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Entitlements, metering, and teams for Stripe powered by Planship

Hacker News

Entitlements, metering, and teams for Stripe powered by Planship

Hi everyone! I’m Trent. My co-founder, Pawel, and I are currently bootstrapping Planship by way of a previous exit (AppThwack -> AWS Device Farm). What’s Planship? Planship [1] provides guardrails and tooling for SaaS plan packaging, dimensions, entitlements, metering, enterprise onboarding/provisioning, upsell logic, experimentation, and other related tasks. Planship allows for the creation of levers (AKA pricing dimensions) and specific lever values (“Entitlements”), which are then grouped together within plans (AKA packages, tiers, Stripe “products”, etc.) that customers and teams subscribe to. Entitlements can be feature-based, usage-based, seat-based, or any combination of models. Basically, we separate pricing logic from product code, making it super easy to provision customers and iterate and optimize pricing and packaging within SaaS apps. We’ve built similar, more limited solutions multiple times in our careers and while necessary, they’ve always been an ongoing distraction. Stripe Integration Planship is payment-service-provider agnostic, but most of the companies we work with use Stripe, either directly or indirectly. With the Planship app for Stripe, it’s now possible to use Planship to manage levers and entitlements and view customer entitlements from within the Stripe dashboard. Also, Planship automatically syncs with your Stripe products, customers, and subscriptions so you can easily consume entitlements within your products. The Planship app for Stripe is available in the Stripe marketplace [2] along with a getting started guide [3]. Product Integration We offer SDKs [4] for languages like Python, JS, and Java and frameworks like React/Next.js and Vue/Nuxt so entitlements can be checked on the frontend for tasks like visually gating features, displaying upsell prompts, and so on while reporting usage, performing entitlement enforcement, and managing customers and subscriptions on the backend. On the frontend, we keep entitlements up to date automatically with WebSockets. Wrapping up Happy to answer any questions about Planship, Stripe integration, pricing optimization, or even the process of publishing an app in the Stripe marketplace. Thanks! Links [1] https://planship.io [2] https://marketplace.stripe.com/apps/planship [3] https://docs.planship.io/howtos/use-planship-with-stripe/ [4] https://docs.planship.io/integration/#languages-and-framewor... [5] https://docs.stripe.com/billing/entitlements

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, stripe · Missing: mac, agents, macos
91%91% 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
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
66%66% 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, way, para · Missing: mobile apps, ios, personal
43%43% 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
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue
16%16% 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.

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