Launching today
Powabase
Build AI apps with Postgres, RAG, and agents
307 followers
Build AI apps with Postgres, RAG, and agents
307 followers
Powabase is a backend-as-a-service for AI-native applications, combining Postgres, RAG, agents, memory, workflows, and automation primitives in one platform. It helps agencies and in-house IT teams build new AI apps or add AI automation to existing products without stitching together fragmented infrastructure. Designed to work seamlessly with modern coding agents, Powabase helps teams ship faster while building more robust, token-efficient systems.
Powabase
Hey Product Hunt 👋
I'm Hunter, co-founder of Powabase. 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.
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.
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.
Powabase is that whole stack as one platform:
Postgres + pgvector + file storage, provisioned per project in one click
Standard Supabase features like auth and realtime
A context engineering layer with multiple RAG algorithms that hits 98.7% on FinanceBench
Supports OpenAI, Anthropic, Google, or open-source LLMs via OpenRouter
Multimodal embeddings, rerankers, OCR, web search, web scraping all included without separate third party API keys or integrations
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
N8n-like visual agent workflow builder for deterministic logic; built-in copilot can help you craft workflows using natural language
Full observability in agent reasoning, token usage, RAG context, tool calls, workflow executions, and system errors
Optimized for coding agents like Claude Code — clean primitives, predictable APIs, token-efficient by design
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.
It's free to start, and our cookbook + example apps 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.
I'll be in the comments all day with @tonyzhangcy , @xin_chen17 , and @michael_t_chang . Tear it apart — what's missing, what's confusing, what would make you actually try it. 🙏
Early access users get free lifetime benefits — try it at app.powabase.ai and tell us what you build 🚀
Postgres + RAG + agents is a useful bundle if it reduces the number of glue decisions teams have to make early.
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.
Powabase
@studentzuo Great observation! Powabase has a full suite of Observability tools built in. It gives you a variety of information ranging from token usage breakdown by model to failed RAG indexings and LLM queries. Since it is a BaaS, every configuration and action is tracked in a Postgres DB table for your records. You can also build custom dashboards on top of auto-collected data to analyze it as you see fit.
Like Supabase, our Postgres db has row level permissions. For certain out-of-the-box agent tools (e.g., database_write), the user must grant explicit permission to the agent on a per-table basis before it can manipulate it. If you want human-in-the-loop workflows like Claude code, you can set up hooks in your agent orchestration so it waits for user's confirmation before continuing.
Would love to hear more about what projects you have in mind!
the 6-8 tools stitched together with glue code is painfully accurate. every AI project starts clean and ends up as a frankenstein of integrations within a month. unified backend makes sense if it actually reduces that sprawl
Powabase
@tina_chhabra Thanks for confirming our experience! A lot of the custom gluing depends on the actual project specifications. If the business logic is complex and many third party integrations are needed, you might still need a good amount of glue code regardless of what backend framework you use.
Powabase's ReAct multi-agent orchestrations allow you to connect agents with custom tools and MCP servers though, so if you're building an AI app, hooking your agents up with tools is a lot easier than building everything from first principles.
Happy to hear more about what you might be working on!
Very cool @hunter_powabase ! Upvoted :)
So the only thing I need to bring is API key for LLMs?
Powabase
@hunter_powabase @aiswarya_s Thanks for the upvote! Bring your API key is an option that many of our users prefer. We are also giving our users the option to use powabase's key out of box so they don't have to get their own - should be out in the next product update.
Powabase
@hunter_powabase @aiswarya_s Yes, for now — bring an LLM API key (OpenAI, Anthropic, Google, or any model via OpenRouter) and you're set. Embeddings, rerankers, OCR, web search, and scraping are all wired in with no extra accounts. We're actually rolling out an even simpler onboarding soon where new users can start with Powabase's LLM access out of the box (bring-your-own-key stays supported and gets you the best margin). What are you planning to build?