Firebolt Core – #1 in ClickBench – Free Scale-Out Analytical SQL Engine

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Hi HN, there’s a distinct lack of modern self-hosted scale-out query engines. A lot of the innovation in the last 10 years has been in SaaS-only systems. That’s also been true for Firebolt until now. We’re now taking the radical step of offering our query engine as a Docker image that’s free for commercial use without any real restrictions on what you can use it for (basically everything except competing with our SaaS offering). There are helm charts and docker compose files in the repo as well to help you get started. The focus of Firebolt is on low-latency, high-concurrency analytics like you might have them in user-facing workloads (dashboards, data-heavy apps, ...). To show just how fast it is, we submitted Core to ClickBench and instantly took the top spot: https://benchmark.clickhouse.com/ . But Firebolt isn’t just a query accelerator, it can also handle scale-out ETL/ELT and all the other things you might want to use a data warehouse for. I’ve been working on Core for the last few months, and moshap (our CTO), wagjamin (VP Eng) and I are here for your questions. Mosha and Benjamin have written about our motivations to offer Core on the company blog: https://www.firebolt.io/blog/introducing-firebolt-core

Visit product View on Hacker NewsJun 25, 2025

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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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.

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