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APIs and SDKs to help developers build pricing plans faster

Hacker News

APIs and SDKs to help developers build pricing plans faster

Hello HN Community, As an ex-founder of a developers-for-hire agency, I was fortunate to build for many different software organizations around the world, and partner with the most talented product teams. One of the things I learned by coding was how painful it is to build & maintain pricing in SaaS. The pricing structure and logic always end up being coupled with the core application code and the billing platform. The product catalog is typically managed in spreadsheets, by the product & monetization teams, while all the possible package types, including the legacy ones, are typically hardcoded across different git repositories. Together with our founding team, we have decided to devote ourselves to help solve this problem, and make it much easier for developers everywhere to build out pricing. At Stigg, we’re building APIs and SDKs that allow engineering teams to safely launch and roll-out pricing plan changes, faster. At their core, our APIs allow you to model any pricing (Trials, Freemium, Usage-based, Subscriptions, Per seat) and handle the access control and provisioning logic. We’re trying to build our platform in such a way that after the first integration, any future changes could be deployed by anyone - allowing developer teams to free-up their time from this tedious maintenance work. We have planned ahead and have already introduced reach functionally like plan migrations and versioning, multiple environments support, a/b testing, and embeddable components you can use such as paywall widget, or customer portal widget. This way, you can ship pricing plans without having to do a full Phd on pricing & billing ontology. We're just getting started and have a bunch of things on our roadmap. But we’d love to get feedback from the HN community to check that we’re on the right way.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, coding, code · Missing: mac, agents, macos
94%94% 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, including, organizations · Missing: supports, reddit linkedin, podcasting
82%82% 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, including · 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.
TrustMRRLess likely to generate early MRR · Strong signals: 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 · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% 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
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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