Pendable - high performance email sending with Amazon SES

Hacker News

Hi HN, Today we announce Pendable (Pendable.io), which is a production-grade email sending platform using Amazon SES. Pendble does parallelized email sending, dynamic priority queues, scheduled future delivery, email events, active throttling based on bounces and complaints, spam domain filtering, email logs and resend options, all using your existing Amazon SES setup. To get started, use our API instead of SES and start routing your emails through Pendable. In our day job, we develop bespoke custom loyalty solutions for enterprise customers and have been doing that over a decade. We use SES extensively as it is cost-effective and robust but we found a lot of gaps and decided to build the features instead of moving to solutions like SendGrid or MailChimp. With Pendable, you can continue using your SES account to send emails, while Pendable makes it robust and high-performant. Furthermore, we provide an easy to use monitoring dashboard that you can directly provide to your internal stakeholder teams. This is our first attempt at a startup and so we highly value your feedback and suggestions for new features. We are planning to build features like Unsubscribe Preferences and support for SES email templates. Website: https://pendable.io/ Docs: https://pendable.io/docs

Visit product View on Hacker NewsSep 18, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, using · Missing: mac, agents, macos

89%89% 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.

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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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