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.
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 🚀
This is very interesting. If this integrates deeply with Claude Code / Cursor workflows I can see dev agencies adopting it really fast. Congrats on shipping.
We tried building something similar internally and realized maintaining embeddings, ingestion and orchestration pipelines becomes a full-time job. Good stuff.
This launch caught my attention on the leaderboard for its positioning. May I ask how this compares architecturally to LangGraph Platform or Mastra?
Finally someone saying AI apps need their own backend abstraction instead of another wrapper around OpenAI APIs.
This is the first AI backend product that actually feels opinionated in a good way. Everyone keeps rebuilding the same RAG + auth + vector DB stack from scratch lol.
The "token-efficient" claim is the one I'd want unpacked, since it's doing a lot of work in the pitch. What's actually saving the tokens — pruning RAG context before it hits the model, caching tool schemas across calls, or something at the memory layer? It's the kind of thing that's easy to assert and hard to measure.
Impressive work, Hunter 👏 abstracting the AI-native stack into one backend feels like the missing piece for a lot of teams. Powabase seems to cut through the glue-code pain that slows projects down. Curious, how do you see developers balancing flexibility with this kind of unified stack, does it risk locking them in, or does it actually open up more room to innovate?
The bundle is useful, but the make-or-break detail is how inspectable the agent layer is. For AI apps, Postgres + RAG gets you started; prompt/version history, retrieval traces, tool-call logs, and permission boundaries are what make it survivable in production.
Do you expose those traces as first-class objects, or mostly as dashboard logs?
Have mixed feelings with these PostgreSQL wrappers. I loved PostgreSQL before it was fashionable and would much prefer to bring my own PostgreSQL, and have clear RAG and other enforcement in N8N, Flowise, or equivalent, and leverage existing agents which can be spread through different infrastructures while maintaining their own memory, skills, knowledge base, etc., in some nice centralized place.
Everything bundled, like Supabase's Edge functions feels like lock in to me. I know it can be run independently, but would prefer these workflows to flow independently of the data container.
Though, there are clear benefits to current agentic limitations having it all be bundled together as it allows better tool, skill, and MCP usage without the agent getting too confused jumping between skills and workspaces.
Looking forward to seeing how it all plays out. Good luck!!
the 'glue code between 6-8 tools' problem is real. spent way too many hours on that exact pattern before. curious how you handle the agent runtime side specifically — is there built-in support for tool calling and memory across conversation turns, or is that something you still wire up yourself?
Congrats on the launch, team!
Powabase looks like a massive time-saver for anyone building multi-agent systems without fighting glue code.
As a backend dev currently building infrastructure around the KYA (Know Your Agent) framework, I have a question regarding security boundaries. Since you unify Postgres and Agent Orchestration in one place, how do you manage the identity and dynamic access rights of autonomous agents? If an agent starts chaining tools or spawning sub-agents, how do you prevent context/prompt hijacking from executing malicious DB queries?
We are designing KYA to serve as a verifiable 'passport & guardrail' layer for AI entities. Are you planning to enforce standardized AI identities like KYA inside Powabase, or do you rely strictly on traditional Postgres Row-Level Security (RLS)?
I'm excited for this. I picked up your GPT Trainer back when you launched it. I preferred it over other agents I tried as your interface was intuitive, and the agent stuck to its designated materials and didn't have any "bleed" from general LLM knowledge impacting answers.
You also kept users abreast of changes and updates, so I'm all in on what you've cooked up here because this (in theory) will be a better for for my anticipated workflow over Supabase.
I love working with @Powabase and @hunter_powabase for Ryden Solutions, the first & leading life science continuous quality and compliance assessment platform simulating FDA inspectors at all times while also becoming more efficient. Hunter's platform has simplified development and set us up for long term success as we scale.
Congrats team, loved this tool.
Are you also planning to add some usecase over the website?
About Powabase on Product Hunt
“Build AI apps with Postgres, RAG, and agents”
Powabase launched on Product Hunt on May 27th, 2026 and earned 439 upvotes and 77 comments, earning #1 Product of the Day. 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 was featured in Developer Tools (513.3k followers), Artificial Intelligence (469.8k followers) and Database (2.1k followers) on Product Hunt. Together, these topics include over 168.3k products, making this a competitive space to launch in.
Who hunted Powabase?
Powabase was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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fmerian
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 🚀