LoadGQL – a CLI for load-testing GraphQL endpoints

Hacker News

Hi HN I’ve been working with GraphQL for a while and always felt the tooling around load testing was lacking. Most tools either don’t support GraphQL natively, or they require heavy setup/config. So I built *LoadGQL* — a single-binary CLI (written in Go) that lets you quickly stress-test a GraphQL endpoint. *What it does today (v1.0.0):* - Run queries against any GraphQL endpoint (no schema parsing required) - Reports median & p95 latency, throughput (RPS), and error rate - Supports concurrency, duration, and custom headers - Minimal and terminal-first by design *Roadmap:* p50/p99 latency, output formats (JSON/CSV), multiple query files. Landing page: [ https://apps.devanswers.org ]( https://apps.devanswers.org ) I’d love feedback from the HN community: - What metrics matter most to you for GraphQL performance? - Any sharp edges you’d expect in a GraphQL load tester? Thanks for checking it out!

Visit product View on Hacker NewsAug 24, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, single · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% 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: apps, answers, way · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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