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LangSpend – Track LLM costs by feature and customer (OpenAI/Anthropic)

Hacker News

LangSpend – Track LLM costs by feature and customer (OpenAI/Anthropic)

We're two developers who got hit twice by LLM cost problems and built LangSpend to fix it. First: We couldn't figure out which features in our SaaS were expensive to run or which customers were costing us the most. Made it impossible to price properly or spot runaway costs. Second: We burned 80% of our $1,000 AWS credits on Claude 4 (AWS Bedrock) in just 2 months while building prototypes of our idea but we had zero visibility into which experiments were eating the budget. So we built LangSpend — a simple SDK that wraps your LLM calls and tracks costs per customer and per feature. How it works: - Wrap your LLM calls and tag them with customer/feature metadata. - Dashboard shows you who's costing what in real-time - Currently supports Node.js and Python SDKs Still early days but solving our problem. Try it out and let me know if it helps you too. - https://langspend.com - Docs: https://langspend.com/docs - Discord: https://discord.gg/Kh9RJ5td

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, openai, open · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000 · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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