condition when two runs start simultaneously for the same advertiser (D1 doesn't fully guarantee serializable isolation between a SELECT and INSERT), and no clean answer for truly irreversible actions like sent emails or processed payments.

I wrote up the full breakdown — including the race condition I haven't fixed yet and why Durable Objects might be the answer — over on riversealab.com.

Full post →