AI Grants & Credit Search

The core value here is curation and organisation, not technology. HackerNoon.ai has taken a chaotic landscape — every cloud vendor, model lab and GPU provider runs its own startup program on its own page — and put it behind two filters that actually map to how founders think: what kind of thing do I need (Cloud Infrastructure, AI/ML Platforms, API, Dev Tooling, Academic, Government) and how big is it ($0–$1,000 up to $100,000+). That's the whole product, and it's enough. Strengths. The listing set is unusually current for a directory: it includes GPU-native programmes like RunPod (1,000 H100 hours), Lambda Labs Research ($5,000), Modal Labs (up to $50,000), Together AI and HuggingFace ZeroGPU, alongside European clouds like OVHcloud (up to €100,000), Scaleway (up to €36,000) and Gcore (€1,000–€25,000). Individual listing pages are clean — the Vercel Pro Plan page, for example, shows the category (Dev Tooling), the funding amount ($30,000), a one-line summary and a single Apply for Access button. Company Data & Blogs panels on each listing give you context on the provider without leaving the directory. And the Submit Your Program portal means the database can grow from the vendor side rather than relying purely on editorial effort. Weaknesses. Amounts are headline figures, not guarantees — Oracle for Startups advertises "up to $500,000" and Google Cloud "$2,000–$350,000", but the actual award depends on stage, geography and the provider's own review. The directory doesn't screen for eligibility, so a solo developer in a non-supported country may see a programme they can't actually win. Some entries use shorthand that requires a click to decode: Cohere's Startup Program is listed as "25% Discount", OpenAI Startups as "2,500", Scale AI as "1,000 labeling units", and OpenAI GPT-5 Bio Bug Bounty as "$10,000 - $25,000" — these are accurate but terse. Finally, because the amount filter buckets are wide ($10,000–$50,000 spans a lot of very different programmes), high-value programmes are easy to overlook if you only browse the top bucket. Where it fits. Pre-seed and seed AI teams deciding which cloud to build on, indie developers weighing a GPU credits application against paying out of pocket, and academic researchers who qualify for the dedicated research tracks from OpenAI, Lambda and HuggingFace. It's also useful before a fundraise — credits are non-dilutive, and a founder can walk into an investor meeting having already secured compute. Where it doesn't. If you need cash rather than credits, this is the wrong tool entirely — it's a credits and services directory. If you need someone to write the application, coordinate with the vendor, or guarantee an award, there's no service layer here at all. And enterprises negotiating bespoke cloud commitments will find public startup programmes irrelevant.