PureRouter – Multi-model AI routing across LLMs (beta and $10 credits)

Hacker News

To earn $10 in free credits, use the code WELCOME10, NO CREDIT CARD REQUIRED. We’ve just launched the open beta for PureRouter, a multi-model AI router that gives you full control over how LLM queries are routed. You can define routing strategies that prioritize speed, cost, or quality, and even fine-tune them with detailed configuration options. PureRouter isn’t just about picking models; it’s about precision orchestration: Route queries across multiple providers (OpenAI, Cohere, Gemini, Groq, DeepSeek, and more). Adjust advanced parameters like context length, batch size, precision, memory usage, and generation controls (temperature, top-p, top-k). Deploy workloads on cloud, edge, or on-prem hardware with flexible scaling (from single GPUs to multi-GPU instances). Manage everything from a clean, intuitive UI designed to make complex orchestration straightforward. Billing is simple and transparent with our credit-based system (no subscriptions, no hidden usage fees). If you’d like to test it, the beta is live now. New users can redeem $10 in credits with WELCOME10 to explore routing strategies, compare models, and deploy real workflows. We’re iterating fast, and feedback from the community is especially valuable at this stage.

Visit product View on Hacker NewsSep 23, 2025

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

93%93% 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: model, user, new · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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