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Open-source load testing on AWS Lambda. With built-in cost reporting

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Open-source load testing on AWS Lambda. With built-in cost reporting

Hiya HN! tldr: Artillery is an open-source load testing toolkit. It's cloud-native, i.e. you can scale out your test with AWS Lambda (other runtimes coming soon). There's no infra to set up or manage - you give Artillery an AWS account, and it takes care of the rest. We just released an update which includes cost reporting. Every time you run a load test, Artillery will show how much that cost in AWS Lambda fees. Blog post with a demo here: https://www.artillery.io/blog/this-load-test-cost-us-how-much The demo shows a distributed load test running in us-east-1 at 100k+ RPS for about 10 minutes. Total cost is $1.23. We constantly hear from our users how cost is one of the biggest barriers to load testing more frequently. We're making cost much less of a factor with Artillery, and hoping that will unlock load testing at scale to more teams. Would love to hear what the biggest barriers are for you with load testing! Hassy & team Artillery

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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