RudderStack, open-source CDI (open-source Segment alternative)

Hacker News

RudderStack (https://github.com/rudderlabs/rudder-server) is an open-source customer data infrastructure (a.k.a open-source alternative to Segment). We firstly want to thank the HN community for showing us love and support in our previous HN post (https://news.ycombinator.com/item?id=21081756). At that point, we had just open-sourced the repo and were not fully prepared for a Show HN. We wanted to share updates since then and also do our official Show HN. Updates since Sept 2019 ================= 1. Changed the name from Rudder to RudderStack :) 2. API compatibility with Segment 3. Open-source control plane so no dependency on the hosted control plane for open-source users. (https://github.com/rudderlabs/rudder-server/blob/config-gen/utils/config-gen/README.md) 4. Multiple hosting options: Docker, Kubernetes, Native, Terraform. 5. 30 integrations (https://rudderstack.com/) including cloud mode and device mode 6. Support all the popular data-warehouses & lakes - RedShift, Snowflake, BigQuery, S3, Google Cloud Storage, Azure Blob Storage (via MINIO) 7. Detailed documentation - https://docs.rudderstack.com/ 8. Multiple production deployments including few really large ones (our largest deployment is sending a peak of ~40K events/sec, ~100M events/day) 9. Switched license from SSPL to AGPLv3 (after long discussions internally as well as on HN) 10. Built some interesting Analytics & ML use cases 11. Launched our “paid plans” (primarily around managed hosting)

Visit product View on Hacker NewsMar 13, 2020

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

95%95% 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: google, user, dock · Missing: mac, agents, macos

89%89% 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, including · Missing: https docs, excited, just released

78%78% 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: google, users · 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: host, users · Missing: plus, platform, intuitive

26%26% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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