Launching today
Powabase
Build AI apps with Postgres, RAG, and agents
684 followers
Build AI apps with Postgres, RAG, and agents
684 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 🚀
@tonyzhangcy @xin_chen17 @michael_t_chang @hunter_powabase The "infra glue was the slowest, most expensive part" framing is the honest reason this category exists, nobody's AI app died from a bad model, they died stitching six tools together. Since token-efficiency is the thread you keep pulling on, that's where I'd push: FinanceBench is clean and bounded, but a client's real knowledge base is messy, duplicated, and growing weekly. The hard part isn't retrieval quality on a benchmark, it's what the cost curve does at 10x the documents. Naive RAG either bloats context and quietly kills the token-efficiency claim, or over-prunes and misses the right chunk. So: as a project's corpus scales, does retrieval cost stay flat, and does precision hold on dirty data? That curve is the difference between a demo scoring 98% and a backend teams trust in production, and it's the one number that'd make "token-efficient by design" provable rather than aspirational.
Powabase
@tonyzhangcy @xin_chen17 @hunter_powabase @arturbrugeman You're naming the real tension — a benchmark isn't proof of production behavior. A few things we lean on: on retrieval cost, pgvector keeps vector search at ~O(log N) with HNSW, and rerankers cut through noise on the candidate set. On dirty data, the harder problem is upstream — one option is using custom metadata enrichment as a preprocessing step to extract structured signal before chunks ever hit the LLM. Precision still degrades with corpus noise, the question is how gracefully.
Production signal we have outside FinanceBench: client app corpora in the 10K–700K-doc range, growing weekly. Powabase is designed to be extensible, and a lot of what gets built per-client ends up as a reusable primitive — that's been our pattern for handling the messier-than-benchmark cases.
If you're working on something specific, would love to hear about it!
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!
Powabase
@studentzuo Thanks for the support!
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!
Powabase
@tina_chhabra thanks for the support! We've already been dogfooding Powabase for other internal projects and it has definitely sped up development
The Postgres-native approach is interesting most RAG tooling treats the database as an afterthought rather than the foundation. Curious how you're handling vector indexing at scale: are you using pgvector under the hood, or did you build something custom? Would also love to know if the agent layer supports tool-calling across multiple data sources or just within a single Powabase instance.
Powabase
@fberrez1 Yep, we are using pgvector under the hood. For multimodal content, we first convert the data into a text-based description using a VLM like Mistral OCR, then index the text. At retrieval time, the database searches the indexed text but pulls the original multimodal content (e.g., base 64 image) and feeds it as context to the LLM.
Agent layer does support tool-calling across multiple data sources. Out-of-the-box tools interface mostly with built-in capabilities of the BaaS itself (e.g., sandbox code execution, permissioned db read/write, web search via Exa, web crawl via Firecrawl, etc.). You can connect custom tools or MCP servers to any agent or orchestrations to supplement its set of tools.
Bababot
The biggest value honestly might not be speed, it’s reducing architectural chaos. AI stacks become fragmented unbeliveably fast 😅
Powabase
@aarav_pittman Indeed. A significant time sink during Powabase's design process was figuring out which available tools are best to include. There are so many out there and some optimize for performance while disregarding cost, some try to strike a good balance and aim at best value, and others make the pure open source free-to-use play.
Example: VLM-based text extraction include LightOnOCR-2, Mistral, or PaddleOCR. This is the default approach for our RAG ETL pipeline. But if you know the source is all native text, you can opt into faster and cheaper non-VLM parsers like OpenDataLoader, PyMuPDF (fitz), or pdfplumber.
We generally try to give developers a choice while setting default options to what we believe is the best value for most common use cases.
Powabase
@aarav_pittman Yes! I've been building apps using Powabase as a backend, and it simplifies the management of all the different tools/integrations needed for an AI app
Serand
Curious how opinionated Powabase is internally. Can teams swap components easily, or is the goal more of an integrated ecosystem experience?
Powabase
@maali_baali Great question! There's a trade off to abstraction level (convenience) vs. control. Powabase definitely tries to strike the best balance based on our own experience building across many use cases during our dev shop days. If your use case involves building AI-native apps with RAG or ReAct agent orchestrations, then you'll find Powabase quite easy to use out of the box.
If your application needs something custom, you don't have to use the complete abstractions offered through Powabase. Instead, you can take relevant "intermediaries" and build a custom layer on top of those to better fit your design specifications. For example, if you need to supplement retrieved RAG context with metadata stored elsewhere, you can invoke the "context_handler" object to get you the content that normally "would have been fed into the agent", then enrich it with custom logic before forwarding it to your agent.
If you share more about what you're trying to build, then we'd be happy to advise you on which features would be most relevant on Powabase!
Powabase
@maali_baali there's a lot of flexibility built into Powabase for processing data sources and indexing them in different ways. In our experience with building client apps, that's where you can get a lot of value from tuning for your use case, e.g. long vs. short documents, scanned vs. digital native PDFs, images vs text-heavy sources.
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?
Powabase
@hunter_powabase @aiswarya_s hey, quick update, we just shipped an update: bringing your own LLM API key is now optional. New sign-ups get $20 in Powabase credits to start with, covering LLM access (Claude, GPT, Gemini, etc.) along with everything else. Bring-your-own-key stays supported and gives you the best margin at scale, but now you can start building with zero outside accounts.