<p>Hey Product Hunt 👋</p><p></p><p>I'm Hunter, co-founder of <a href="https://powabase.ai/" target="_blank" rel="nofollow noopener noreferrer"><strong>Powabase</strong></a>. We've been running an AI dev shop since ChatGPT first came out, and after many client projects we noticed the same pattern repeating itself. Nearly every AI-native app ends up needing the same stack: Postgres, a vector store, RAG pipelines, an agent runtime, memory, auth, and file storage. </p><p></p><p>Today you stitch that together from 6–8 tools, write a lot of glue code, and then watch your coding agent burn tokens navigating it. We've built ~100 production AI apps across regulated industries — finance, insurance, education, government — and the infra glue was always the slowest, most expensive part.</p><p></p><p>So we abstracted it into a unified backend. Powabase is the backend we wished we'd had — and now every new AI project we take on ships in a fraction of the time.</p><p></p><p><strong>Powabase is that whole stack as one platform:</strong></p><ul><li><p>Postgres + pgvector + file storage, provisioned per project in one click</p></li><li><p>Standard Supabase features like auth and realtime</p></li><li><p>A context engineering layer with multiple RAG algorithms that hits <strong>98.7% on FinanceBench</strong></p></li><li><p>Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter</p></li><li><p>Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations</p></li><li><p>ReAct multi-agent orchestration with prebuilt tools (web search, database r/w, sandboxed code execution, etc.) and support for custom tool integrations via API and MCP</p></li><li><p>N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language</p></li><li><p>Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors</p></li><li><p>Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design</p></li></ul><p>AI apps deserve their own backend abstraction, not a Frankenstein of generic infra + LLM wrappers. Supabase made Postgres easy to use; we want to do that for the full AI-native stack.</p><p></p><p>It's free to start, and our <a href="https://cookbook.powabase.ai/" target="_blank" rel="nofollow noopener noreferrer">cookbook</a> + <a href="https://github.com/powabase-ai/cookbook" target="_blank" rel="nofollow noopener noreferrer">example apps</a> are open source on GitHub. We plan to open source a self-hosted version after early access period ends, likely around late June / July 2026.</p><p></p><p>I'll be in the comments all day with <a href="https://www.producthunt.com/@tonyzhangcy" target="_blank" rel="nofollow noopener noreferrer">@tonyzhangcy</a> , <a href="https://www.producthunt.com/@xin_chen17" target="_blank" rel="nofollow noopener noreferrer">@xin_chen17</a> , and <a href="https://www.producthunt.com/@michael_t_chang" target="_blank" rel="nofollow noopener noreferrer">@michael_t_chang</a> . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏</p><p></p><p>Early access users get free lifetime benefits — try it at <a href="http://app.powabase.ai" target="_blank" rel="nofollow noopener noreferrer">app.powabase.ai</a> and tell us what you build 🚀</p>
<p>Postgres + RAG + agents is a useful bundle if it reduces the number of glue decisions teams have to make early.</p><p>The question I’d have is where Powabase draws the line between prototype convenience and production control: evals for retrieval quality, prompt/version history, permissions, and audit logs. Those are usually the pieces teams discover they need right after the first demo works.</p>
