CrashLens

"Our LLM bill jumped 40% overnight. Finance wanted answers by noon." Meet Nisha Verma, FinOps Lead at a Series-B startup. Last month this exact scenario hit her team. Their monthly LLM spend: $18k across OpenAI and vendor APIs. Datadog showed higher token usage. Devs blamed network issues. Product said "just one customer flow." But Nisha needed prompt-level visibility, not account-level guesses. Here's what she discovered: Silent token burn was everywhere: → Retry storms hitting GPT-4 three times per failed request → Fallback chains burning through expensive models → Text classification tasks using $0.03 calls instead of $0.002 calls The problem? Most tools give you dashboards and trends. What Nisha actually needed: One-line Slack alerts with exact ROI. "Add exponential backoff to retry logic → Save $2,100/month" "Route classification tasks to GPT-3.5 → Save $1,800/month" That's actionable. That's what she could take to engineering in 30 minutes. Her team piloted CrashLens last week. CLI tool, runs locally, scans logs for prompt-level waste patterns. Nisha's result: 18% reduction in LLM spend in two weeks. No dashboards. No PII leaving their infrastructure. Just precise alerts that engineers could actually fix. What's your biggest LLM cost blind spot? 👇 #FinOps #LLMOps

Platform teams waste 15-30% of their LLM budget on silent failures. Last week Priya (FinOps) got a brutal Slack alert: 🚨 CrashLens: Retry storm detected → 847 requests failed, retried 3x each → Fallback chain: gpt-4 → claude-3-opus → gpt-4 again → Estimated waste: $3,200/month → Fix: exponential backoff + model routing Rahul (Platform) ran our CLI tool over 48 hours of logs. Results in 2 minutes: - Text classification hitting gpt-4 ($0.03/call) instead of gpt-3.5 ($0.002/call) - 200+ failed requests with no circuit breaker - Bloated system prompts adding 180 unnecessary tokens per call - Three config changes later: $2,800/month saved. No dashboards. No complex setup. Just a CLI that scans your logs locally and tells you exactly what's burning money. We built CrashLens because platform teams need answers, not analytics. Repo: github.com/crashlens/ What's the biggest LLM cost surprise you've discovered in your logs? 👇 #LLMOps

Overkill models: $20 to fix a comma Teams often default to GPT-4 or Claude Opus for every task — even trivial jobs like punctuation fixes or date formatting. That’s like firing up a moving truck to carry a houseplant. Result: tiny wins, massive bills. Fix it with: • Match task to tool: regex or small models for trivial jobs • Add cost-aware checks: block big models on low-complexity requests • Route heavy reasoning to strong models; keep simple tasks cheap CrashLens flags when overkill models creep into your logs — before finance asks why commas cost thousands. Repo: github.com/crashlens/ #LLMOps

Overkill models: $20 to fix a comma Teams often default to GPT-4 or Claude Opus for every task — even trivial jobs like punctuation fixes or date formatting. That’s like firing up a moving truck to carry a houseplant. Result: tiny wins, massive bills. Fix it with: • Match task to tool: regex or small models for trivial jobs • Add cost-aware checks: block big models on low-complexity requests • Route heavy reasoning to strong models; keep simple tasks cheap CrashLens flags when overkill models creep into your logs — before finance asks why commas cost thousands. Repo: github.com/crashlens/ #LLMOps

Retry storms: the invisible bill spike One flaky network request triggered 3 identical model calls. Each cost money. Each logged as a fresh request. Across thousands of users, that’s thousands of wasted gpt-4 calls. Why it happens: timeout → retry loop → silent model happily answering every call. Your bill doubles, triples, without anyone noticing. Fixes: ⏳ Use exponential backoff   📦 Cache first answer for a short window   🔒 Make retries idempotent (1 request = 1 response) We’ve seen retry storms eat 30–40% of spend. CrashLens flags them in minutes. Repo: https://lnkd.in/gJNRcmXr #LLMOps

Fallback storms: when one user question runs 3 models A customer asks: “Where’s my order?” - Primary model → timeout - Fallback A → slow - Fallback B → also runs Result: 3 model runs for a single request. Multiply that by thousands of queries and your costs spike fast. Fix it with: • Cap the chain: set max_fallbacks: 1 • Designate a single reliable backup • Add a circuit breaker to stop escalation when failure rates spike • Log + enforce: group fallbacks under one trace_id so cost multipliers are visible Fallbacks should be a safety net, not a runaway chain. Repo: github.com/crashlens/ #LLMOps

Fallback storms: when one user question runs 3 models A customer asks: “Where’s my order?” - Primary model → timeout - Fallback A → slow - Fallback B → also runs Result: 3 model runs for a single request. Multiply that by thousands of queries and your costs spike fast. Fix it with: • Cap the chain: set max_fallbacks: 1 • Designate a single reliable backup • Add a circuit breaker to stop escalation when failure rates spike • Log + enforce: group fallbacks under one trace_id so cost multipliers are visible Fallbacks should be a safety net, not a runaway chain. Repo: github.com/crashlens/ #LLMOps

Retry storms: the invisible bill spike One flaky network request triggered 3 identical model calls. Each cost money. Each logged as a fresh request. Across thousands of users, that’s thousands of wasted gpt-4 calls. Why it happens: timeout → retry loop → silent model happily answering every call. Your bill doubles, triples, without anyone noticing. Fixes: ⏳ Use exponential backoff   📦 Cache first answer for a short window   🔒 Make retries idempotent (1 request = 1 response) We’ve seen retry storms eat 30–40% of spend. CrashLens flags them in minutes. Repo: https://lnkd.in/gJNRcmXr #LLMOps

We built Crashlens so no engineer ships blind into a $5k surprise bill. Thank you for following along this week as we've explored LLM cost optimization and introduced our tool. Our mission is simple: to give developers powerful, private, and easy-to-use tools to take control of their LLM costs. What you see today is just the beginning. We're already working on what's next, including features like policy enforcement to prevent costly deployments and a live CLI firewall for real-time protection. But the best tools are built by a community. Crashlens is open source (MIT licensed), and we want you to be a part of its future. Here’s how you can get involved: ⭐ Star the repo on GitHub: It’s the easiest way to show support and stay updated. 🤔 Open an issue: Have a great idea for a new feature? Let's discuss it. 🛠 Submit a PR: Clone the repo and help us build the next version. Let's build the future of LLM cost optimization together. The GitHub repository is here: https://lnkd.in/gp5Txnh2 #OpenSourceSoftware #Community #Contribute #Developers #GitHub #FutureOfTech

Datadog shows a bill spike. CrashLens shows the 10k wasted tokens that caused it. Observability platforms like Datadog, Langfuse, and Helicone are great Swiss Army knives for continuous monitoring. CrashLens is a scalpel — a local-first, lightweight diagnostic you run in minutes to find retry storms, silent model escalations, and token bloat. When the bill spikes: run CrashLens locally, get a Markdown report that points to the offending calls, and push a CI rule to stop the next one. It complements full-stack observability, it doesn’t replace it. Try it: github.com/crashlens/ #FinOps